Merge pull request #1610 from MemPalace/release/3.3.6

Release v3.3.6

.claude-plugin/README.md

@@ -23,7 +23,7 @@ claude plugin add /path/to/mempalace
 
 ## Post-Install Setup
 
-After installing the plugin, run the init command to complete setup (pip install, MCP configuration, etc.):
+After installing the plugin, run the init command to complete setup (installs the `mempalace` package via `uv tool` or `pip`, configures MCP, etc.):
 
 ```
 /mempalace:init

.claude-plugin/hooks/hooks.json

@@ -6,7 +6,8 @@
         "hooks": [
           {
             "type": "command",
-            "command": "bash ${CLAUDE_PLUGIN_ROOT}/hooks/mempal-stop-hook.sh"
+            "command": "bash \"${CLAUDE_PLUGIN_ROOT}/hooks/mempal-stop-hook.sh\"",
+            "timeout": 30
           }
         ]
       }
@@ -16,7 +17,8 @@
         "hooks": [
           {
             "type": "command",
-            "command": "bash ${CLAUDE_PLUGIN_ROOT}/hooks/mempal-precompact-hook.sh"
+            "command": "bash \"${CLAUDE_PLUGIN_ROOT}/hooks/mempal-precompact-hook.sh\"",
+            "timeout": 90
           }
         ]
       }

.claude-plugin/marketplace.json

@@ -9,7 +9,7 @@
       "name": "mempalace",
       "source": "./.claude-plugin",
       "description": "AI memory system — mine projects and conversations into a searchable palace. 19 MCP tools, auto-save hooks, guided setup.",
-      "version": "3.3.4",
+      "version": "3.3.6",
       "author": {
         "name": "milla-jovovich"
       }

.claude-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "mempalace",
-  "version": "3.3.4",
+  "version": "3.3.6",
   "description": "Give your AI a memory — mine projects and conversations into a searchable palace. 19 MCP tools, auto-save hooks, and guided setup.",
   "author": {
     "name": "milla-jovovich"

.claude-plugin/skills/mempalace/SKILL.md

@@ -16,10 +16,10 @@ Ensure `mempalace` is installed:
 mempalace --version
 ```
 
-If not installed:
+If not installed (uv recommended):
 
 ```bash
-pip install mempalace
+uv tool install mempalace   # or: pip install mempalace
 ```
 
 ## Usage

.codex-plugin/README.md

@@ -6,7 +6,7 @@ Give your AI a persistent memory -- mine projects and conversations into a searc
 
 - Python 3.9+
 - Codex CLI installed and configured
-- `pip install mempalace`
+- `uv tool install mempalace` (recommended) or `pip install mempalace`
 
 ## Installation
 
@@ -39,12 +39,17 @@ git clone https://github.com/MemPalace/mempalace.git
 cd mempalace
 ```
 
-2. Install the Python package:
+2. Install the Python package so the `mempalace-mcp` script lands on
+   your PATH (the bundled `plugin.json` invokes it by bare name):
 
 ```bash
-pip install -e .
+uv tool install --editable .   # or: pip install -e .
 ```
 
+   Plain `uv sync` is **not** enough here — it installs the scripts into
+   `.venv/bin/`, which Codex will not find unless you activate the venv
+   before launching Codex.
+
 3. The `.codex-plugin` directory is already in the repo root. Codex CLI will detect it automatically when you run Codex from inside the repository.
 
 4. Initialize your palace:

.codex-plugin/hooks.json

@@ -6,7 +6,7 @@
         "hooks": [
           {
             "type": "command",
-            "command": "${CODEX_PLUGIN_ROOT}/hooks/mempal-hook.sh session-start"
+            "command": "\"${CODEX_PLUGIN_ROOT}/hooks/mempal-hook.sh\" session-start"
           }
         ]
       }
@@ -17,7 +17,7 @@
         "hooks": [
           {
             "type": "command",
-            "command": "${CODEX_PLUGIN_ROOT}/hooks/mempal-hook.sh stop"
+            "command": "\"${CODEX_PLUGIN_ROOT}/hooks/mempal-hook.sh\" stop"
           }
         ]
       }
@@ -28,7 +28,7 @@
         "hooks": [
           {
             "type": "command",
-            "command": "${CODEX_PLUGIN_ROOT}/hooks/mempal-hook.sh precompact"
+            "command": "\"${CODEX_PLUGIN_ROOT}/hooks/mempal-hook.sh\" precompact"
           }
         ]
       }

.codex-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "mempalace",
-  "version": "3.3.4",
+  "version": "3.3.6",
   "description": "Give your AI a memory — mine projects and conversations into a searchable palace. 19 MCP tools, auto-save hooks, and guided setup.",
   "author": {
     "name": "milla-jovovich"

.github/workflows/ci.yml

@@ -50,6 +50,9 @@ jobs:
         with:
           python-version: "3.11"
           cache: 'pip'
-      - run: pip install "ruff>=0.4.0,<0.5"
+      # Keep this pin identical to the `ruff==` pin in pyproject.toml
+      # ([project.optional-dependencies].dev and [dependency-groups].dev)
+      # so CI and `pip install -e ".[dev]"` format/lint identically.
+      - run: pip install "ruff==0.15.14"
       - run: ruff check .
       - run: ruff format --check .

.github/workflows/deploy-docs.yml

@@ -29,7 +29,7 @@ jobs:
 
       - name: Configure GitHub Pages
         id: pages
-        uses: actions/configure-pages@v5
+        uses: actions/configure-pages@v6
 
       - uses: oven-sh/setup-bun@v2
         with:
@@ -44,6 +44,7 @@ jobs:
         env:
           DOCS_BASE: ${{ steps.pages.outputs.base_path }}
           DOCS_EDIT_BRANCH: ${{ github.ref_name }}
+          MEMPALACE_DOCS_GA_ID: ${{ vars.MEMPALACE_DOCS_GA_ID }}
         run: bun run docs:build
 
       - uses: actions/upload-pages-artifact@v5

.gitignore

@@ -36,3 +36,5 @@ venv/
 
 # ChromaDB local data
 *.sqlite3-journal
+.envrc
+.review-logs

.pre-commit-config.yaml

@@ -1,9 +1,10 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    # Keep in lock-step with the ruff version pinned in .github/workflows/ci.yml
-    # (>=0.4.0,<0.5). Using a newer rev here produces a different formatter
-    # output than CI and breaks `ruff format --check` in the lint job.
-    rev: v0.4.10
+    # Keep this rev in lock-step with the exact ruff pin in pyproject.toml
+    # ([project.optional-dependencies].dev / [dependency-groups].dev) and
+    # .github/workflows/ci.yml. A different ruff here produces different
+    # formatter output than CI and breaks `ruff format --check` in lint.
+    rev: v0.15.14
     hooks:
       - id: ruff
         args: [--fix]

.python-version

@@ -0,0 +1 @@
+3.12

CHANGELOG.md

@@ -6,6 +6,131 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 
 ---
 
+## [3.3.6] — 2026-05-24
+
+### Features
+
+- **Office-document mining via `--mode extract`.** New `mempalace mine <dir> --mode extract` ingests PDFs, Word (`.docx`), PowerPoint (`.pptx`), Excel (`.xlsx`), RTF, and EPUB books in addition to the existing source-code/text path. Install with `pip install mempalace[extract]` — pulls `striprtf` for RTF and MarkItDown (with `[docx,pdf,pptx,xlsx]` sub-extras) for the binary formats. Python 3.9 users get RTF coverage only because MarkItDown requires 3.10+. Drawers from the extract path carry `extract_mode` metadata so the convo miner's "already mined?" check and drawer-id generation stay isolated per mode (#1528). (#1555)
+
+- **Virtual line numbers + surgical closet pointers.** Stored drawers now carry virtual line numbers so the read CLI verb and closet pointers can cite exact line ranges. Closet pointers (Tier 6a) include date+line-range information derived from filename and content-body date parsing (`python-dateutil` is now a core dep), so MemPalace can point you to exact lines on exact dates rather than just "somewhere in this drawer." (#1555, #1584)
+
+- **Within-wing hallways.** When two entities (people, projects, topics) co-occur in the same drawer, the miner now records a "hallway" — a graph edge connecting them inside that wing. Computed automatically as part of the post-mine step in `compute_hallways_for_wing` so the graph grows incrementally with new content, no separate command. Foundation for cross-room entity navigation inside one palace. (#1558, #1560)
+
+- **Cross-wing tunnels promoted from hallways.** When the same entity appears in hallways across multiple wings, MemPalace now automatically promotes that into a tunnel — letting queries hop from one person's wing to a project wing they appear in, without anyone calling `create_tunnel` manually. Topic tunnels from the existing `compute_topic_tunnels` path remain unchanged. (#1565)
+
+- **Living-memory dynamics (Hebbian potentiation + Ebbinghaus decay).** Hallways and tunnels get stronger every time the same connection is reinforced by new content ("what fires together wires together") and fade gradually if a connection stops appearing in incoming drawers. Navigation weights track real palace usage instead of being static, so retrieval ranking improves over time as the palace is actually used. (#1578)
+
+- **API-tool transcripts auto-route to `wing_api`.** Conversation transcripts from API-style AI tools (Claude Code, Claude.ai, ChatGPT, Slack-bot exports, generic OpenAI-shape JSON, etc.) now route into a dedicated `wing_api` instead of mixing into the human-conversation wings. Keeps tool-call traffic from polluting personal/project wings and improves search precision when you're looking for "what did *I* say" vs. "what did the agent say." (#1236)
+
+- **Multilingual embedding by default for new installs: `embeddinggemma-300m` ONNX (q8, MRL→384-dim).** MemPalace's previous embedder (`all-MiniLM-L6-v2`) is trained English-only — cross-lingual cosine similarity on parallel-translated text averages 0.35 across DE/FR/HI/IT/KO/RU (RU at 0.17, near-orthogonal). A Russian-speaking user effectively cannot find their own memories, which breaks the "100% recall" design promise from CLAUDE.md. New `EmbeddinggemmaONNX` class in [`mempalace/embedding.py`](mempalace/embedding.py) brings this to 0.88 average (validated lossless vs the Ollama gguf via direct ONNX-runtime test). Lazy-downloads `onnx-community/embeddinggemma-300m-ONNX` (~300 MB) on first use via `huggingface_hub`. Output is truncated to 384 dims via Matryoshka Representation Learning so the model is a drop-in for ChromaDB's 384-dim collections — no schema change. Sim prefix (`"task: sentence similarity | query: "`) is applied automatically.
+
+  Onboarding (`python -m mempalace.onboarding`) now offers the multilingual model as the default — choosing it writes `embedding_model: embeddinggemma` to `config.json` so subsequent runs pick it up without re-prompting. Existing installs that never set the env var or ran onboarding stay on `minilm` (back-compat). `MEMPALACE_EMBEDDING_MODEL=minilm|embeddinggemma` overrides both. Switching models on an existing palace requires re-embedding — run `mempalace repair rebuild-index` after the change. (#1483)
+
+- **Multilingual deps moved to core.** `huggingface_hub`, `tokenizers`, and `numpy` are now required deps so the multilingual path works out of the box after `pip install mempalace`. The `[multilingual]` extra is kept as a no-op alias for back-compat with install scripts. The 300 MB ONNX model itself is still lazy-downloaded on first use, not at install time.
+
+- **Friendlier ChromaDB EF-name-mismatch error.** Switching `MEMPALACE_EMBEDDING_MODEL` on an existing palace without running `rebuild-index` previously surfaced ChromaDB's bare `Embedding function conflict: new: X vs persisted: Y` `ValueError` — accurate but didn't tell users how to recover. `ChromaBackend.get_collection()` now wraps that error and points at both options: revert the env var, or run `mempalace repair rebuild-index --palace <path>`. (#1483)
+
+- **`hooks.auto_save` toggle for silent-mode sessions.** New config knob (and `--silent` CLI flag wiring through the save hook) lets users opt out of automatic diary saves on `Stop` / `PreCompact`. Useful for "silent mode" sessions where you don't want every conversation captured. Default behavior is unchanged — auto-save still runs unless explicitly disabled. (#711)
+
+- **Filter common English content words from entity detection.** High-frequency English content words ("system", "user", "memory", "project", "context", etc.) were getting tagged as entities by the per-drawer detector and polluting the entity registry as "people." A shipped COCA wordlist (top-N content words) is now consulted during entity classification so these get filtered before they reach the registry. Hardened against malformed JSON in the bundled wordlist. (#1605)
+
+- **Case-insensitive entity matching at mine time.** The initial palace build (`mempalace init`) matched entity names case-insensitively, but the per-drawer tagger used during incremental mining did not — so the same person was tagged differently between init and ingest ("Aya" vs. "aya" became distinct entities). The incremental tagger now mirrors the case-insensitive matcher, restoring entity-tag consistency across the palace lifecycle. (#1557)
+
+### Bug Fixes
+
+- **Silent data loss in three upsert paths.** Three upsert sites (file ingest, conversation ingest, and one repair branch) were calling the embedder on unchunked content, silently truncating at the embedder's max-token limit. Long drawers landed with only their leading section indexed, breaking the "100% recall" promise on long-form content. All three now route through the chunker first so the full document is embedded and stored. (#1540, follow-up to #1539)
+
+- **Paragraph chunker emitted oversized chunks for long paragraphs.** The paragraph splitter assumed paragraphs were always shorter than `CHUNK_SIZE` and emitted them whole; long paragraphs (legal text, dense technical writeups) silently exceeded the embedder's context window. The splitter now hard-caps each emitted chunk to honor `CHUNK_SIZE`. (#1538, fixes #1534)
+
+- **Per-file chunk cap was hardcoded and too low for large transcripts.** A safety limit capped chunks per file at a value tuned for source code; mining very large conversation transcripts silently dropped the tail past that cap. Now configurable, with the default raised to cover realistic transcript sizes. (#1554, fixes #1455)
+
+- **Hook subprocess / ChromaDB deadlock on Windows.** Stop/PreCompact hooks could deadlock against an already-open ChromaDB client on Windows, leaving the host (Claude Code) stuck waiting on the hook. Three-part fix: stale-PID timeout on mine-lock reclamation, idle-exit path in the MCP server, and structured errors when the deadlock pattern is detected so the host can recover. (#1562, fixes #1552)
+
+- **`create_tunnel` corrupted hyphenated wing names.** The endpoint parser split on `-`, so wings whose name contained a hyphen (`mem-palace`, `my-app`) were truncated mid-name and the tunnel pointed at a non-existent endpoint. Endpoint parsing now preserves the full slug. (#1529, fixes #1504)
+
+- **MCP knowledge-graph cache produced duplicate graphs for symlinked / differently-cased palace paths.** Cache key was the raw path string, so `/Users/me/.mempalace/palace` and `/Users/me/.mempalace/Palace` (case-folded on macOS) or a symlinked alias produced two separate cached `KnowledgeGraph` instances pointing at the same SQLite file, with stale-read risk. Cache now normalizes via `realpath` + `normcase` so they collapse onto a single canonical key. (#1383, fixes #1372)
+
+- **Save-hook truncated hyphenated project folder names.** Wing-name parser was splitting on `-` and keeping only the first segment, so `mem-palace` became `mem`. Fix preserves the full project-folder slug so hyphenated palaces stay coherent across hook invocations. (#1424, fixes #1410)
+
+- **Miner silently skipped symlinks.** Users were confused about missing data after mining; the miner was skipping symlinks without surfacing it. Now logs each skipped symlink with the reason so the gap is visible. (#1466, fixes #1462)
+
+- **Host-leaked `PYTHONPATH` could shadow MemPalace's own modules at import.** Package `__init__` now strips leaked entries on import so the imported MemPalace is always the installed one. (#1439, fixes #1423)
+
+- **macOS stock-bash hook scripts.** Hook scripts used `mapfile` (bash 4+), breaking macOS's stock `/bin/bash` 3.2. Switched to a sed pipeline so hooks work out of the box on every Mac. (#1441, fixes #1440)
+
+- **Plugin Stop/PreCompact hooks could hang indefinitely on a stuck child.** Bounded timeout ensures the host can always make forward progress even if MemPalace's child process is unhealthy. (#1470, fixes #1465)
+
+- **MCP handlers now return structured JSON-RPC errors for malformed input.** Unknown parameter names returned `-32602 Invalid params` instead of an unstructured Python `TypeError`; parameters of the wrong shape return a structured error instead of a raw traceback. (#1500, #1513)
+
+- **CLI distinguished "palace doesn't exist" from "palace exists but is empty".** Two states that look the same to a new user are now reported separately with actionable next-step messages for each. (#1532, fixes #1498)
+
+- **`mempalace repair` post-pass: VACUUM + FTS5 rebuild.** After a palace repair, the SQLite knowledge-graph file kept fragmented pages and a stale FTS5 index; running `VACUUM` and rebuilding FTS5 at the end reclaims disk and restores search performance. (#1523)
+
+- **Convo miner mode isolation.** The "already mined?" check and drawer-id generation ignored `extract_mode`, so switching modes either re-mined the same content (data dup) or collided drawer IDs across modes. Scoping by mode keeps each mode's drawers isolated and dedup-correct. (#1528, fixes #1505)
+
+- **FTS5 validation at end of mine.** Mining now validates the FTS5 index integrity as a post-step so corruption is caught at write time, not at read time. (#1548, fixes #1537)
+
+- **`hooks_cli` crashed on shallow install paths.** Code indexed `Path.parents[3]` assuming a deep install tree, raising `IndexError` when MemPalace ran from a shallow path (e.g. `/opt/mp`). Adds a guard so shallow installs no longer crash on hook commands. (#1585)
+
+- **HNSW segment quarantine: zero-byte vs missing-dim.** Earlier quarantine heuristic flagged any HNSW segment missing the `dim` metadata field as corrupt, but most were recoverable; the check now distinguishes recoverable-missing-dim from actually-corrupt, preserving working index segments. Zero-byte link-list files (partially-written segments) are now rejected outright. (#1452, #1461, fixes #1457)
+
+- **Mine-lock holder file written as UTF-8 instead of cp1252.** Non-ASCII Windows usernames and paths no longer corrupt the lock file and break stale-lock detection. (#1438)
+
+- **Miner slot claim now writes a placeholder PID immediately.** Crash between claim and PID-write no longer leaves a phantom lock. (#1543, fixes #1443)
+
+- **`mine_convos` now runs inside `mine_palace_lock`.** Two concurrent `convos mine` invocations can no longer corrupt the index. (#1477)
+
+- **Migration tool cleanup.** ChromaDB-version migration tool now closes its SQLite connection and removes the temp palace directory if an exception fires mid-migration; failed migrations stop leaking file handles and disk. Entity-registry atomic-write now deletes its `.tmp` sidecar if the write or rename fails. (#1216, #1408, fixes #1373)
+
+- **Repair tool tolerated empty/None metadata cells.** ChromaDB occasionally returns cells with empty metadata dicts or `None` during rebuild; both are now coerced to sensible defaults so the rebuild completes and otherwise-stuck palaces recover. (#1459, #1445, fixes #1426)
+
+- **`create_tunnel` MCP handler now propagates errors.** Bad endpoint or direction was being swallowed and returned as misleading success; now propagates as a structured MCP error. (#1546, fixes #1473)
+
+- **Explicit tunnels were stored at a hardcoded ``~/.mempalace/tunnels.json`` path that ignored ``MempalaceConfig.palace_path``.** Drawers, KG triples, the people map, and every other piece of palace state honour the configured ``palace_path`` (and the ``MEMPALACE_PALACE_PATH`` env var), but ``palace_graph._TUNNEL_FILE`` was a module-level constant initialised once from ``os.path.expanduser("~") + "/.mempalace/tunnels.json"``. Under any setup where ``$HOME`` is isolated from the configured palace — subagent profiles with their own ``$HOME``, sandboxes, multi-tenant hosts, container mounts that move the palace to ``/srv/`` — drawers landed in the configured palace while tunnels silently landed in a different file that no other process touching the same palace could see. Worst case is the agentic one: an isolated worker calls ``create_tunnel`` then ``list_tunnels`` and gets back its own write from the bubble, so the worker self-confirms a tunnel that doesn't exist in the shared palace and reports completion to the orchestrator. ``palace_graph._TUNNEL_FILE`` is replaced by ``_get_tunnel_file()`` which derives the path from a new ``MempalaceConfig.tunnel_file`` property (sibling of ``palace_path``). The default single-user install is unchanged because the default ``palace_path`` is still ``~/.mempalace/palace`` and its sibling ``tunnels.json`` is the legacy path. Backwards-compatibility: if the configured tunnel file does not exist but a file is present at the pre-3.3.6 hardcoded ``~/.mempalace/tunnels.json`` path AND the two paths differ, ``_load_tunnels`` logs a one-line ``WARNING`` naming both paths and returns an empty list — we intentionally do NOT auto-migrate because silently merging tunnel state across two locations risks clobbering newer data; the user moves or copies the file themselves. (#1467)
+
+- **``create_tunnel`` did not validate that the source and target rooms actually exist in the chroma index.** ``_require_name`` only checked that wing/room names were non-empty strings; nothing queried the collection to confirm at least one drawer carried matching ``{wing, room}`` metadata. Pointing an explicit tunnel at a phantom room — common when an agent fabricates a room name it expects to exist, or types a slug wrong — silently succeeded. Combined with the read-bubble described in the previous fix, an agent could ``create_tunnel`` → ``list_tunnels`` and have both calls return its own bogus write. ``create_tunnel`` now calls ``_check_room_exists(wing, room, col)`` for both endpoints before persisting an explicit tunnel; if either endpoint has zero matching drawers the call raises ``ValueError`` naming the offending wing/room pair. Three deliberate carve-outs: (1) ``kind != "explicit"`` skips validation because topic tunnels generated by ``compute_topic_tunnels`` use synthetic ``topic:<name>`` room identifiers that don't correspond to real chroma rooms; (2) ``_get_collection`` returning ``None`` (palace not yet created, transient backend failure, tests without a real chroma backend) skips validation rather than fail-closed — matches the tolerance pattern used everywhere else in ``palace_graph``; (3) exceptions raised by the underlying ``col.get(where=..., limit=1, include=[])`` query are logged and treated as "can't verify, allow" so a temporary index fault never blocks legitimate writes. **Behaviour change:** existing callers that previously created tunnels pointing at empty rooms (e.g. as scaffolding before mining them) will now raise ``ValueError``. File the drawer first, then create the tunnel — this is the order the documentation has always recommended. (#1468)
+
+### Performance
+
+- **Convo miner pre-fetches mined-set once.** Was issuing one `WHERE` query per file to check "already mined?"; now pre-fetches the full mined set once, slashing wall time on large transcript corpuses. (#1474)
+
+- **`rebuild_index` progress callback.** Multi-hour rebuilds now report progress with default ETA printer; users no longer have to guess whether the process is making progress. (#1487)
+
+- **MCP cold-start diagnostics + opt-in warmup.** Adds visibility into which embedder is loading and how long it takes, plus an opt-in warmup path so users can see and address slow first-query latency. (#1530, fixes #1495)
+
+### Internal
+
+- ``palace_graph._TUNNEL_FILE`` (module-level constant) replaced by ``_get_tunnel_file(config=None)`` and ``_legacy_tunnel_file()``. Tests previously monkeypatching the constant must now monkeypatch the resolver functions. The ``tests/test_palace_graph_tunnels.py::_use_tmp_tunnel_file`` helper, ``tests/test_closets.py::TestTunnels`` setup/teardown, and three tests in ``tests/test_miner.py`` were updated accordingly. Topic-tunnel tests in ``test_miner`` continue to work without stubbing ``_get_collection`` because ``kind="topic"`` short-circuits the new validation path.
+
+---
+
+## [3.3.5] — 2026-05-09
+
+### Bug Fixes
+
+- **MCP `tool_search` now retries once on transient `Error finding id` from chromadb's HNSW flush window.** After a bulk CLI mine, ChromaDB's HNSW segment metadata can be unflushed for ~30-60s; wing-scoped MCP search hits `Internal error: Error finding id` during that window. `tool_search` now detects this transient via response-shape sniffing, drops both the MCP-local client cache and `_DEFAULT_BACKEND._clients` / `_freshness` for the palace, sleeps 2s, and retries once. Successful retries are tagged with `index_recovered: true` so callers can observe when it fired; non-transient errors bypass the retry path entirely. Partial fix for the broader #1315 cluster — `tool_check_duplicate` and other index-touching tools still need the same wrapper. (#1396, refs #1082, #1315)
+- **`mempalace_diary_read` silently dropped entries on agent-name case mismatch.** `tool_diary_write` stored the `agent` metadata verbatim after `sanitize_name`, which preserves case, while `tool_diary_read` filtered by exact match. Writing as `"Claude"` and reading as `"claude"` (or vice-versa) returned zero rows. Both endpoints now lowercase `agent_name` immediately after sanitization, so reads are case-insensitive and the default per-agent wing slug is stable across casings. **Behavior change:** entries written prior to this fix under mixed-case agent names will not match the new lowercase filter; run `mempalace repair` if you need to migrate legacy diary metadata. (#1243)
+- **Knowledge-graph triples with `valid_to < valid_from` were silently invisible.** `KnowledgeGraph.query_entity()` filters with `valid_from <= as_of AND valid_to >= as_of`, so an inverted interval matches no `as_of` and the row is durably stored but unreachable — a P0 data-integrity foot-gun any caller that mixes up the two date params can hit. `add_triple()` now rejects inverted intervals at write time with a clear `ValueError` naming both bounds. Open intervals (one bound only) and point-in-time facts (`valid_from == valid_to`) remain accepted unchanged. (#1214)
+- **`ChromaBackend.close_palace()` / `close()` did not release the SQLite file lock.** Evicted clients sat in `_clients` without `close()`, and chromadb 1.5.x retains the rust-side SQLite lock until GC. Reopening the same palace path after `shutil.rmtree` + recreate within one process failed with `SQLITE_READONLY_DBMOVED` (code 1032). New `_close_client()` helper now calls `PersistentClient.close()` (with a try/except fallback for older chromadb) on `close_palace()`, on whole-backend `close()`, and on the `_client()` invalidation path that detects a missing `chroma.sqlite3`. The mtime/inode auto-invalidation branch is intentionally left alone — callers there may still hold a live `ChromaCollection`. (#1067, #1105)
+- **`EntityRegistry.save()` could leave a corrupt or empty `entity_registry.json` on crash.** `Path.write_text()` is not atomic — kernel sees `open('w')` (truncate), `write`, `close`, and any failure between truncate and full-flush (power loss, OOM, FS-full, kill -9) wipes the months-of-mining people/projects map silently (the registry's `load()` swallows `JSONDecodeError`). Save now writes to a sibling `.tmp` in the same directory, `fsync`s, `chmod 0o600`s, then `os.replace()`s into place — atomic on POSIX and Windows. The previous registry stays intact on any crash before the rename returns. (#1215)
+- **`miner.detect_room` bidirectional substring matching caused systemic misrouting.** The priority-1 (path parts) and priority-2 (filename) checks used `c in part or part in c` against room names + keywords, so any token that was an unbounded substring of a room name (or vice versa) matched. Priority-1 iterates left-to-right and returns on first match, so `views/billing-page/src/Foo.test.tsx` routed to an `interviews` room because `"views" in "interviews"` matched before reaching `billing-page`. Both call sites now use a `_name_matches` helper that compares names as equal or as separator-bounded tokens of each other (split on `-`, `_`, `.`, `/`). (#1004, closes #1002)
+- **`mempalace compress` crashed on large palaces.** `regenerate_closets` fetched all closet_llm drawers in a single `col.get()`, which trips `SQLITE_MAX_VARIABLE_NUMBER` on palaces above ~32k drawers. Mirrors the #851 fix in `miner.py`: drawer fetch is now paginated at `batch_size=5000`. Per-source aggregation works across batches, so the LLM regeneration call still groups chunks correctly. (#1073, #1107)
+- **CLI and `fact_checker --stdin` mojibaked non-ASCII content on Windows.** Python defaults `sys.stdin`/`stdout`/`stderr` to the system ANSI codepage (cp1252/cp1251/cp950), so `mempalace search > out.txt` and piped fact_checker invocations corrupted Cyrillic / CJK drawer text at the process boundary. New `mempalace/_stdio.py` helper reconfigures all three streams to UTF-8 on `sys.platform == "win32"`, with per-stream `errors` policy: `surrogateescape` on stdin (preserves bad bytes from redirected files for the consumer's parser), `replace` on stdout/stderr (substitutes U+FFFD instead of `UnicodeEncodeError`-ing mid-print). With this, all three user-facing console_scripts (`mcp_server`, `hooks_cli`, `cli`/`fact_checker`) now reconfigure identically on Windows. (#1282)
+- **MCP knowledge-graph tools forwarded malformed date strings to SQLite.** `tool_kg_query` (`as_of`), `tool_kg_add` (`valid_from`), and `tool_kg_invalidate` (`ended`) accepted any string and produced empty result sets on natural-language inputs like `"March 2026"` or `"yesterday"` — callers (especially LLM agents) could not distinguish "no fact at this time" from "your date format was unrecognized." New `sanitize_iso_temporal()` validator in `config.py` (with `sanitize_iso_date()` retained as a backwards-compat wrapper) accepts `YYYY-MM-DD`, `YYYY-MM-DDTHH:MM:SSZ`, and `YYYY-MM-DDTHH:MM:SS+00:00` (normalized to the `Z` form), and passes `None`/`""` through unchanged; all three KG tools call it before values reach the storage layer. Partial dates (`YYYY`, `YYYY-MM`), naive datetimes, and non-UTC timezone offsets are rejected because KG queries compare TEXT temporal values where mixed formats silently return wrong results. **Behavior change:** previously-silent date typos now raise a clear `ValueError` naming the offending field; partial-date inputs that worked in 3.3.4 (`"2026"`, `"2026-05"`) no longer parse — pass a full `YYYY-MM-DD` or a canonical UTC datetime instead. (#1164, #1167, #1374, #1417)
+- **MCP server's `_kg` was a module-level singleton.** Multi-tenant hosts that rotate `MEMPALACE_PALACE_PATH` between tool calls hit the wrong sqlite file, because the KG was constructed once at import time while the ChromaDB side was already per-call via `_get_client()`. The KG is now resolved per-call through a lazy per-path cache (`_kg_by_path` keyed by `os.path.abspath`, with a double-checked-locking init under `_kg_cache_lock`). `tool_reconnect` drains and `close()`s cached KGs alongside the existing chroma reconnect. A `_call_kg` retry guard catches `sqlite3.ProgrammingError` once after a reconnect race. (#1136, #1160)
+- **`mempalace repair` can now recover palaces whose HNSW segment writer is stuck on `apply_logs`.** Both the existing `--mode legacy` rebuild and the inline `cli.cmd_repair` path call `Collection.count()` as their first read — exactly the call that raises `chromadb.errors.InternalError: Failed to apply logs to the hnsw segment writer` on the corruption class introduced upstream and reported in #1308. Repair would print `Cannot recover — palace may need to be re-mined from source files` even though the underlying SQLite tables were fully intact (the corruption lives in the on-disk index files, not the data layer). New `--mode from-sqlite` reads `(id, document, metadata)` rows directly from `chroma.sqlite3` via a `segments` → `embeddings` → `embedding_metadata` join, never opens a chromadb client against the corrupt palace, and re-upserts everything into a fresh palace at `--palace`. `--source PATH` extracts from a corrupt palace already moved aside; `--archive-existing` handles the in-place case by renaming the existing palace to `<palace>.pre-rebuild-<timestamp>` before reading from it. Documents are re-embedded under the user's configured embedding function (the original HNSW vectors live in the corrupt `data_level0.bin` and cannot be recovered, but the embedding model is deterministic so search results remain semantically equivalent). Verified end-to-end on a 52,300-row real-world corrupt palace. (#1308)
+
+### Documentation
+
+- **`CONTRIBUTING.md` git-identity guidance.** New section asks contributors to verify `git config user.name` and `git config user.email` before pushing, with an explicit warning for agentic coding tools that may not inherit the user's normal Git config. Avoids placeholder/template author values in commit history. (#1385, closes #1317)
+
+### Internal
+
+- **Test reliability: `multiprocessing` start method.** `tests/test_palace_locks.py` and `tests/test_chroma_collection_lock.py` switched from `fork` to `spawn` for child processes. Under Python 3.13 the pytest parent is multi-threaded by the time these tests run (chromadb + onnxruntime each spawn background threads on import); `fork` snapshotting that state into the child without the threads themselves deadlocked Linux 3.13 and macOS CI jobs indefinitely while Linux 3.9 / 3.11 / Windows finished normally. macOS additionally forbids fork-without-exec via CoreFoundation. `spawn` re-imports modules in the child (~0.5s per Process — bounded by the 10 subprocesses these tests fork) but is safe under threading. (#1431)
+- **Test cleanup: SQLite connection lifecycle.** Wrapped naked `conn = sqlite3.connect(...)` blocks in `tests/test_backends.py`, `tests/test_sources.py`, and `tests/test_repair.py` with `contextlib.closing(...)`. The flat `conn.close()` pattern at the end of each test leaked the connection on any exception or assertion failure between connect and close, producing `ResourceWarning: unclosed database` noise in CI logs and creating a secondary risk of advisory-lock starvation on Python 3.13 / macOS. Mirrors the `try/finally` pattern already used in production code. (#1430)
+
+---
+
 ## [3.3.4] — 2026-04-30
 
 ### Added
@@ -19,17 +144,13 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 
 ### Bug Fixes
 
-- **Cross-wing topic tunnels for hyphenated dir names.** `mempalace init` recorded the `topics_by_wing` registry key under the raw directory name (e.g. `mempalace-public`), while `mempalace.yaml`'s `wing` field used the lower-cased + separator-collapsed slug (`mempalace_public`). At mine time the miner read the slug from the yaml and missed the registry, so `_compute_topic_tunnels_for_wing` returned `0` silently. Real-world: any project whose folder contained a hyphen or space lost every topic tunnel. Producer side: `cmd_init`, `room_detector_local`, `miner.load_config` no-yaml fallback, and `convo_miner` now all route through a shared `normalize_wing_name()` in `config.py` so future writes use the same key. Lookup side: `palace_graph.create_tunnel`, `list_tunnels`, `follow_tunnels`, and `find_tunnels` normalize incoming wing names too, so existing palaces with raw-name keys on disk also recover. (#1194, #1195, #1197, follow-up to #1180)
-- **HNSW index bloat from repeated resize+persist cycles.** ChromaDB's HNSW segment was growing into the tens of GB on palaces past ~15K drawers because `link_lists.bin` was being re-allocated on every flush. Setting `hnsw:batch_size` and `hnsw:sync_threshold` on collection metadata via the new `_HNSW_BLOAT_GUARD` constant pins the segment to one allocation per batch instead. Empirical: a fresh 39,792-drawer palace went from 30 GB on disk and segfaulting `mempalace status` to 376 MB and instant. Migration note — already-bloated palaces still need a `mempalace repair` or full re-mine; HNSW config is honoured at collection-create time only. (#1191, supersedes #346)
-- **`max_seq_id` poisoning from old `_fix_blob_seq_ids` shim.** The 0.6.x → 1.5.x BLOB-to-INTEGER migration was running `int.from_bytes(blob, 'big')` over chromadb 1.5.x's native `b'\x11\x11' + ASCII-digit` `max_seq_id` format, yielding ~1.23e18 integers that silently suppressed every subsequent `embeddings_queue` write for the affected segment. The shim is now narrowed to the `embeddings` table only, with an additional defense-in-depth guard that skips sysdb-10-prefixed BLOBs even there. New `mempalace repair --mode max-seq-id` un-poisons existing palaces either from a pre-corruption sidecar DB (exact restore) or heuristically (`MAX(embeddings.seq_id)` over the owning collection). (#1135)
-- **Auto-ingest hooks now mine the active transcript as `--mode convos`.** Stop and PreCompact hooks were spawning `mempalace mine <transcript-dir>` without `--mode`, defaulting to `projects` — so Claude Code session JSONLs were being ingested as if they were source code via `READABLE_EXTENSIONS`. The hooks now thread the correct mode through every spawn, and `MEMPAL_DIR` (when set) becomes additive rather than overriding the transcript path: a user with `MEMPAL_DIR` pointed at their project still gets the active conversation mined verbatim. Shell hooks also gained the same `_validate_transcript_path` rejection logic the Python entry point already had (extension + `..` traversal). (#1230, #1231)
+- **MCP server `tool_diary_write` SIGSEGV when default EF provider differs.** `mcp_server._get_collection` bypassed `ChromaBackend.get_collection` and called `client.get_collection` / `client.create_collection` without `embedding_function=`. ChromaDB 1.x persists the EF *identity* (its `name()`) with the collection but not the EF *instance/configuration*, so the MCP server's reopen silently bound chromadb's built-in `DefaultEmbeddingFunction` — its `name()` matches `mempalace.embedding`'s spoofed `"default"` so the identity check passes, but its provider list is chromadb's default rather than the user's resolved device. The miner / Stop hook ingest path routes through the backend helper and binds the configured EF instead. On bleeding-edge interpreters (python 3.14 + chromadb 1.5.x on Apple Silicon) the default provider selection could SIGSEGV the host process on first `col.add()`, killing the MCP stdio server and leaving every subsequent tool call returning `Connection closed` until Claude Code was relaunched. `_get_collection` now reuses `ChromaBackend._resolve_embedding_function()` on the reopen branches that actually open a collection (warm-cache reads stay zero-cost), matching the miner/backend path. (#1299, follow-up to #1262 / #1289)
+- **Hooks no longer recreate `~/.mempalace/` after the user removes it.** When `~/.mempalace/` is deleted (a strong "do not auto-capture" signal), the next `Stop`, `PreCompact`, or `SessionStart` hook would silently rebuild the dir hierarchy and ingest existing transcripts: `_log()` called `STATE_DIR.mkdir(parents=True, exist_ok=True)` unconditionally, so the very act of writing `[HH:MM] SESSION START …` recreated `~/.mempalace/hook_state/`; subsequent calls in the save path then materialized `palace/`, `wal/`, `knowledge_graph.sqlite3`, and N drawers from `~/.claude/projects/*.jsonl`. All four entry points (`hook_stop`, `hook_precompact`, `hook_session_start`, and `_log` itself) now check a new module-level `PALACE_ROOT = Path.home() / ".mempalace"` constant first and short-circuit (returning `{}` on stdout, never logging) when the directory is absent. The user-removable directory becomes a kill-switch — `rm -rf ~/.mempalace` is now a stable state. Net: 23 lines added in `mempalace/hooks_cli.py`, 5 unit tests in `tests/test_hooks_cli.py`. (#1305)
+- **Cross-wing topic tunnels for hyphenated dir names.** `mempalace init` recorded the `topics_by_wing` registry key under the raw directory name (e.g. `mempalace-public`), while `mempalace.yaml`'s `wing` field used the lower-cased + separator-collapsed slug (`mempalace_public`). At mine time the miner read the slug from the yaml and missed the registry, so `_compute_topic_tunnels_for_wing` returned `0` silently. Real-world: any project whose folder contained a hyphen or space lost every topic tunnel. Now both call sites route through a shared `normalize_wing_name()` in `config.py`. (#1194, follow-up to #1180)
 - **CLI `mempalace search` retrieval quality.** The CLI was using pure ChromaDB cosine distance with no BM25 rerank, so drawers containing every query term but embedding as noise (directory listings, diff output, shell logs) scored `Match: 0.0` alongside genuinely irrelevant results with no way to tell them apart. Wired the CLI through the same `_hybrid_rank` the `mempalace_search` MCP tool already used, and surfaced both `cosine=` and `bm25=` scores in the output so users see which component of the match is firing. MCP search was unaffected; this fixes the human-facing CLI parity gap.
 - **Legacy-palace distance-metric warning.** CLI search now detects palaces created before `hnsw:space=cosine` was consistently set and prints a one-line notice pointing at `mempalace repair`. Without the warning such palaces silently used L2 distance, under which the similarity display floored every result to `Match: 0.0`. New palaces mined today already set cosine correctly and now have invariant tests pinning that behavior so future refactors can't silently regress it. (#1179)
 - **Graceful Ctrl-C during `mempalace mine`.** Interrupting a long mine no longer dumps a multi-frame `KeyboardInterrupt` traceback. The main file-processing loop now catches the signal, prints `files_processed: N/M`, `drawers_filed: K`, and `last_file:` so the user knows what landed, then exits with code 130 (standard SIGINT). Already-filed drawers are upserted idempotently on re-mine via deterministic IDs, so resuming is safe. The hooks PID lock at `~/.mempalace/hook_state/mine.pid` is now also actively cleaned up in a `finally` when its entry points at us — clean exit, error, or interrupt — preventing the next hook fire from briefly waiting on a stale PID. (#1182)
 - **`mempalace init` is now idempotent across re-runs.** Running `init` twice on the same project produced different `origin.json` results because the first run wrote `entities.json` into the project directory, and the second run's corpus-origin sampling included that file as corpus content — shifting Tier 1's character-density math. Sampling now skips the per-project artifacts (`entities.json`, `mempalace.yaml`), so re-running `init` produces the same classification it did the first time. Pinned by an integration test in `tests/test_corpus_origin_integration.py`. (#TBD)
-- **HNSW divergence floor now scales with `hnsw:sync_threshold`.** The capacity probe added in #1227 hardcoded a 2,000-row floor for the "DIVERGED" decision, sized against chromadb's default `sync_threshold` of 1,000. The bloat-guard fix above (#1191) raised `hnsw:sync_threshold` to 50,000 without updating the divergence floor to track. Result: any new palace past ~100K drawers spent roughly 80% of each write cycle reporting `DIVERGED`, and `mcp_server._refresh_vector_disabled_flag` silently routed vector search to the BM25 fallback even though chromadb was behaving correctly. The floor now reads `hnsw:sync_threshold` from collection metadata and scales to `2 × sync_threshold`, preserving the legacy 2,000 fallback for older palaces that pre-date #1191. (#1287, fixes interaction between #1191 and #1227)
-- **Stop hook no longer crashes ChromaDB on reopen.** `ChromaBackend.get_collection(create=True)` was calling `client.get_or_create_collection` with metadata on every open. In chromadb 1.5.x, when that metadata differs from the stored collection metadata the Rust binding SIGSEGVs with no traceback — the failure mode behind the session-end stop-hook crashes reported in #1089. The call is now split into `get_collection` first, falling back to `create_collection` only when the collection does not yet exist. Existing palaces open without touching their metadata; new ones are created with the full settings as before. The MCP server's `_get_collection(create=True)` path (reached by `tool_add_drawer` and `tool_diary_write`, the latter being what the Stop hook fires at session end) carried the same metadata payload at a parallel call site and got the same try/except split applied, closing the crash class on both reopen paths. (#1089, #1262, #1289)
-- **`repair --mode max-seq-id` heuristic now decodes BLOB-typed `embeddings.seq_id` rows.** The recovery feature added in #1135 was running `int(row[0])` directly on the result of `MAX(e.seq_id)`. On palaces where chromadb 1.5.x has been writing seq_ids natively (8-byte big-endian uint64 BLOB), that raised `ValueError: invalid literal for int() with base 10: b'\x00\x00\x00\x00\x00\x00-\xae'` before the dry-run summary could print, leaving users with no path through the un-poison feature #1135 was specifically designed to provide. `_compute_heuristic_seq_id` now decodes BLOB return values via `int.from_bytes(val, "big")` and keeps the existing `int(val)` path for INTEGER rows. (#1254, #1288, follow-up to #1135)
 
 ---
 
@@ -184,6 +305,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 - Hall detection — routes drawer content to `emotions` / `technical` / `family` / `memory` / `identity` / `consciousness` / `creative` halls, enabling hall-based graph connectivity within wings (#835)
 
 ### Bug Fixes
+- Repair `max_seq_id` corruption caused by `_fix_blob_seq_ids` misinterpreting chromadb 1.5.x's sysdb-10 BLOB format (`b'\x11\x11'` + ASCII digits) as legacy 0.6.x big-endian BLOBs. The shim now skips the `max_seq_id` table entirely and guards the `embeddings` branch with a prefix check. New subcommand `mempalace repair --mode max-seq-id [--from-sidecar <path>]` restores affected palaces. Fixes silent drawer-write drops that began after chromadb 1.5.x upgrades on palaces that still had BLOB-typed `max_seq_id` rows at migration time.
 - Set `hnsw:space=cosine` metadata on all collection creation sites — fixes broken similarity scoring under ChromaDB's default L2 distance (#807, #218)
 - File-level locking prevents duplicate drawers when agents mine the same file concurrently (#784, #826)
 - Hybrid closet+drawer retrieval — closets boost ranking, never gate results (#795)

CLAUDE.md

@@ -36,26 +36,26 @@ We do not accept summarization of user content, cloud storage/sync features, tel
 ## Setup
 
 ```bash
-pip install -e ".[dev]"
+uv sync --extra dev   # recommended; or: pip install -e ".[dev]"
 ```
 
 ## Commands
 
 ```bash
 # Run tests
-python -m pytest tests/ -v --ignore=tests/benchmarks
+uv run pytest tests/ -v --ignore=tests/benchmarks
 
 # Run tests with coverage
-python -m pytest tests/ -v --ignore=tests/benchmarks --cov=mempalace --cov-report=term-missing
+uv run pytest tests/ -v --ignore=tests/benchmarks --cov=mempalace --cov-report=term-missing
 
 # Lint
-ruff check .
+uv run ruff check .
 
 # Format
-ruff format .
+uv run ruff format .
 
 # Format check (CI mode)
-ruff format --check .
+uv run ruff format --check .
 ```
 
 ## Project Structure

CONTRIBUTING.md

@@ -10,25 +10,56 @@ git clone https://github.com/<your-username>/mempalace.git
 cd mempalace
 git remote add upstream https://github.com/MemPalace/mempalace.git
 
-pip install -e ".[dev]"    # installs with dev dependencies (pytest, build, twine)
+# Recommended: uv (https://docs.astral.sh/uv/) handles the venv for you
+uv sync --extra dev
+
+# Or with pip in your own venv:
+# pip install -e ".[dev]"
+
+# Activate pre-commit hooks (one-time, per clone)
+pre-commit install
 ```
 
+The `pre-commit install` step is important: the repo has a
+`.pre-commit-config.yaml` that pins ruff to the exact version CI uses,
+but the actual git hook is per-machine and **must be installed
+locally**. Without this step, you can commit code that passes your
+local lint (using whatever ruff version you happen to have installed)
+but fails CI on push.
+
 ## Running Tests
 
 ```bash
-pytest tests/ -v
+uv run pytest tests/ -v
 ```
 
 All tests must pass before submitting a PR. Tests should run without API keys or network access.
 
+### Property-based tests (optional)
+
+`hypothesis` is available in the dev extras for property-based tests:
+
+```python
+from hypothesis import given, strategies as st
+
+@given(st.text(min_size=1, max_size=40))
+def test_function_never_fabricates_output_on_random_input(s):
+    # ... property that must hold for ANY string in the strategy
+```
+
+Hypothesis generates hundreds of inputs per test and shrinks failing
+cases to a minimal counterexample. Useful any time a function returns
+`Optional[X]` or has a wide input domain — it catches the failure-space
+gaps that hand-written positive tests miss.
+
 ## Running Benchmarks
 
 ```bash
 # Quick test (20 questions, ~30 seconds)
-python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json --limit 20
+uv run python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json --limit 20
 
 # Full benchmark (500 questions, ~5 minutes)
-python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json
+uv run python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json
 ```
 
 See [benchmarks/README.md](benchmarks/README.md) for data download instructions and reproduction guide.
@@ -49,7 +80,7 @@ assets/             ← logo + brand
 1. Fork the repo and create a feature branch: `git checkout -b feat/my-thing`
 2. Write your code
 3. Add or update tests if applicable
-4. Run `pytest tests/ -v` — everything must pass
+4. Run `uv run pytest tests/ -v` — everything must pass
 5. Commit with a clear message following [conventional commits](https://www.conventionalcommits.org/):
    - `feat: add Notion export format`
    - `fix: handle empty transcript files`
@@ -93,3 +124,15 @@ If you're planning a significant change, open an issue first to discuss the appr
 ## License
 
 MIT — your contributions will be released under the same license.
+
+## Git identity for contributions
+
+Before pushing commits, verify that Git is configured with an email address that GitHub can associate with your account:
+
+```bash
+git config user.name
+git config user.email
+```
+
+This is especially important when commits are created through agentic coding tools or automation, because those tools may not inherit your normal shell Git configuration. Avoid placeholder values such as `your@email.com` or localized template text; unresolved author emails can create avoidable provenance and SBOM review friction for downstream users.
+

README.md

@@ -1,10 +1,20 @@
 > [!CAUTION]
-> **Scam alert.** The only official sources for MemPalace are this
-> [GitHub repository](https://github.com/MemPalace/mempalace), the
-> [PyPI package](https://pypi.org/project/mempalace/), and the docs site at
-> **[mempalaceofficial.com](https://mempalaceofficial.com)**. Any other
-> domain — including `mempalace.tech` — is an impostor and may distribute
-> malware. Details and timeline: [docs/HISTORY.md](docs/HISTORY.md).
+> # 🚨 CRITICAL SECURITY WARNING: BEWARE OF SCAMS
+> **MemPalace has NO other official websites.**
+> 
+> The **ONLY** official sources are:
+> 1. This **[GitHub repository](https://github.com/MemPalace/mempalace)**
+> 2. The **[PyPI package](https://pypi.org/project/mempalace/)**
+> 3. The docs at **[mempalaceofficial.com](https://mempalaceofficial.com)**
+> 
+> **ANY other domain** (including `.tech`, `.net`, or other `.com` variants) is an **impostor** and may distribute **malware**. Do not download executables from untrusted sites. Details and timeline: [docs/HISTORY.md](docs/HISTORY.md).
+
+> [!IMPORTANT]
+> **🚨 Claude Code sessions expire in 30 days w/out auto-save hooks wired!** **[Read this →](https://github.com/MemPalace/mempalace/discussions/1388)**
+>
+> Need the shortest recovery/setup path? Use the
+> [Claude Code retention setup checklist](https://mempalaceofficial.com/guide/claude-code-retention.html).
+
 
 <div align="center">
 
@@ -45,11 +55,30 @@ Architecture, concepts, and mining flows:
 
 ## Install
 
+MemPalace ships a CLI, so install it in an isolated environment to avoid
+PEP 668 errors on Debian/Ubuntu/Homebrew Pythons and to keep mempalace's
+deps (`chromadb`, `numpy`, `grpcio`, …) from conflicting with anything
+else in your global site-packages.
+
+We recommend [`uv`](https://docs.astral.sh/uv/) — `uv tool install` puts
+the `mempalace` CLI in an isolated environment on your PATH:
+
 ```bash
-pip install mempalace
+uv tool install mempalace
 mempalace init ~/projects/myapp
 ```
 
+[`pipx`](https://pipx.pypa.io/) works the same way if you prefer it:
+`pipx install mempalace`.
+
+Prefer plain `pip` only inside an activated virtualenv where you
+explicitly want `import mempalace` available:
+
+```bash
+python -m venv .venv && source .venv/bin/activate
+pip install mempalace
+```
+
 ## Quickstart
 
 ```bash
@@ -116,9 +145,9 @@ own research page for their published numbers.
 ```bash
 git clone https://github.com/MemPalace/mempalace.git
 cd mempalace
-pip install -e ".[dev]"
+uv sync --extra dev   # or: pip install -e ".[dev]"
 # see benchmarks/README.md for dataset download commands
-python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json
+uv run python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json
 ```
 
 ---
@@ -149,6 +178,11 @@ system prompt:
 Two Claude Code hooks save periodically and before context compression:
 [mempalaceofficial.com/guide/hooks](https://mempalaceofficial.com/guide/hooks.html).
 
+If you are installing under time pressure, start with the
+[Claude Code retention setup checklist](https://mempalaceofficial.com/guide/claude-code-retention.html):
+wire the hooks, back up existing JSONL transcripts, and backfill them with
+`mempalace mine ~/.claude/projects/ --mode convos`.
+
 For per-message recall on top of the file-level chunks the hooks produce,
 run `mempalace sweep <transcript-dir>` periodically — it stores one
 verbatim drawer per user/assistant message, idempotent and resume-safe.
@@ -159,7 +193,7 @@ verbatim drawer per user/assistant message, idempotent and resume-safe.
 
 - Python 3.9+
 - A vector-store backend (ChromaDB by default)
-- ~300 MB disk for the default embedding model
+- ~300 MB disk for the embedding model. Onboarding (`python -m mempalace.onboarding`) offers `embeddinggemma-300m` (multilingual, 100+ languages, recommended) or `all-MiniLM-L6-v2` (English-only, ~30 MB). See the docstring at [`mempalace/embedding.py`](mempalace/embedding.py) for details and migration notes.
 
 No API key is required for the core benchmark path.
 
@@ -181,7 +215,7 @@ PRs welcome. See [CONTRIBUTING.md](CONTRIBUTING.md).
 MIT — see [LICENSE](LICENSE).
 
 <!-- Link Definitions -->
-[version-shield]: https://img.shields.io/badge/version-3.3.4-4dc9f6?style=flat-square&labelColor=0a0e14
+[version-shield]: https://img.shields.io/badge/version-3.3.6-4dc9f6?style=flat-square&labelColor=0a0e14
 [release-link]: https://github.com/MemPalace/mempalace/releases
 [python-shield]: https://img.shields.io/badge/python-3.9+-7dd8f8?style=flat-square&labelColor=0a0e14&logo=python&logoColor=7dd8f8
 [python-link]: https://www.python.org/

benchmarks/BENCHMARKS.md

@@ -344,7 +344,7 @@ The palace classifies each question into one of 5 halls. Pass 1 searches only wi
 ```bash
 git clone https://github.com/MemPalace/mempalace.git
 cd mempalace
-pip install -e ".[dev]"
+uv sync --extra dev   # or: pip install -e ".[dev]"
 mkdir -p /tmp/longmemeval-data
 curl -fsSL -o /tmp/longmemeval-data/longmemeval_s_cleaned.json \
   https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_s_cleaned.json
@@ -724,8 +724,8 @@ python benchmarks/longmemeval_bench.py /tmp/longmemeval-data/longmemeval_s_clean
 The question: how much of the 96.6% → 99.4% improvement is the heuristics, and how much would come from just using a better embedding model?
 
 ```bash
-pip install fastembed
-python benchmarks/longmemeval_bench.py /tmp/longmemeval-data/longmemeval_s_cleaned.json \
+uv pip install fastembed   # or: pip install fastembed
+uv run python benchmarks/longmemeval_bench.py /tmp/longmemeval-data/longmemeval_s_cleaned.json \
   --mode raw --embed-model bge-large
 ```
 

benchmarks/HYBRID_MODE.md

@@ -198,7 +198,7 @@ python benchmarks/longmemeval_bench.py data/longmemeval_s_cleaned.json --mode hy
 # Setup
 git clone https://github.com/MemPalace/mempalace.git
 cd mempalace
-pip install -e ".[dev]"
+uv sync --extra dev   # or: pip install -e ".[dev]"
 
 # Download data
 mkdir -p /tmp/longmemeval-data

benchmarks/README.md

@@ -7,7 +7,7 @@ Run the exact same benchmarks we report. Clone, install, run.
 ```bash
 git clone https://github.com/MemPalace/mempalace.git
 cd mempalace
-pip install -e ".[dev]"
+uv sync --extra dev   # or: pip install -e ".[dev]"
 ```
 
 ## Benchmark 1: LongMemEval (500 questions)

benchmarks/mine_bench.py

@@ -58,11 +58,7 @@ def build_corpus(dest: Path, n_files: int, paragraphs_per_file: int, seed: int)
             paragraphs.append(" ".join(words))
         (dest / f"doc_{i:03d}.md").write_text("\n\n".join(paragraphs))
     (dest / "mempalace.yaml").write_text(
-        "wing: bench\n"
-        "rooms:\n"
-        "  - name: general\n"
-        "    description: all\n"
-        "    keywords: [general]\n"
+        "wing: bench\nrooms:\n  - name: general\n    description: all\n    keywords: [general]\n"
     )
 
 
@@ -121,8 +117,7 @@ def _process_file_unbatched(filepath, project_path, collection, wing, rooms, age
             ]
             closet_lines = build_closet_lines(source_file, drawer_ids, content, wing, room)
             closet_id_base = (
-                f"closet_{wing}_{room}_"
-                f"{hashlib.sha256(source_file.encode()).hexdigest()[:24]}"
+                f"closet_{wing}_{room}_{hashlib.sha256(source_file.encode()).hexdigest()[:24]}"
             )
             closet_meta = {
                 "wing": wing,
@@ -155,7 +150,7 @@ def mine_once(project_dir: str, palace_path: str, batched: bool) -> tuple[int, f
     t0 = time.perf_counter()
     for filepath in files:
         if batched:
-            drawers, _ = miner.process_file(
+            drawers, _, _ = miner.process_file(
                 filepath=filepath,
                 project_path=project_path,
                 collection=collection,
@@ -217,9 +212,9 @@ def run_scenario(label: str, n_files: int, paragraphs_per_file: int, seed: int)
 
 
 SCENARIOS = {
-    "small":  ("Small files (~50 paragraphs)",  10, 50),
+    "small": ("Small files (~50 paragraphs)", 10, 50),
     "medium": ("Medium files (~200 paragraphs)", 20, 200),
-    "large":  ("Large files (~500 paragraphs)",  10, 500),
+    "large": ("Large files (~500 paragraphs)", 10, 500),
 }
 
 
@@ -237,7 +232,9 @@ def _env_summary(device_label: str) -> list[str]:
         import onnxruntime as ort
 
         ort_v = ort.__version__
-        providers = ",".join(p.replace("ExecutionProvider", "") for p in ort.get_available_providers())
+        providers = ",".join(
+            p.replace("ExecutionProvider", "") for p in ort.get_available_providers()
+        )
     except Exception:
         ort_v = "?"
         providers = "?"

benchmarks/model_eval/README.md

@@ -0,0 +1,237 @@
+# MemPalace small-model evaluation harness
+
+Evaluates ≤4B-parameter Ollama models (plus optional cloud reference models) on MemPalace's classification and extraction tasks. Outputs accuracy, latency (TTFT, TPS, e2e p50/p95), and VRAM per `(model, task, mode)` triple. Replaces vibe-based model selection with data.
+
+If you want to validate the findings on your own hardware, this README walks you end-to-end. The whole local matrix takes ~60 min on an RTX 3090.
+
+---
+
+## Reproducing the published results
+
+### Prerequisites
+
+- **GPU**: NVIDIA card, ~10 GB VRAM minimum for the Tier 1 set, ~14 GB for the FP16 variant. The published numbers are from an RTX 3090 (24 GB).
+- **Ollama**: install per [ollama.com/download](https://ollama.com/download). Tested against Ollama 0.23.2. Newer should work; older may break the `think` parameter or the `/api/ps` endpoint shape (the harness has a fallback for the latter).
+- **Python**: 3.10+ (project uses `uv` for env management).
+- **Disk**: ~30 GB free for the full local candidate set, ~24 GB for Tier 1 only.
+- **Ollama Cloud account** (optional): only needed if you want to re-run the cloud-tier ceiling measurements. Sign in via `ollama signin` before running.
+
+### 1. Set up the environment
+
+```bash
+git clone https://github.com/MemPalace/mempalace.git
+cd mempalace
+uv sync
+```
+
+### 2. Pull candidate models
+
+The full candidate list is in `benchmarks/model_eval/candidates.yaml`. Pull what you want; tier filters in the orchestrator only run what's installed locally.
+
+**Bulk-pull the Tier 1 set (the must-evaluate set, ~24 GB total)**:
+
+```bash
+# This script reads candidates.yaml and pulls every tier-1 model + the embedding model
+uv run python -c "
+import yaml
+import subprocess
+with open('benchmarks/model_eval/candidates.yaml') as f:
+    cands = yaml.safe_load(f)['candidates']
+for c in cands:
+    if c.get('tier') == 1:
+        print(f'pulling {c[\"tag\"]}')
+        subprocess.run(['ollama', 'pull', c['tag']], check=True)
+subprocess.run(['ollama', 'pull', 'nomic-embed-text'], check=True)
+"
+```
+
+To also pull Tier 2 (sub-3B sizes), the `modern` tier (Gemma 4, Granite 4.1, Ministral 3, Qwen 3.5), or Tier 3 (FP16 ceiling), substitute the filter: `c.get('tier') in (1, 2)`, `c.get('tier') == 'modern'`, etc.
+
+**For the cloud comparison** (optional, requires `ollama signin`):
+
+```bash
+for tag in gpt-oss:20b-cloud gpt-oss:120b-cloud qwen3-coder:480b-cloud \
+           deepseek-v3.1:671b-cloud deepseek-v4-flash:cloud deepseek-v4-pro:cloud \
+           kimi-k2.6:cloud; do
+  ollama pull "$tag"
+done
+```
+
+### 3. Smoke test (under 30 seconds)
+
+Confirm the harness works against one model and one task before committing to a full run:
+
+```bash
+uv run python -m benchmarks.model_eval.runner \
+  --model qwen3:4b-instruct-2507-q4_K_M \
+  --task calibration \
+  --mode default \
+  --dataset-dir benchmarks/model_eval/datasets
+```
+
+You should see ~20 inferences and JSON output with `accuracy: 0.95` (or close). If accuracy is much lower, check that `ollama list` shows the model loaded and that `nomic-embed-text` is pulled (needed for any open-set or memory task even though calibration doesn't use it).
+
+### 4. Run the matrix
+
+**Tier 1 only (~30-40 min on RTX 3090)**:
+
+```bash
+uv run python -m benchmarks.model_eval.orchestrator \
+  --candidates tier1 \
+  --tasks all \
+  --dataset-dir benchmarks/model_eval/datasets \
+  --output benchmarks/model_eval/results/$(date -u +%Y-%m-%d)-$(hostname).csv
+```
+
+**Everything local (~60-80 min)**:
+
+```bash
+uv run python -m benchmarks.model_eval.orchestrator \
+  --candidates local \
+  --tasks all \
+  --dataset-dir benchmarks/model_eval/datasets \
+  --output benchmarks/model_eval/results/$(date -u +%Y-%m-%d)-$(hostname).csv
+```
+
+**Cloud only (~25-50 min, n=30 to control cost)**:
+
+```bash
+uv run python -m benchmarks.model_eval.orchestrator \
+  --candidates cloud \
+  --tasks all \
+  --n 30 \
+  --dataset-dir benchmarks/model_eval/datasets \
+  --output benchmarks/model_eval/results/$(date -u +%Y-%m-%d)-cloud-$(hostname).csv
+```
+
+The orchestrator prints `[i/N]` progress, `acc=… e2e_p50=…ms vram=…` after each run, and writes the CSV incrementally — safe to Ctrl-C if you only want partial data.
+
+### 5. Render a report
+
+```bash
+uv run python -m benchmarks.model_eval.summarize \
+  --csv benchmarks/model_eval/results/$(date -u +%Y-%m-%d)-$(hostname).csv \
+  --output benchmarks/model_eval/reports/$(date -u +%Y-%m-%d)-$(hostname).md
+```
+
+Output: a markdown report with per-task rankings, production picks, open-set viability, and an instruct-vs-reasoning comparison.
+
+### 6. Compare with the published baseline
+
+The committed baseline CSVs are in `benchmarks/model_eval/results/`:
+
+- `2026-05-10-z690-ex-glacial.csv` — full local matrix (RTX 3090)
+- `2026-05-11-cloud-z690-ex-glacial.csv` — cloud tier
+- `2026-05-11-modern-z690-ex-glacial.csv` — modern tier (Gemma 4, Granite 4.1, Ministral 3, Qwen 3.5)
+- `2026-05-10-spotcheck-qwen3.csv` — reproducibility spot-check
+
+For **accuracy**, your numbers should land within ~1% of the baseline. Bigger drift suggests something different in your setup (different Ollama version, different model digest, different system prompt rendering).
+
+For **speed**, your numbers will differ — they depend on your GPU, driver, thermal state, and concurrent load. Use **relative rank within your machine** as the comparable signal, not absolute milliseconds.
+
+For **VRAM resident**, expect close agreement on the same Ollama version. Peak VRAM is noisier (it depends on other GPU activity at measurement time).
+
+### 7. Sharing your results
+
+If your results disagree with the published baseline in a meaningful way:
+
+1. Run the smoke test (step 3) on the specific model that disagrees, save the JSON output
+2. Open an issue against this repo with: your `ollama --version`, GPU model, CSV file attached or pasted, and the smoke-test JSON
+3. If the harness has a bug, we'll fix it. If the model behavior genuinely changed (Ollama Cloud model rotation, new quantization upstream), we'll re-run and document the drift in the report
+
+To attach your CSV to a follow-up PR or comparison study, drop it in `benchmarks/model_eval/results/` with a name like `YYYY-MM-DD-yourhostname.csv` and reference it in the analysis report.
+
+---
+
+## What it measures
+
+For each `(model, task, mode)`:
+
+- **Accuracy** — task-specific scoring against the labeled dataset
+- **TTFT** (time-to-first-token) — approximation from Ollama's `prompt_eval_duration + load_duration`, p50 and p95 over N=20 sample runs
+- **TPS** (tokens/second) — from Ollama's `eval_count / eval_duration`, p50 and p95
+- **e2e latency** — full single-classification time, p50 and p95
+- **VRAM resident** — model memory after warmup (read from `/api/ps`)
+- **VRAM peak** — peak GPU memory during inference (polled via `nvidia-smi` every 500ms)
+
+The first run of each model is discarded (cache + GPU clock ramp).
+
+## Tasks
+
+- `room_classification` — closed-set (model picks from a provided room list) and open-set (model invents a slug). 101 samples.
+- `entity_extraction` — JSON list of entities per sample. 50 samples, 247 ground-truth entities.
+- `memory_extraction` — structured memory items per sample. 40 samples, 55 ground-truth memories across 5 types.
+- `calibration` — simple 5-class sentence-type. 20 samples. Sanity check the harness.
+
+All datasets are synthetic (no real-person info). Generated once and frozen so benchmark numbers stay comparable across runs.
+
+If you need to extend the dataset, **add** samples; don't replace existing ones, otherwise prior numbers stop being comparable.
+
+## CLI references
+
+```bash
+# Single (model, task, mode)
+uv run python -m benchmarks.model_eval.runner --help
+
+# Matrix runs with tier filtering
+uv run python -m benchmarks.model_eval.orchestrator --help
+
+# CSV → markdown report
+uv run python -m benchmarks.model_eval.summarize --help
+```
+
+Tier filter values: `tier1`, `tier2`, `tier3`, `tier<=N`, `local` (everything not tier=cloud), `cloud` (everything tier=cloud), `modern` (the Gemma 4 / Granite 4.1 / Ministral 3 / Qwen 3.5 additions), or any exact model tag for a single-model run.
+
+## Reusing existing infrastructure
+
+The harness uses `mempalace.llm_client.get_provider("ollama", model=tag)` and `provider.classify(...)` — the same code path as production. For thinking-capable models, the runner always passes `think=False` so hybrid models stay in fast-classification mode.
+
+No new HTTP plumbing. No reimplementation of provider abstraction. The harness benchmarks the same code that ships.
+
+## Hardware reporting
+
+Every result file includes the test machine's metadata (CPU, GPU, VRAM total, Ollama version, OS, hostname). Speed numbers are **not portable across machines** — use them for relative ranking on a single setup. Accuracy numbers cross-port cleanly.
+
+## Contributions welcome
+
+### Adding models we missed
+
+The published candidate list is what one engineer + one search pass surfaced. There are absolutely small instruct models we didn't catch. If you know of a competitive ≤4B-parameter model that should be in the comparison, please:
+
+1. Add an entry to `candidates.yaml` following the existing schema (`tag`, `family`, `size_b`, `variant`, `quantization`, `expected_vram_mb`, `tier`, `notes`)
+2. Run it through the smoke test then the matrix using the existing `--candidates <your-tag>` filter
+3. Open a PR with the new candidate row + CSV results + a one-line analysis-report addendum
+
+Particularly interested in: function-calling-tuned models (Phi-4 mini, Nemotron-mini), recent instruction-tuned variants from research labs (Hermes, Dolphin, OpenHermes), and quantization-aware-trained variants of established families. If the model has multiple competitive quantizations, pick the smaller one that's within accuracy noise (per the project finding "newer ≠ better" and the quantization sweet-spot rule from the analysis report).
+
+### Adding other inference backends
+
+The harness is wired through `mempalace.llm_client.get_provider()`, which already supports three provider types: `ollama` (currently used here), `openai-compat`, and `anthropic`. That means any **OpenAI-compatible** local server should plug in with minimal work:
+
+- **LM Studio** — serves an OpenAI-compatible API on `http://localhost:1234/v1` by default
+- **llama.cpp server** — `./server` exposes OpenAI-compat on `http://localhost:8080/v1`
+- **vLLM** — `--port 8000` runs an OpenAI-compatible endpoint
+- **unsloth studio** — likewise serves OpenAI-compat for inference
+- **Docker Model Runner** — exposes models over OpenAI-compat on a per-model port
+- **Hugging Face TGI / TEI** — OpenAI-compatible endpoints
+
+The plumbing pieces a contributor would need to add:
+
+1. **A backend-selection flag** in `runner.py` and `orchestrator.py` (e.g. `--backend ollama|openai-compat|anthropic|lm-studio|...`) that constructs the right provider via `get_provider(backend_name, model=tag, endpoint=...)`.
+2. **Backend-specific timing extraction in `metrics.py`.** The current `extract_timing()` reads Ollama's `eval_count`, `prompt_eval_duration`, etc. Other backends report timing differently (OpenAI's `usage.completion_tokens`, llama.cpp's `tokens_per_second`, etc.). The harness will currently fill those columns with zeros for non-Ollama backends — degrades gracefully but loses the per-request timing breakdown.
+3. **Backend-specific VRAM probe.** Ollama exposes `/api/ps`; LM Studio has its own status endpoint; llama.cpp doesn't expose model memory directly. For non-Ollama backends, `vram_resident_mb` would return `None` (already handled). Peak-VRAM via `nvidia-smi` still works regardless.
+4. **A `candidates.yaml` field** to mark backend per candidate (e.g. `backend: lm-studio`).
+
+If you implement a new backend, the existing dataset and scoring code applies unchanged — the harness's accuracy numbers stay comparable across backends. Open a PR with the runner/orchestrator changes plus one CSV from your backend so we can validate the integration on a known model.
+
+If you build something niche that's worth comparing (Apple MLX, Intel OpenVINO, AMD ROCm-specific runtimes, edge-device runtimes like Termux + llama.cpp on phones), please share the methodology. Cross-runtime comparisons are exactly the kind of follow-up this harness is designed to enable.
+
+## Notes for harness maintainers
+
+When modifying the harness internals:
+
+- **`format: json` mode is enforced locally but ignored on Ollama Cloud.** Per Ollama's docs. Cloud models that "happen to" emit JSON do so by default behavior, not because Ollama enforces it. The `kimi-k2.6:cloud` memory-extraction `valid_json_rate: 0.37` is a documented manifestation.
+- **The memory-extraction `hallucination_rate` metric over-penalizes thorough models.** See the analysis report. Trust `mean_coverage` until a follow-up refines the scoring.
+- **Cloud reproducibility is worse than local.** ~6 points of drift on `gpt-oss:20b` between runs. Cloud numbers should be reported as ranges, not point estimates.
+
+Read `reports/2026-05-10-analysis.md` for the full set of findings, surprises, and methodology notes from the original run.

benchmarks/model_eval/candidates.yaml

@@ -0,0 +1,321 @@
+# Candidate models for evaluation.
+#
+# Pull with: ollama pull <tag>
+# Reference: ollama.com/library/<family>/tags
+#
+# Tiers control orchestrator default selection:
+#   tier1: must-evaluate set (~10 models, balanced size/quantization)
+#   tier2: worth running if time permits
+#   tier3: special cases (FP16 ceilings, QAT comparisons)
+#   community: third-party GGUF fine-tunes (igorls, heretic, etc.)
+#   local: models already pulled locally — no extra disk needed
+
+candidates:
+  # ── Tier 1: balanced q4_K_M sweep across families ────────────────────
+  - tag: qwen3:4b-instruct-2507-q4_K_M
+    family: qwen3
+    size_b: 4.0
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 2500
+    tier: 1
+    notes: "Recommended baseline. Explicit instruct release."
+
+  - tag: qwen3:4b-instruct-2507-q8_0
+    family: qwen3
+    size_b: 4.0
+    variant: instruct
+    quantization: q8_0
+    expected_vram_mb: 4300
+    tier: 1
+    notes: "Higher precision comparator for the q4_K_M baseline."
+
+  # Pure reasoning variants are excluded by policy. MemPalace
+  # classification tasks always run with thinking disabled
+  # (see benchmarks/model_eval/runner.py and the project memory
+  # "no reasoning models for classification"). To benchmark a
+  # hybrid model, use its base tag and the runner forces
+  # think=False on every call.
+
+  - tag: qwen2.5:3b-instruct-q4_K_M
+    family: qwen2.5
+    size_b: 3.0
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 1900
+    tier: 1
+    notes: "Strong older comparator. Often punches above its size class."
+
+  - tag: gemma3:4b-it-q4_K_M
+    family: gemma3
+    size_b: 4.0
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 3300
+    tier: 1
+    notes: "Top non-Qwen contender at this size."
+
+  - tag: gemma3:4b-it-qat
+    family: gemma3
+    size_b: 4.0
+    variant: instruct
+    quantization: qat
+    expected_vram_mb: 4000
+    tier: 1
+    notes: "Quantization-aware-trained variant. Compare to post-hoc q4_K_M."
+
+  - tag: gemma3:1b-it-q4_K_M
+    family: gemma3
+    size_b: 1.0
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 815
+    tier: 1
+    notes: "Smallest credible Gemma."
+
+  - tag: llama3.2:3b-instruct-q4_K_M
+    family: llama3.2
+    size_b: 3.0
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 2000
+    tier: 1
+    notes: ""
+
+  - tag: llama3.2:1b-instruct-q4_K_M
+    family: llama3.2
+    size_b: 1.0
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 808
+    tier: 1
+    notes: ""
+
+  - tag: phi3.5:3.8b-mini-instruct-q4_K_M
+    family: phi3.5
+    size_b: 3.8
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 2400
+    tier: 1
+    notes: ""
+
+  # ── Tier modern: model families missed in the first benchmark pass ──
+  # These existed at the time of the first run but weren't surfaced by
+  # the initial ollama.com search. Held in a separate tier so we can
+  # filter `--candidates modern` for cheap re-runs.
+
+  - tag: qwen3.5:4b-q4_K_M
+    family: qwen3.5
+    size_b: 4.0
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 3400
+    tier: modern
+    notes: "Qwen 3.5 4B. Newer generation. Same tag resolves to qwen3.5:4b (default quantization is q4_K_M)."
+
+  - tag: gemma4:e2b-it-q4_K_M
+    family: gemma4
+    size_b: 2.0
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 7200
+    tier: modern
+    notes: "Gemma 4 e2b (extended 2B). On-disk 7.2 GB — bigger than its parameter count suggests, likely vision/multimodal layers."
+
+  - tag: gemma4:e4b-it-q4_K_M
+    family: gemma4
+    size_b: 4.0
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 9600
+    tier: modern
+    notes: "Gemma 4 e4b (extended 4B). On-disk 9.6 GB."
+
+  - tag: granite4.1:3b-q4_K_M
+    family: granite
+    size_b: 3.0
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 2100
+    tier: modern
+    notes: "IBM Granite 4.1 3B. Smaller, focused on enterprise tasks."
+
+  - tag: ministral-3:3b
+    family: mistral
+    size_b: 3.0
+    variant: instruct
+    quantization: default
+    expected_vram_mb: 3000
+    tier: modern
+    notes: "Mistral Ministral 3 3B. No explicit q4_K_M tag; default 3.0 GB."
+
+  # ── Tier 2: smaller sizes for VRAM-constrained users ─────────────────
+  - tag: qwen3:1.7b-q4_K_M
+    family: qwen3
+    size_b: 1.7
+    variant: mixed
+    quantization: q4_K_M
+    expected_vram_mb: 1400
+    tier: 2
+    notes: "qwen3 small. Variant ambiguous in this tag."
+
+  - tag: qwen3:0.6b-q4_K_M
+    family: qwen3
+    size_b: 0.6
+    variant: mixed
+    quantization: q4_K_M
+    expected_vram_mb: 523
+    tier: 2
+    notes: "Edge-device size. Variant ambiguous."
+
+  - tag: qwen2.5:1.5b-instruct-q4_K_M
+    family: qwen2.5
+    size_b: 1.5
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 986
+    tier: 2
+    notes: ""
+
+  - tag: qwen2.5:0.5b-instruct-q4_K_M
+    family: qwen2.5
+    size_b: 0.5
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 398
+    tier: 2
+    notes: "Smallest viable for any non-trivial task."
+
+  - tag: gemma3:270m-it-q8_0
+    family: gemma3
+    size_b: 0.27
+    variant: instruct
+    quantization: q8_0
+    expected_vram_mb: 292
+    tier: 2
+    notes: "Tiny. Probably fails most tasks but worth a baseline."
+
+  # ── Tier 3: ceilings and special cases ───────────────────────────────
+  - tag: qwen3:4b-instruct-2507-fp16
+    family: qwen3
+    size_b: 4.0
+    variant: instruct
+    quantization: fp16
+    expected_vram_mb: 8100
+    tier: 3
+    notes: "Full-precision ceiling for the leader. Quality target."
+
+  # ── Cloud tier: Ollama-hosted reference and ceiling models ──────────
+  # Cloud models run on Ollama's servers, not locally. No local VRAM
+  # cost. Latency includes network round-trip. Used as accuracy ceiling
+  # to size the gap between local-best and what a 100x bigger model can
+  # do. Inclusion in benchmarks does not imply production use; the
+  # privacy and cost trade-offs make local models the default.
+  - tag: gpt-oss:20b-cloud
+    family: gpt-oss
+    size_b: 20.0
+    variant: instruct
+    quantization: cloud
+    expected_vram_mb: 0
+    tier: cloud
+    cloud: true
+    notes: "Smaller cloud reference. Faster + cheaper than the 120B."
+
+  - tag: gpt-oss:120b-cloud
+    family: gpt-oss
+    size_b: 120.0
+    variant: instruct
+    quantization: cloud
+    expected_vram_mb: 0
+    tier: cloud
+    cloud: true
+    notes: "Mid-tier cloud reference."
+
+  - tag: qwen3-coder:480b-cloud
+    family: qwen3-coder
+    size_b: 480.0
+    variant: coder-tuned
+    quantization: cloud
+    expected_vram_mb: 0
+    tier: cloud
+    cloud: true
+    notes: "Code-tuned variant. May behave differently on natural-language classification."
+
+  - tag: deepseek-v3.1:671b-cloud
+    family: deepseek
+    size_b: 671.0
+    variant: instruct
+    quantization: cloud
+    expected_vram_mb: 0
+    tier: cloud
+    cloud: true
+    notes: "Older DeepSeek generation. Kept for historical comparison."
+
+  - tag: deepseek-v4-flash:cloud
+    family: deepseek
+    size_b: 284.0
+    variant: instruct
+    quantization: cloud
+    expected_vram_mb: 0
+    tier: cloud
+    cloud: true
+    notes: "DeepSeek V4 Flash MoE — 284B total, 13B activated. Newer generation, optimized for speed."
+
+  - tag: deepseek-v4-pro:cloud
+    family: deepseek
+    size_b: 671.0
+    variant: instruct
+    quantization: cloud
+    expected_vram_mb: 0
+    tier: cloud
+    cloud: true
+    notes: "DeepSeek V4 Pro. 1M context, multiple reasoning modes."
+
+  - tag: kimi-k2.6:cloud
+    family: kimi
+    size_b: 1000.0
+    variant: instruct
+    quantization: cloud
+    expected_vram_mb: 0
+    tier: cloud
+    cloud: true
+    notes: "Kimi K2.6 — current Moonshot release. Replaces kimi-k2:1t-cloud which returned HTTP 500 on the first run."
+
+  # ── Community tier: third-party GGUF variants ────────────────────────
+  - tag: igorls/gemma4-e4b-classifier:latest
+    family: gemma4
+    size_b: 7.5
+    variant: classifier-tuned
+    quantization: q4_K_M
+    expected_vram_mb: 6000
+    tier: community
+    notes: "Igor's classifier fine-tune on Gemma 4 E4B. Purpose-built for classification tasks."
+
+  - tag: igorls/gemma4-e4b-classifier:Q8_0
+    family: gemma4
+    size_b: 7.5
+    variant: classifier-tuned
+    quantization: q8_0
+    expected_vram_mb: 8000
+    tier: community
+    notes: "Q8_0 variant of Igor's classifier fine-tune. 8.0 GB on disk — may require >8 GB VRAM."
+
+  - tag: igorls/gemma-4-E4B-it-heretic-GGUF:Q4_K_M
+    family: gemma4
+    size_b: 7.5
+    variant: instruct
+    quantization: q4_K_M
+    expected_vram_mb: 6612
+    tier: community
+    notes: "Heretic fine-tune. Architecture is Gemma 4 Extended; actual params=7.5B despite E4B name. Outside ≤4B scope — compare separately."
+
+  # ── Local tier: models already pulled, no extra disk needed ──────────
+  - tag: gemma4:e4b
+    family: gemma4
+    size_b: 4.0
+    variant: instruct
+    quantization: default
+    expected_vram_mb: 4510
+    tier: local
+    notes: "Already pulled locally. Default quantization (q4_K_M equivalent)."

benchmarks/model_eval/datasets/README.md

@@ -0,0 +1,168 @@
+# MemPalace Small-Model Benchmark Datasets
+
+Synthetic, hand-curated evaluation datasets for testing small (≤4B parameter) Ollama models on the four classification and extraction tasks that matter for MemPalace's local-first memory pipeline.
+
+- **Synthetic only.** No real-person names, no real organizations, no real PII.
+- **Public-safe.** Intended to be committed to the public MemPalace open-source repository.
+- **Generated 2026-05-10.**
+- **211 samples** total across four tasks (100 synthetic + 1 real-format-flavored sample in room_classification).
+
+## Tasks
+
+| Task | Samples | Purpose |
+|---|---|---|
+| `room_classification/` | 101 | Given a session summary and a room list, pick the right room (or `"other"`). Tests the closed-set room-routing path used at filing time. 100 synthetic + 1 real-format-flavored (rc_101). |
+| `entity_extraction/` | 50 | Given a conversation excerpt, extract typed entities (person / project / place / organization). Tests the entity detector that feeds the knowledge graph. |
+| `memory_extraction/` | 40 | Given a snippet, extract memory-worthy items (decision / preference / fact / opinion / commitment). Tests the layer-promotion heuristic. |
+| `calibration/` | 20 | Sentence-type classification (question / command / statement / exclamation / greeting). A trivially-easy baseline used to detect a broken model load before more meaningful evals run. |
+
+## The five fictional personae
+
+The 100 synthetic room-classification samples are split 20-per-agent across these synthetic agents (rc_101 is a separate real-format-flavored sample assigned to Solas). Each agent has a fixed room taxonomy; per-sample room lists are drawn as 5-10 room subsets that always include `general`.
+
+### 1. Aria — research assistant (she/her)
+
+Synthesizes academic papers.
+
+Rooms: `about`, `projects/meta-cognition`, `projects/embedding-spaces`, `projects/topic-clustering`, `skills/latex`, `skills/python`, `skills/statistical-tests`, `daily-logs`, `general`
+
+### 2. Solas — coding agent (they/them)
+
+Writes and reviews code; focuses on systems work.
+
+Rooms: `about`, `projects/distributed-tracing`, `projects/parser-combinators`, `projects/type-systems`, `skills/rust`, `skills/ocaml`, `skills/llvm`, `daily-logs`, `general`
+
+### 3. Fenra — creative writing agent (she/her)
+
+Drafts fiction and worldbuilds.
+
+Rooms: `about`, `projects/world-building`, `projects/character-arcs`, `projects/dialogue-engine`, `skills/narrative-design`, `skills/etymology`, `skills/mythology`, `daily-logs`, `general`
+
+### 4. Bramble — gardening agent (he/him)
+
+Companion-plant and ecological-gardening advice.
+
+Rooms: `about`, `projects/pollinator-paths`, `projects/soil-microbes`, `projects/native-species`, `skills/phenology`, `skills/plant-pathology`, `skills/propagation`, `daily-logs`, `general`
+
+### 5. Thresh — finance/accounting agent (they/them)
+
+Small-business bookkeeping.
+
+Rooms: `about`, `projects/invoice-parsing`, `projects/tax-prep`, `projects/cashflow-models`, `skills/double-entry`, `skills/depreciation-rules`, `skills/ifrs`, `daily-logs`, `general`
+
+## Distribution stats
+
+### `room_classification/` (101 samples)
+
+| Metric | Value |
+|---|---|
+| Samples per agent | Aria 20, Solas 20, Fenra 20, Bramble 20, Thresh 20 |
+| Messy samples (`include_messy_features: true`) | 14 (target: ~15%) |
+| Closed-set `"other"` | 18 (target: ~20%) |
+| Closed-set `"general"` (escape hatch used as best fit) | 6 |
+| Closed-set match to a non-`general` non-`other` room | 76 |
+
+`"other"` distribution by agent: Aria 2, Solas 3, Fenra 3, Bramble 7, Thresh 3. Bramble skews higher because gardening conversations frequently span topics that don't cleanly fit a single skill room (e.g. seed-saving + plant pathology, soil chemistry, vegetable-pest control).
+
+### `entity_extraction/` (50 samples)
+
+| Metric | Value |
+|---|---|
+| Total entities | 247 |
+| `person` | 114 |
+| `organization` | 74 |
+| `project` | 32 |
+| `place` | 27 |
+| Entities per sample (min / max / avg) | 3 / 9 / 4.9 |
+
+Person-skew is intentional: real MemPalace sessions are heavily person-centric, and the entity detector's hardest job is disambiguating people (the dataset deliberately reuses some last names like "Halloran" across distinct individuals to test this).
+
+### `memory_extraction/` (40 samples)
+
+| Metric | Value |
+|---|---|
+| Total memories | 55 |
+| `decision` | 12 |
+| `preference` | 9 |
+| `commitment` | 12 |
+| `fact` | 15 |
+| `opinion` | 7 |
+| Memories per sample | 1 (25 samples) or 2 (15 samples) |
+
+### `calibration/` (20 samples)
+
+Exactly 4 samples per class: `question`, `command`, `statement`, `exclamation`, `greeting`. Designed to be unambiguous — any working model should hit ≥95% accuracy here. If a model fails calibration, it is broken or misconfigured and the more interesting tasks should not be run.
+
+## Schema reference
+
+### `room_classification/dataset.jsonl`
+
+```json
+{"id": "rc_001", "agent": "Aria", "session_summary": "...", "include_messy_features": false}
+```
+
+### `room_classification/labels.jsonl`
+
+```json
+{"id": "rc_001", "closed_set_label": "projects/meta-cognition", "preferred_open_label": "meta-cognition-research"}
+```
+
+`closed_set_label` is either a room from that sample's `rooms` list, or exactly `"other"`.
+`preferred_open_label` is the slug a human annotator would invent if labeling freely (lowercase, hyphenated).
+
+### `room_classification/room_lists.jsonl`
+
+```json
+{"id": "rc_001", "rooms": ["about", "projects/meta-cognition", "skills/python", "daily-logs", "general"]}
+```
+
+Always includes `"general"`. Always 5-10 rooms. Composition varies per sample so the model can't just memorize an agent's full taxonomy.
+
+### `entity_extraction/dataset.jsonl` + `labels.jsonl`
+
+```json
+{"id": "ent_001", "text": "..."}
+{"id": "ent_001", "entities": [{"name": "Aria", "type": "person"}, {"name": "Embedding Spaces", "type": "project"}]}
+```
+
+Entity types: `person`, `project`, `place`, `organization`.
+
+### `memory_extraction/dataset.jsonl` + `labels.jsonl`
+
+```json
+{"id": "mem_001", "text": "..."}
+{"id": "mem_001", "memories": [{"type": "decision", "content": "..."}]}
+```
+
+Memory types: `decision`, `preference`, `fact`, `opinion`, `commitment`.
+
+### `calibration/dataset.jsonl` + `labels.jsonl`
+
+```json
+{"id": "cal_001", "text": "Could you fix the indentation on this Python file?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_001", "label": "command"}
+```
+
+The `classes` field is identical across all 20 samples, included in each line for self-contained downstream loading.
+
+## Annotation conventions
+
+- **Decision vs. commitment.** A *decision* changes how something will be done from now on (a policy or design choice); a *commitment* binds the speaker to a specific deliverable, often with a deadline. "Switching to Jaccard similarity" is a decision; "I'll deliver the draft Friday" is a commitment. When both apply to one sentence, both are emitted.
+- **Fact vs. opinion.** A *fact* is a claim about the world that is checkable in principle. An *opinion* is signaled by hedge words ("I think", "honestly", "my read") or by being a value judgement. Borderline cases (e.g. an arguable empirical claim about a paper) lean to opinion when the speaker frames it as their own assessment.
+- **Preference.** Preferences attach to a person and describe how that person likes things done. They are durable across conversations, unlike one-off decisions.
+- **Closed-set "other".** Used when no listed room is a substantively better home for the content than `general`. If `general` is a clean fit (catch-all small talk, identity questions), the label is `general` rather than `other`.
+- **Ambiguous closed-set samples** (~10% of room_classification) have two rooms that fit; the label picks the one that better matches the dominant content of the session. `preferred_open_label` may name the secondary topic when the closed-set choice is forced.
+
+## Extending
+
+To regenerate or extend:
+
+1. **Add new personae.** Define a name, pronouns, role, and 7-9 rooms (always include `about`, `daily-logs`, `general`). Add 20 samples covering each room at least once, plus a few drift / messy / `"other"` cases.
+2. **Add more samples.** Keep the per-agent balance even. Maintain ~70/20/10 distribution for closed-set / `"other"` / ambiguous.
+3. **Add new tasks.** Pick a discriminative task that small models would plausibly fail in interesting ways (relation extraction, coreference, stance, etc.). Match the pattern of separate `dataset.jsonl` and `labels.jsonl` files keyed by `id`.
+
+## Provenance
+
+- Synthetic, generated 2026-05-10 for the MemPalace project.
+- No real-person names. Fictional personae and place/organization names invented for this benchmark.
+- Safe to commit to a public repository.

benchmarks/model_eval/datasets/calibration/dataset.de.jsonl

@@ -0,0 +1,20 @@
+{"id": "cal_001", "text": "Könntest du die Einrückung in dieser Python-Datei korrigieren?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_002", "text": "Um wie viel Uhr beginnt das Meeting morgen?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_003", "text": "Die Vorhersage zeigt Regen über das Wochenende.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_004", "text": "Das ist unglaublich!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_005", "text": "Hallo, Bramble.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_006", "text": "Führen Sie die Tests mit ausführlicher Ausgabe aus.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_007", "text": "Haben Sie kurz Zeit für einen Anruf?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_008", "text": "Vorsicht!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_009", "text": "Guten Morgen, Team.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_010", "text": "Ich habe gestern den Quartalsabschluss abgeschlossen.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_011", "text": "Zeigen Sie mir den AR-Aging-Bericht.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_012", "text": "Warum ist der Build auf aarch64 fehlgeschlagen?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_013", "text": "Hallo.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_014", "text": "Das Deployment war eine Katastrophe!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_015", "text": "Die Bestäuber-Erhebung identifizierte sieben heimische Bienenarten.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_016", "text": "Lösche die veralteten Branches vom Remote.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_017", "text": "Hallo, Aria.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_018", "text": "Wow, der Latenzabfall ist riesig!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_019", "text": "Können Sie das dritte Paper zusammenfassen?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_020", "text": "Der Kontenplan verwendet nun eine vierstellige Hierarchie.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}

benchmarks/model_eval/datasets/calibration/dataset.es.jsonl

@@ -0,0 +1,20 @@
+{"id": "cal_001", "text": "¿Podrías arreglar la sangría en este archivo de Python?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_002", "text": "¿A qué hora empieza la reunión mañana?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_003", "text": "El pronóstico prevé lluvias durante el fin de semana.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_004", "text": "¡Eso es increíble!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_005", "text": "Hola, Bramble.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_006", "text": "Ejecute las pruebas con resultados detallados.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_007", "text": "¿Estás disponible para una llamada rápida?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_008", "text": "¡Cuidado!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_009", "text": "Buenos días equipo.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_010", "text": "Terminé el cierre trimestral ayer.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_011", "text": "Muéstrame el informe de antigüedad de AR.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_012", "text": "¿Por qué falló la compilación en aarch64?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_013", "text": "Hola.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_014", "text": "¡Ese despliegue fue un desastre!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_015", "text": "La encuesta sobre polinizadores identificó siete especies de abejas nativas.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_016", "text": "Elimina las ramas obsoletas del control remoto.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_017", "text": "Hola, Aria.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_018", "text": "¡Vaya, esa caída de latencia es enorme!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_019", "text": "¿Puedes resumir el tercer artículo?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_020", "text": "El plan de cuentas ahora utiliza una jerarquía de cuatro dígitos.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}

benchmarks/model_eval/datasets/calibration/dataset.fr.jsonl

@@ -0,0 +1,20 @@
+{"id": "cal_001", "text": "Pouvez-vous corriger l'indentation de ce fichier Python ?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_002", "text": "À quelle heure commence la réunion demain ?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_003", "text": "Les prévisions annoncent de la pluie tout le week-end.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_004", "text": "C'est incroyable !", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_005", "text": "Bonjour, Bramble.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_006", "text": "Exécutez les tests avec une sortie verbeuse.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_007", "text": "Êtes-vous disponible pour un appel rapide ?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_008", "text": "Attention !", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_009", "text": "Bonjour, l'équipe.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_010", "text": "J'ai terminé la clôture trimestrielle hier.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_011", "text": "Montrez-moi le rapport de vieillissement AR.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_012", "text": "Pourquoi le build a-t-il échoué sur aarch64 ?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_013", "text": "Salut.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_014", "text": "Ce déploiement a été un désastre !", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_015", "text": "L'enquête sur les pollinisateurs a identifié sept espèces d'abeilles indigènes.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_016", "text": "Supprimer les branches obsolètes du dépôt distant.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_017", "text": "Salut, Aria.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_018", "text": "Wouah, cette baisse de latence est énorme !", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_019", "text": "Pouvez-vous résumer le troisième article ?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_020", "text": "Le plan comptable utilise désormais une hiérarchie à quatre chiffres.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}

benchmarks/model_eval/datasets/calibration/dataset.hi.jsonl

@@ -0,0 +1,20 @@
+{"id": "cal_001", "text": "क्या आप इस Python फ़ाइल का इंडेंटेशन ठीक कर सकते हैं?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_002", "text": "कल बैठक कितने बजे शुरू होगी?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_003", "text": "पूर्वानुमान के अनुसार सप्ताहांत तक बारिश होगी।", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_004", "text": "यह अविश्वसनीय है!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_005", "text": "नमस्ते, Bramble.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_006", "text": "विस्तृत आउटपुट के साथ परीक्षण चलाएँ।", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_007", "text": "क्या आप एक छोटी सी कॉल के लिए उपलब्ध हैं?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_008", "text": "सावधान!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_009", "text": "सुप्रभात, टीम।", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_010", "text": "मैंने कल तिमाही समापन पूरा कर लिया।", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_011", "text": "मुझे AR aging report दिखाओ।", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_012", "text": "aarch64 पर बिल्ड क्यों फेल हुआ?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_013", "text": "नमस्ते।", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_014", "text": "वह डिप्लॉयमेंट एक तबाही था!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_015", "text": "परागणकर्ता सर्वेक्षण ने सात देशी मधुमक्खी प्रजातियों की पहचान की।", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_016", "text": "रिमोट से पुरानी शाखाएं हटा दें।", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_017", "text": "अरे, Aria।", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_018", "text": "वाह, latency में वो गिरावट बहुत बड़ी है!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_019", "text": "क्या आप तीसरे पेपर का सारांश दे सकते हैं?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_020", "text": "खातों का चार्ट अब चार-अंकीय पदानुक्रम का उपयोग करता है।", "classes": ["question", "command", "statement", "exclamation", "greeting"]}

benchmarks/model_eval/datasets/calibration/dataset.it.jsonl

@@ -0,0 +1,20 @@
+{"id": "cal_001", "text": "Potresti correggere l'indentazione in questo file Python?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_002", "text": "A che ora inizia la riunione domani?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_003", "text": "Le previsioni indicano pioggia per tutto il fine settimana.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_004", "text": "È incredibile!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_005", "text": "Ciao, Bramble.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_006", "text": "Esegui i test con output dettagliato.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_007", "text": "Sei disponibile per una breve chiamata?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_008", "text": "Attenzione!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_009", "text": "Buongiorno, team.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_010", "text": "Ho terminato la chiusura trimestrale ieri.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_011", "text": "Mostrami l'AR aging report.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_012", "text": "Perché il build è fallito su aarch64?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_013", "text": "Ciao.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_014", "text": "Quel rilascio è stato un disastro!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_015", "text": "L'indagine sui pollinatori ha identificato sette specie di api native.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_016", "text": "Elimina i rami obsoleti dal remoto.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_017", "text": "Ehi, Aria.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_018", "text": "Wow, quel calo di latenza è enorme!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_019", "text": "Puoi riassumere il terzo articolo?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_020", "text": "Il piano dei conti ora utilizza una gerarchia a quattro cifre.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}

benchmarks/model_eval/datasets/calibration/dataset.jsonl

@@ -0,0 +1,20 @@
+{"id": "cal_001", "text": "Could you fix the indentation on this Python file?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_002", "text": "What time does the meeting start tomorrow?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_003", "text": "The forecast shows rain through the weekend.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_004", "text": "That's incredible!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_005", "text": "Hello, Bramble.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_006", "text": "Run the tests with verbose output.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_007", "text": "Are you available for a quick call?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_008", "text": "Watch out!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_009", "text": "Good morning, team.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_010", "text": "I finished the quarterly close yesterday.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_011", "text": "Show me the AR aging report.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_012", "text": "Why did the build fail on aarch64?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_013", "text": "Hi there.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_014", "text": "That deployment was a disaster!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_015", "text": "The pollinator survey identified seven native bee species.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_016", "text": "Delete the stale branches from the remote.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_017", "text": "Hey, Aria.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_018", "text": "Wow, that latency drop is huge!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_019", "text": "Can you summarize the third paper?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_020", "text": "The chart of accounts now uses a four-digit hierarchy.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}

benchmarks/model_eval/datasets/calibration/dataset.ko.jsonl

@@ -0,0 +1,20 @@
+{"id": "cal_001", "text": "이 Python 파일의 들여쓰기를 수정해 주실 수 있나요?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_002", "text": "내일 회의는 몇 시에 시작하나요?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_003", "text": "예보에 따르면 주말 내내 비가 올 것으로 보입니다.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_004", "text": "정말 놀라워요!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_005", "text": "안녕하세요, Bramble.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_006", "text": "테스트를 자세한 출력으로 실행하세요.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_007", "text": "잠깐 통화 가능하신가요?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_008", "text": "조심해!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_009", "text": "여러분, 좋은 아침입니다.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_010", "text": "어제 분기 결산을 마쳤습니다.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_011", "text": "AR 장기화 보고서를 보여주세요.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_012", "text": "왜 aarch64에서 빌드가 실패했나요?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_013", "text": "안녕하세요.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_014", "text": "그 배포는 재앙이었어!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_015", "text": "수분 매개자 조사에서 토종 벌 7종이 확인되었다.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_016", "text": "원격 저장소에서 오래된 브랜치를 삭제하세요.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_017", "text": "안녕, Aria.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_018", "text": "와, 지연 시간 감소폭이 엄청나네!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_019", "text": "세 번째 논문을 요약해 주실 수 있나요?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_020", "text": "계정과목표는 이제 4자리 계층 구조를 사용합니다.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}

benchmarks/model_eval/datasets/calibration/dataset.pt-BR.jsonl

@@ -0,0 +1,20 @@
+{"id": "cal_001", "text": "Você poderia corrigir o recuo neste arquivo Python?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_002", "text": "A que horas começa a reunião amanhã?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_003", "text": "A previsão é de chuva durante o fim de semana.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_004", "text": "É incrível!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_005", "text": "Olá, Bramble.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_006", "text": "Execute os testes com saída detalhada.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_007", "text": "Você está disponível para uma ligação rápida?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_008", "text": "Atenção!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_009", "text": "Bom dia, equipe.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_010", "text": "Terminei o fechamento trimestral ontem.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_011", "text": "Mostre-me o relatório de envelhecimento AR.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_012", "text": "Por que a compilação falhou no aarch64?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_013", "text": "Olá.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_014", "text": "Essa implantação foi um desastre!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_015", "text": "O levantamento de polinizadores identificou sete espécies de abelhas nativas.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_016", "text": "Exclua as ramificações obsoletas do controle remoto.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_017", "text": "Olá, Aria.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_018", "text": "Uau, essa queda de latência é enorme!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_019", "text": "Você pode resumir o terceiro artigo?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_020", "text": "O plano de contas agora usa uma hierarquia de quatro dígitos.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}

benchmarks/model_eval/datasets/calibration/dataset.ru.jsonl

@@ -0,0 +1,20 @@
+{"id": "cal_001", "text": "Не могли бы вы исправить отступы в этом файле Python?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_002", "text": "Во сколько завтра начинается встреча?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_003", "text": "Прогноз показывает дождь на всё выходные.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_004", "text": "Это невероятно!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_005", "text": "Привет, Bramble.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_006", "text": "Запустите тесты с подробным выводом.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_007", "text": "Вы свободны для короткого звонка?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_008", "text": "Осторожно!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_009", "text": "Доброе утро, команда.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_010", "text": "Я вчера завершил квартальное закрытие.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_011", "text": "Покажите мне отчет по срокам AR.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_012", "text": "Почему сборка не удалась на aarch64?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_013", "text": "Привет.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_014", "text": "Это развёртывание было катастрофой!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_015", "text": "Обследование опылителей выявило семь видов местных пчёл.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_016", "text": "Удалите устаревшие ветки с удалённого репозитория.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_017", "text": "Привет, Aria.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_018", "text": "Вау, какое огромное падение задержки!", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_019", "text": "Можешь кратко изложить третью статью?", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_020", "text": "План счетов теперь использует четырёхзначную иерархию.", "classes": ["question", "command", "statement", "exclamation", "greeting"]}

benchmarks/model_eval/datasets/calibration/dataset.zh.jsonl

@@ -0,0 +1,20 @@
+{"id": "cal_001", "text": "你能修复这个 Python 文件上的缩进吗？", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_002", "text": "明天会议几点开始？", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_003", "text": "天气预报显示整个周末都会有雨。", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_004", "text": "那真是难以置信！", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_005", "text": "你好，Bramble。", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_006", "text": "使用详细输出运行测试。", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_007", "text": "您可以快速致电吗？", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_008", "text": "当心！", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_009", "text": "早上好，团队。", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_010", "text": "我昨天完成了季度结算。", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_011", "text": "显示 AR 老化报告。", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_012", "text": "为什么在 aarch64 上构建失败？", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_013", "text": "你好呀。", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_014", "text": "那次部署是一场灾难！", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_015", "text": "传粉媒介调查确定了七种本地蜜蜂。", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_016", "text": "从远程删除过时的分支。", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_017", "text": "嘿，Aria。", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_018", "text": "哇，延迟下降幅度非常大！", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_019", "text": "你能总结一下第三篇论文吗？", "classes": ["question", "command", "statement", "exclamation", "greeting"]}
+{"id": "cal_020", "text": "会计科目表现在使用四位数的层次结构。", "classes": ["question", "command", "statement", "exclamation", "greeting"]}

benchmarks/model_eval/datasets/calibration/labels.jsonl

@@ -0,0 +1,20 @@
+{"id": "cal_001", "label": "command"}
+{"id": "cal_002", "label": "question"}
+{"id": "cal_003", "label": "statement"}
+{"id": "cal_004", "label": "exclamation"}
+{"id": "cal_005", "label": "greeting"}
+{"id": "cal_006", "label": "command"}
+{"id": "cal_007", "label": "question"}
+{"id": "cal_008", "label": "exclamation"}
+{"id": "cal_009", "label": "greeting"}
+{"id": "cal_010", "label": "statement"}
+{"id": "cal_011", "label": "command"}
+{"id": "cal_012", "label": "question"}
+{"id": "cal_013", "label": "greeting"}
+{"id": "cal_014", "label": "exclamation"}
+{"id": "cal_015", "label": "statement"}
+{"id": "cal_016", "label": "command"}
+{"id": "cal_017", "label": "greeting"}
+{"id": "cal_018", "label": "exclamation"}
+{"id": "cal_019", "label": "question"}
+{"id": "cal_020", "label": "statement"}

benchmarks/model_eval/datasets/entity_extraction/dataset.de.jsonl

@@ -0,0 +1,50 @@
+{"id": "ent_001", "text": "Aria erwähnte, dass das Paper des Tartine Lab zu retrieval-augmented evaluation endlich auf arXiv erschienen ist. Saela hatte darauf gewartet – sie leitet das Embedding Spaces-Projekt und möchte es im nächsten Preprint ihrer Gruppe zitieren. Das Labor befindet sich in Crestmoor, nur ein Stück die Straße hinunter von Bridgewater Studio, wo Fenra früher beratend tätig war. Anscheinend wechselt der Hauptautor, Gera Vossen, nächstes Semester an das Hollowmounts Institute."}
+{"id": "ent_002", "text": "Solas hat einen Fix für den Trace-Propagation-Bug im Projekt Distributed Tracing gepusht. Gereviewt von Brennan Lyle vom Runtime-Team bei Crestmoor Systems. Der Bug trat nur unter Last auf, weshalb der Stresstest des Aerwyn Telemetry-Konsortiums ihn vor unserem internen CI entdeckte."}
+{"id": "ent_003", "text": "Bramble ging den Entwurf des neuen Pollinator Paths-Korridors für den Wendelsea Botanic Garden durch. Die Entomologin des Gartens, Yumara Felk, schlug vor, am östlichen Rand Bergminze hinzuzufügen. Das Projekt wird vom Crestmoor Conservation Trust finanziert, der seit zwei Jahrzehnten die Arbeit mit einheimischen Pflanzen in der Region unterstützt."}
+{"id": "ent_004", "text": "Thresh schloss die Bücher für Bridgewater Studio ab. Der AR-Aging-Bericht markierte Carag-aana Press als Konto mit Zahlungsverzögerung — 78 Tage überfällig. Er nahm Kontakt zu Pell Halloran in der Kreditorenbuchhaltung von Carag-aana Press auf. Die Invoice-Parsing-Pipeline leitete drei ihrer Rechnungen automatisch in die Warteschlange für manuelle Prüfung weiter, weil die Positionen nicht mit der PO des Unterauftrags von Tartine Lab übereinstimmten."}
+{"id": "ent_005", "text": "Fenra hat mit dem Benutzer den dritten Akt des Aerwyn-Zyklus in einem Workshop erarbeitet. Saelas Handlungsbogen erreicht seinen Wendepunkt in den Hollowmounts, und die Wends sind entscheidende Zeugen ihrer Entscheidung. Der Benutzer möchte den Höhepunkt in Bryn-iili ansetzen, was ich angesichts des in Kapitel vier etablierten Salzschulden-Motivs für eine starke Wahl halte."}
+{"id": "ent_006", "text": "Die Pipeline von Topic Clustering ist während der nächtlichen Indizierung abgestürzt. Aria hat die Protokolle abgerufen und das Problem auf ein Speicherleck in der HDBSCAN-Bibliothek zurückgeführt – genauer gesagt auf das joblib-Backend im Rechencluster von Crestmoor. Sie hat ein Issue bei den Upstream-Maintainern eingereicht und Mette Olafsen in Kopie gesetzt, die die Hauptansprechpartnerin bei Crestmoor IT ist."}
+{"id": "ent_007", "text": "Brambles Tagebuch: Morgens Besuch im Bridgewater Community Garden, um die Situation mit den Kürbiswanzen zu beurteilen. Karis Tornau, die Freiwilligenkoordinatorin, führte mich durch ihren integrierten Pflanzenschutzplan. Nachmittags: Ralf Ginder beim Entwurf eines Native Species-Beetes für sein Grundstück im Distrikt Wendelsea geholfen. Abends: ein Paper des Hollowmounts Phenology Network zu Verschiebungen der Erstblüte durchgelesen."}
+{"id": "ent_008", "text": "Solas hat einen PR von Aerwyn Labs begutachtet. Es handelt sich um eine Parser-Kombinator-Bibliothek, die auf Megaparsec aufbaut und für das Projekt Type Systems entwickelt wurde. Der Maintainer, Pol Krisat, war sehr entgegenkommend. Sollte ich das dem Team der Crestmoor Compiler Group erwähnen — sie suchten nach etwas Ähnlichem."}
+{"id": "ent_009", "text": "Thresh hat an der IFRS-Einführung für Tartine Lab gearbeitet. Sie stellen von lokalen GAAP um, und die Cashflow Models, die wir im letzten Quartal erstellt haben, müssen an die neue Leasingbilanzierung nach IFRS 16 angepasst werden. Abstimmung mit ihrer CFO Ivora Tinn und ihrer externen Wirtschaftsprüferin Bek Halloran von Wendelsea Audit Partners."}
+{"id": "ent_010", "text": "Aria stellte eine Leseliste für den neuen Postdoc, Ren Solanke, zusammen, der dem Meta-Cognition-Projekt bei Crestmoor beitritt. Sie begann mit den grundlegenden Arbeiten von Bridgewater Cognitive Science, fügte dann die Reihe des Hollowmounts Institute über selbstreferenzielles Prompting hinzu und schließlich das aktuelle Preprint von Aerwyn Labs zur Chain-of-Thought-Stabilität."}
+{"id": "ent_011", "text": "Fenra und der Benutzer arbeiteten eine Stunde an der Dialogue Engine. Das aktuelle Beziehungsgraphen-Modell deckt einen spezifischen Grenzfall nicht ab: wenn zwei Charaktere aus verfeindeten Fraktionen (die Wends und die Drukar) zur Zusammenarbeit gezwungen werden. Ein Workaround wurde entworfen. Insbesondere muss Saelas Stimme weicher werden, wenn sie mit Hellis Mar spricht — es gibt eine Vorgeschichte, die die Engine nicht erfasst."}
+{"id": "ent_012", "text": "Bramble hat ein Pilzproblem bei den Altsorten-Äpfeln in Wendelsea Orchard festgestellt. Apfelschorf, klassisch. Die Obstgartenmanagerin, Doreth Ainsleigh, hatte keine Kupferspritzungen in der Vegetationsruhe durchgeführt. Wir erarbeiteten einen Übergangsplan mit ihr. Sie erwähnte, dass Crestmoor Cooperative Extension nächsten Monat einen Workshop veranstaltet, und wir waren beide der Meinung, dass sie daran teilnehmen sollte."}
+{"id": "ent_013", "text": "Solas debuggte einen segfault im Type Systems-repo, der nur auf aarch64 auftrat. Es stellte sich heraus, dass bei der Fehlerbehebung LLVM intrinsics eine Rolle spielten, die sich über targets hinweg unterschiedlich verhielten. Solas eröffnete ein upstream-issue bei den LLVM-Maintainern; Brennan Lyle von der Aerwyn Compiler Group bot an, auf seiner Seite einen bisect durchzuführen. Wir vergleichen unsere Notizen beim Treffen von Crestmoor Systems nächsten Donnerstag."}
+{"id": "ent_014", "text": "Thresh unterstützte Pell Halloran dabei, einen komplizierten Dreifachabgleich zwischen PO, Wareneingangsdokument und Rechnung von Hollowmounts Logistics abzustimmen. Die Abweichung stellte sich als Nichtübereinstimmung der Maßeinheiten heraus – die PO war in Kisten, die Rechnung in Stück. Die Invoice Parsing-Regeln wurden aktualisiert, um UoM-Nichtübereinstimmungen zukünftig automatisch zu kennzeichnen."}
+{"id": "ent_015", "text": "Aria hat drei Fragen zu statistischen Tests von Saela nacheinander durchgesehen. Die dritte war die kniffligste: ob bei gepaarten, aber nicht identischen Stichproben ein gepaarter oder ungepaarter Test verwendet werden soll. Aria ging ihr die Implikationen durch. Sie bereitet eine Überarbeitung für das Tartine Lab-Journal vor, und die Deadline ist Freitag."}
+{"id": "ent_016", "text": "Fenra arbeitete an Namenskonventionen für die Drukar des Aerwyn-Zyklus. Entwarf phonologische Regeln. Hellis Mar – eine zentrale Drukar-Figur in Kapitel acht – ist die empathische Ausnahme, und ihr Name muss sowohl die Drukar- als auch die Wendish-Lautsysteme überbrücken. Sandte das Arbeitsdokument an die Wendelsea Writing Group und setzte Saela in Kopie, die durch ihre Arbeit bei Bridgewater Cognitive Science ein scharfes Ohr für erfundene Sprachen hat."}
+{"id": "ent_017", "text": "Die Beratung von Bramble zu Pollinator Paths mit dem Gartenkomitee der Bridgewater Schools. Sie wollen ausschließlich heimische Pflanzen, haben aber ein enges Budget. Empfohlen wurde, mit den Topfplatten von Wendelsea Native Plant Nursery zu beginnen – sie sind günstiger als Gallonen-Behälter und wachsen schneller an. Gespräch mit deren Gründerin, Iset Karadzic, die zustimmte, den Schulen ihren Nonprofit-Tarif zu gewähren."}
+{"id": "ent_018", "text": "Solas arbeitete mit Pol Krisat an der Integration der neuen parser combinator Bibliothek in die Codebasis von Aerwyn Labs. Die Integration war sauber, außer an einer Stelle, wo ihr eigener Fehlertyp mit unserem Distributed Tracing Kontext kollidierte. Gelöst durch die Einführung eines kleinen Adapter-Traits. Die Integration wurde als separate PR hochgeladen, um die Überprüfung zu erleichtern."}
+{"id": "ent_019", "text": "Thresh nahm an einer Planungssitzung mit der Geschäftsleitung von Bridgewater Studio teil. Die CFO Ivora Tinn führt ein neues Cashflow Models-Dashboard für das Führungsteam ein. Die Leiterin der Operationen Karis Tornau möchte eine Aufschlüsselung nach Abteilung; Ivora möchte nur zusammenfassende Übersichten auf hoher Ebene. Kompromiss: zwei Ansichten, dieselbe zugrunde liegende Datenbasis."}
+{"id": "ent_020", "text": "Aria untersuchte den Zusammenhang zwischen Attention-Entropie und Reasoning-Faithfulness. Das Paper des Hollowmounts Institute aus dem letzten Jahr behauptete, sie seien positiv korreliert; die Replikation von Aerwyn Labs fand das Gegenteil. Habe eine Synthese erarbeitet, die argumentiert, dass beide Papers korrekt sind, aber für unterschiedliche Modellskalen. Muss das auf dem Crestmoor-Cluster verifizieren, bevor ich etwas behaupte."}
+{"id": "ent_021", "text": "Fenra skizzierte den Anhang des Aerwyn-Zyklus zur Sprache. Drei Familien: Wendish (die Wends sprechen sie), Drukic (die Drukar) und die ältere Salt Tongue, die nur die Priester der Hollowmounts zeremoniell verwenden. Verfolgte Lautverschiebungen zwischen Wendish und Salt Tongue. Der Benutzer möchte, dass Saela in Kapitel zwölf etwas Salt Tongue spricht."}
+{"id": "ent_022", "text": "Am Morgen führte Bramble eine Bestäuber-Erhebung im Botanischen Garten Wendelsea durch. Sieben Arten heimischer Bienen gezählt, darunter die seltene Crestmoor Mining Bee, die Yumara Felk anhand der Bänderung des Abdomens identifizierte. Wichtige Daten für die Baseline von Pollinator Paths. Yumara veröffentlicht die Erhebungsergebnisse zusammen mit dem Hollowmounts Phenology Network."}
+{"id": "ent_023", "text": "Solas half Mette Olafsen, der neuen SRE bei Crestmoor, dabei, Distributed Tracing für den Aerwyn Telemetry Stack einzurichten. Der schwierige Teil war, den OpenTelemetry Collector dazu zu bringen, die gemischten Rust und OCaml Workloads zu verarbeiten. Wir entschieden uns für OTLP HTTP Export, damit wir uns nicht mit gRPC Versionsabweichungen über die Services hinweg herumschlagen mussten."}
+{"id": "ent_024", "text": "Thresh arbeitete die Abschreibung für die neue Ausrüstung von Bridgewater Studio durch. Ivora Tinn drängte darauf, mit Section 179 die gesamten $180k im ersten Jahr sofort abzuschreiben. Er rechnete die Zahlen durch — sie haben nicht genug steuerpflichtiges Einkommen, um das ohne phase-out-Probleme vollständig abzuziehen. Er empfahl stattdessen bonus depreciation, da hier keine income limitation besteht. Sie stimmte zu."}
+{"id": "ent_025", "text": "Aria und die Benutzerin hatten ein langes Gespräch über das Visualisierungstool Embedding Spaces. Die Benutzerin, Saela, möchte einen Zeitschieberegler hinzufügen, damit sie abspielen kann, wie sich Cluster über den Indizierungszeitraum hinweg gebildet haben. API skizziert. Koordination mit Brennan Lyle beim Rendering — er hat Erfahrung mit WebGL aus seiner Zeit bei Bridgewater Visualization."}
+{"id": "ent_026", "text": "Fenra überlegte, wie sie den inneren Monolog für Hellis Mar handhaben könnte, ohne die im restlichen Aerwyn-Zyklus etablierte POV der dritten Person zu brechen. Drei Optionen. Sie entschied sich für einen Hybrid: tiefe Innerlichkeit in Dialogszenen, oberflächliche Beschreibung ansonsten. Die Wendelsea Writing Group könnte Feedback dazu geben, ob das zu holprig wirkt."}
+{"id": "ent_027", "text": "Bramble ging mit ihr das Boden-pH-Problem auf Doreth Ainsleighs Heidelbeerfeld durch. Eisenchlorose durch Verschiebung in den neutralen Bereich. Ihr Boden ist von Natur aus eher alkalisch — der Bezirk Wendelsea liegt auf Kalkstein-Grundgestein. Empfohlen wurden Schwefelgaben und die Verlagerung der neuen Pflanzungen in ein Hochbeet mit importiertem saurem Boden."}
+{"id": "ent_028", "text": "Solas hat Pol Krisats PR zur Refaktorierung der Type Systems-Fehlerberichterstattung begutachtet. Das neue Design verwendet spans-with-labels, ähnlich wie rustc, aber mit unseren row-polymorphism-Erweiterungen. Er schlug einige Verbesserungen an der Formatierung der Diagnostik vor. Bek Halloran (ein anderer Bek – dieser hier ist ein Mitwirkender, nicht der Auditor) meldete sich zu Wort und äußerte Bedenken bezüglich perf. Das meiste ist geklärt, aber ein Kommentar-Thread ist noch offen."}
+{"id": "ent_029", "text": "Threshs Quartalsabschluss für Tartine Lab. Die Cashflow Models prognostizierten einen Fehlbetrag in Woche 9, der nicht eingetreten ist, weil Hollowmounts Logistics vorzeitig zahlte. Das Modell ist nicht kaputt – es liegt nur daran, dass frühe Zahlungen einen dicken rechten Schwanz aufweisen. Verteilungsannahmen des Modells aktualisiert und erneut ausgeführt. Überarbeitete Prognose an Ivora Tinn gesendet."}
+{"id": "ent_030", "text": "Aria, der Benutzer möchte ein neues Projekt aufsetzen – nennen wir es Faithful Chains – für die Forschung zur Stabilität von chain-of-thought. Es unterscheidet sich deutlich genug von Meta-Cognition, um meiner Meinung nach einen eigenen Bereich zu rechtfertigen. Ich werde mich mit Ren Solanke abstimmen und wir werden die Publikation von Hollowmounts Institute als Ausgangspunkt zitieren. Finanzierung über den Zuschuss von Crestmoor Cognitive Science."}
+{"id": "ent_031", "text": "Fenras Tagebuch: Schreibtag. 2.200 Wörter für die Saela / Hellis Mar-Konfrontation in Kapitel elf geschafft. Schwierige Szene — die Salt Tongue-Gespräche mussten echt wirken, obwohl wir sie nicht auf der Seite übersetzen. Entwurf an die Wendelsea Writing Group-Leserin Karis Tornau geschickt (die neben ihrem Tagesjob Belletristik liest, praktisch)."}
+{"id": "ent_032", "text": "Bramble besuchte den Versuchsgarten von Hollowmounts Institute. Das Programm Native Species leistet dort bemerkenswerte Arbeit bei der Samenerhaltung für regionale Ökotypen. Ich traf mich mit der leitenden Gärtnerin von Hollowmounts Institute, Iset Karadzic — dieselbe Iset wie von der Baumschule, sie berät das Hollowmounts Institute ebenfalls. Sie gab mir ein Päckchen Samen der Kardinalblume aus lokaler Provenienz."}
+{"id": "ent_033", "text": "Solas half einem für das Projekt neuen Nutzer, den Pol Krisat vorgestellt hatte, bei der Einrichtung einer Entwicklungsumgebung für den Compiler von Aerwyn Labs. Der Nutzer arbeitete unter Windows und stieß dabei auf die üblichen Probleme bei der Kreuzkompilierung mit unserer LLVM-Toolchain. Solas verwies ihn auf das Wiki von Crestmoor Systems für die Windows-spezifischen Einrichtungsschritte. Nach etwa einer Stunde ließ sich das Projekt erfolgreich bauen."}
+{"id": "ent_034", "text": "Thresh und Ivora Tinn sprachen über die Cap Table von Bridgewater Studio. Sie führen einen kleinen Sekundärverkauf durch und müssen die Buchhaltung der vorherigen Runde aufräumen, bevor der nächste Investor (Aerwyn Capital) die Due Diligence durchführt. Einige Anteile wurden ohne ordnungsgemäße 83(b) elections ausgegeben. Ihr wurde empfohlen, ihren Wertpapieranwalt von Crestmoor Legal Group einzubeziehen, bevor weitere Schritte unternommen werden."}
+{"id": "ent_035", "text": "Aria verbrachte den Nachmittag mit einer Literaturübersicht zu kontrastiven Einbettungszielen. Die Nutzerin, Saela, wollte eine einseitige Zusammenfassung, die SimCLR, BYOL und Barlow Twins im Betrachtungsrahmen des spektralen Kerns verbindet. Ich habe Referenzen aus Tartine Lab und den Vorabveröffentlichungen von Aerwyn Labs herangezogen. Außerdem habe ich einen relevanten Artikel aus dem Jahr 2023 von Bridgewater Cognitive Science am Ende hinzugefügt."}
+{"id": "ent_036", "text": "Fenra diskutierte mit dem Benutzer die mythologischen Grundlagen des Aerwyn-Zyklus. Der Mythos der Mother of Salt hat Parallelen in der mündlichen Überlieferung von Hollowmounts. Recherchierte walisische und finnische mythologische Motive und synthetisierte eine Version, die sich in Aerwyn heimisch anfühlt und nicht entlehnt wirkt. Die Wendelsea Writing Group wird es nächste Woche lesen."}
+{"id": "ent_037", "text": "Bramble führte eine Untersuchung der Bodenmikroben im Gemeinschaftsgarten in Bridgewater durch. Der Kompost dort ist ausgezeichnet – hohe mikrobielle Diversität, ein gutes Verhältnis von Pilzen zu Bakterien für die Gemüsebeete. Karis Tornau möchte dies auf die Satellitengärten in Crestmoor und Wendelsea ausweiten. Der Pollinator Paths-Korridor, den wir letztes Jahr entworfen haben, funktioniert ebenfalls gut; die Bienendiversität ist um 40 % gestiegen."}
+{"id": "ent_038", "text": "Solas führte ein Threadsicherheits-Audit für die Distributed Tracing Client-Bibliothek durch. Fand zwei Race Conditions, die ThreadSanitizer nicht erfasst hatte — sie traten nur bei sehr hoher Nebenläufigkeit auf dem Crestmoor-Produktions-Stack auf. Mette Olafsen bestätigte, dass sie sie reproduzieren konnte. Puschte Korrekturen; beide von Brennan Lyle geprüft."}
+{"id": "ent_039", "text": "Thresh' Review zum Invoice Parsing mit Pell Halloran von Carag-aana Press. Deren AP stellt von PDF-Rechnungen auf ein strukturiertes ZUGFeRD-Format um. Wir haben unsere Invoice Parsing-Pipeline aktualisiert, um das eingebettete XML zu verarbeiten. Der Umstieg wird Carag-aana bei unseren Bearbeitungsgebühren etwa $4k pro Jahr sparen, was Pell zu schätzen wusste."}
+{"id": "ent_040", "text": "Aria hat Saelas Einleitungsentwurf für den Artikel zu Embedding Spaces begutachtet. Der Inhalt ist technisch stark, aber die Rahmung vergräbt die Kernaussage — der neue Beitrag sollte im zweiten Satz stehen, nicht auf der zweiten Seite. Sie empfahl eine strukturelle Neuordnung. Da Saela den Artikel nächste Woche bei der Zeitschrift von Tartine Lab einreicht, haben wir nur begrenzte Zeit für Überarbeitungen."}
+{"id": "ent_041", "text": "Fenra und der Benutzer kartierten die Geografie der Drukar-Gebiete. Die Drukar leben südlich der Hollowmounts, in einer Region namens Salt Flats. Ihre Hauptsiedlung, Vroth-Karadz, liegt am Rand eines trockenen Sees. Hellis Mar stammt aus einem kleineren Dorf, Krast-Endel, drei Tagesritte von der Hauptstadt entfernt."}
+{"id": "ent_042", "text": "Brambles Arbeit an Native Species mit Iset Karadzic im Wendelsea Native Plant Nursery zahlt sich aus. Die Produktion in Stecklingstrays ist in dieser Saison um 60 Prozent gestiegen, und sie beliefern Restaurierungsprojekte des Crestmoor Conservation Trust in der gesamten Region. Das Hollowmounts Institute bezieht jetzt auch Samen aus ihrer Ökotyp-Sammlung."}
+{"id": "ent_043", "text": "Solas verfasste eine Nachbetrachtung für den Distributed Tracing-Ausfall letzte Woche bei Crestmoor. Ursache: eine fehlerhafte Konfigurationsänderung, die den OTLP-Exporter für 47 Minuten deaktivierte. Mette Olafsen leitete die Reaktion; Brennan Lyle verfasste das Handbuch-Update. Hinzufügen eines Validierungsschritts für die Konfiguration zu CI zur Vermeidung einer Wiederholung. Die Nachbetrachtung wurde im Aerwyn Telemetry Vorfall-Wiki abgelegt."}
+{"id": "ent_044", "text": "Thresh half Doreth Ainsleigh von Wendelsea Orchard bei ihrer Schedule F. Sie hat sowohl Obstverkäufe als auch Einnahmen aus dem Agriturismus und war sich unsicher, wie sie Letzteres behandeln sollte. Er ging die relevanten IRS-Richtlinien mit ihr durch — Agriturismus ist im Allgemeinen eine separate Geschäftstätigkeit. Er empfahl ihr, sich vor der Einreichung mit ihrer CPA zu besprechen. Pell Halloran (keine Verwandtschaft, seltsam häufiger Name in Wendelsea) war die CPA, die sie schließlich beauftragte."}
+{"id": "ent_045", "text": "Aria untersuchte, ob sich die Erkenntnisse des Projekts Meta-Cognition auf multimodale Modelle übertragen lassen. Das Hollowmounts Institute experimentiert dazu mit ihrem internen visuell-sprachlichen Modell. Vereinbaren Sie ein Treffen mit Ren Solanke für nächste Woche, um die Ergebnisse abzugleichen. Die Gruppe für Kognitionswissenschaft in Bridgewater verfolgt ein paralleles Vorhaben, das zitierenswert ist."}
+{"id": "ent_046", "text": "Fenra stellte ein Glossar für die Aerwyn-Anhänge zusammen. Bislang dreiundzwanzig Begriffe, die von kulturellen Konzepten (Salzschuld, die Wendelsea-Bindung) bis zu geografischen Begriffen (die Hollowmounts, die Salt Flats, Bryn-iili, Vroth-Karadz) reichen. Die Beta-Leser der Wendelsea Writing Group berichteten, das Glossar habe ihnen geholfen, im zehnten Kapitel den Überblick zu behalten."}
+{"id": "ent_047", "text": "Die Bestäuber-Erhebung von Bramble mit Yumara Felk identifizierte eine neue Kolonie der Crestmoor Mining Bee im Bridgewater Community Garden. Bedeutsam — man ging davon aus, dass die Art auf den Bezirk Wendelsea beschränkt war. Hollowmounts Phenology Network wurde benachrichtigt, damit sie ihre Verbreitungskarten aktualisieren können. Karis Tornau freut sich über die Möglichkeit zum Freiwilligenengagement."}
+{"id": "ent_048", "text": "Solas arbeitete mit Pol Krisat an der row-polymorphism-Implementierung für Type Systems. Ihr Algorithmus behandelt den kind-checking-Grenzfall, den ich letzte Woche markiert hatte. Wir testeten ihn gegen das Aerwyn Labs-Corpus, und er akzeptiert jedes zuvor akzeptierte Programm sowie eine Handvoll, die vom alten Algorithmus abgelehnt wurden. Bek Halloran (der Mitwirkende, nicht der Prüfer) hat den diff begutachtet."}
+{"id": "ent_049", "text": "Thresh arbeitete die Due-Diligence-Fragen von Aerwyn Capital für Bridgewater Studio durch. Sie wollen saubere Cashflow Models für die letzten 24 Monate, geprüfte Finanzunterlagen (die liegen bei Wendelsea Audit Partners) und die Abstimmung der Beteiligungsübersicht, an der Ivora Tinn und ich gearbeitet haben. Knapper Zeitplan — sie wollen alles bis zum Monatsende. Koordiniert mit Bek Halloran vom Prüfungsteam."}
+{"id": "ent_050", "text": "Aria fasste die Forschung des Tages für Saela zusammen. Drei zentrale Erkenntnisse: Die Behauptungen im Paper des Tartine Lab zu chain-of-thought faithfulness lassen sich bei kleineren Modellen nicht sauber replizieren; die vision-language-Erweiterung des Hollowmounts Institute ist vielversprechend, aber unvollständig; und das Preprint der Aerwyn Labs zu attention entropy weist eine subtile methodische Schwäche auf, die gekennzeichnet werden muss. Entwarf ein Memo für die wöchentliche Sitzung von Crestmoor Cognitive Science."}

benchmarks/model_eval/datasets/entity_extraction/dataset.es.jsonl

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+{"id": "ent_001", "text": "Aria mencionó que el documento Tartine Lab sobre evaluación de recuperación aumentada finalmente está en arXiv. Saela lo estaba esperando: está ejecutando el proyecto Embedding Spaces y quiere citarlo en la próxima preimpresión de su grupo. El laboratorio está en Crestmoor, justo al final de la calle de Bridgewater Studio, donde Fenra solía realizar consultas. Aparentemente, el autor principal, Gera Vossen, pasará al próximo período Hollowmounts Institute."}
+{"id": "ent_002", "text": "Solas impulsó una solución para el error de propagación de seguimiento en el proyecto Distributed Tracing. Revisado por Brennan Lyle del equipo de ejecución en Crestmoor Systems. El error solo se manifestó bajo carga, razón por la cual la prueba de estrés del consorcio Aerwyn Telemetry lo detectó antes que nuestro CI interno."}
+{"id": "ent_003", "text": "Bramble recorrió el diseño del nuevo corredor Pollinator Paths para Wendelsea Botanic Garden. Su entomólogo, Yumara Felk, sugirió agregar menta de montaña a lo largo del borde este. El proyecto está financiado por Crestmoor Conservation Trust, que lleva dos décadas apoyando el trabajo con plantas nativas en la región."}
+{"id": "ent_004", "text": "Thresh cerró los libros de Bridgewater Studio. Su informe de antigüedad de AR marcó a Carag-aana Press como una cuenta de pago lento: 78 días pendientes. Nos comunicamos con Pell Halloran en su equipo de cuentas por pagar. La canalización Invoice Parsing enruta automáticamente tres de sus facturas a la cola de revisión manual porque las líneas de pedido no coincidían con la orden de compra del subcontrato Tartine Lab."}
+{"id": "ent_005", "text": "Fenra realizó el tercer acto del ciclo de Aerwyn con el usuario. El arco de Saela alcanza su punto de inflexión en Hollowmounts, y la gente de Wends son testigos críticos de su decisión. El usuario quiere establecer el clímax en Bryn-iili, lo cual creo que es una buena elección dado el motivo de la deuda de sal establecido en el capítulo cuatro."}
+{"id": "ent_006", "text": "La canalización Topic Clustering falló durante la indexación nocturna. Aria extrajo los registros y los rastreó hasta una pérdida de memoria en la biblioteca HDBSCAN, específicamente el backend de joblib en el clúster de computación Crestmoor. Presentó un problema con los mantenedores ascendentes, copió a Mette Olafsen, quien ha sido el contacto principal en Crestmoor IT."}
+{"id": "ent_007", "text": "Registro diario de Bramble: visita matutina a Bridgewater Community Garden para evaluar la situación del error de squash. Karis Tornau, el coordinador de voluntarios, me explicó su plan de manejo integrado de plagas. Tarde: ayudó a Ralf Ginder a diseñar un borde Native Species para su propiedad en el distrito Wendelsea. Tarde: lea un artículo de Hollowmounts Phenology Network sobre los turnos de primera floración."}
+{"id": "ent_008", "text": "Solas revisó un PR de Aerwyn Labs. La suya es una biblioteca combinadora de analizadores que se basa en Megaparsec, diseñada para el proyecto Type Systems. El mantenedor, Pol Krisat, fue muy receptivo. Necesito mencionarle esto al equipo de Crestmoor Compiler Group: estaban buscando algo similar."}
+{"id": "ent_009", "text": "Thresh trabajó en la adopción de las NIIF para Tartine Lab. Están haciendo la transición de los GAAP locales y el Cashflow Models que construimos el último trimestre debe ajustarse a la nueva contabilidad de arrendamiento según la NIIF 16. En coordinación con su director financiero Ivora Tinn y su auditor externo Bek Halloran de Wendelsea Audit Partners."}
+{"id": "ent_010", "text": "Aria preparó una lista de lectura para el nuevo postdoctorado, Ren Solanke, que se unirá al proyecto Meta-Cognition en Crestmoor. Comenzó con los artículos fundamentales de Bridgewater Cognitive Science, luego agregó la serie Hollowmounts Institute sobre indicaciones autorreferenciales y luego la reciente preimpresión Aerwyn Labs sobre estabilidad de la cadena de pensamiento."}
+{"id": "ent_011", "text": "Fenra y el usuario pasaron una hora en Dialogue Engine. El modelo actual de gráfico de relaciones no maneja un caso extremo específico: cuando dos personajes de facciones enfrentadas (el Wends y el Drukar) se ven obligados a cooperar. Se redactó una solución alternativa. La voz de Saela en particular necesita suavizarse cuando habla con Hellis Mar; hay una historia ahí que el motor no captura."}
+{"id": "ent_012", "text": "Bramble identificó un problema de hongos en las manzanas heredadas en Wendelsea Orchard. Costra de manzana, clásica. El administrador del huerto, Doreth Ainsleigh, no había estado realizando pulverizaciones de cobre latentes. Trabajé con ella en un plan de transición. Mencionó que Crestmoor Cooperative Extension organizará un taller el próximo mes y ambos estuvimos de acuerdo en que debería asistir."}
+{"id": "ent_013", "text": "Solas depuró un error de segmentación en el repositorio Type Systems que solo se activó en aarch64. Resultó que la solución involucraba elementos intrínsecos de LLVM que se comportaban de manera diferente entre los objetivos. Se presentó un problema ascendente con los mantenedores de LLVM; Brennan Lyle del Aerwyn Compiler Group se ofreció a dividirlo en dos. Compararemos notas en la reunión de sistemas Crestmoor el próximo jueves."}
+{"id": "ent_014", "text": "Thresh ayudó a Pell Halloran a conciliar una complicada coincidencia a tres bandas entre la orden de compra, el documento de recepción y la factura de Hollowmounts Logistics. La discrepancia resultó ser una discrepancia en las unidades de medida: la orden de compra estaba en los casos, la factura en cada uno. Se actualizaron las reglas Invoice Parsing para marcar automáticamente las discrepancias de UM en el futuro."}
+{"id": "ent_015", "text": "Aria revisó tres preguntas de pruebas estadísticas de Saela seguidas. El tercero fue el más complicado: si se debía utilizar una prueba emparejada o no para muestras coincidentes pero no idénticas. La guié a través de las implicaciones. Está preparando una revisión para la revista Tartine Lab y la fecha límite es el viernes."}
+{"id": "ent_016", "text": "Fenra trabajó en convenciones de nomenclatura para las personas Drukar del ciclo de Aerwyn. Reglas fonológicas redactadas. Hellis Mar, un personaje Drukar central en el capítulo ocho, es la excepción empática, y su nombre debe unir los sistemas de sonido Drukar y Wendish. Envió el documento de trabajo a Wendelsea Writing Group y copió a Saela, que tiene buen oído para los idiomas inventados, de su trabajo en Bridgewater Cognitive Science."}
+{"id": "ent_017", "text": "Consulta Pollinator Paths de Bramble con el comité de jardinería Bridgewater Schools. Quieren plantaciones exclusivamente nativas pero tienen un presupuesto estricto. Se recomienda comenzar con las bandejas de plugs de Wendelsea Native Plant Nursery: son más económicas que los contenedores de un galón y se establecen más rápido. Habló con su fundador, Iset Karadzic, quien acordó otorgarles a las escuelas su tarifa sin fines de lucro."}
+{"id": "ent_018", "text": "Solas se emparejó con Pol Krisat para integrar la nueva biblioteca combinadora de analizadores en el código base Aerwyn Labs. La integración fue limpia excepto por un lugar donde su tipo de error personalizado chocó con nuestro contexto Distributed Tracing. Se resuelve introduciendo un pequeño rasgo adaptador. Impulsamos la integración como un PR separado para facilitar la revisión."}
+{"id": "ent_019", "text": "Thresh participó en una reunión de planificación con el liderazgo de Bridgewater Studio. Su director financiero Ivora Tinn está implementando un nuevo panel Cashflow Models para el equipo ejecutivo. El director de operaciones Karis Tornau quiere desglosar por departamento; Ivora sólo quiere resúmenes de alto nivel. Compromiso: dos puntos de vista, los mismos datos subyacentes."}
+{"id": "ent_020", "text": "Aria exploró la relación entre la entropía de la atención y la fidelidad del razonamiento. El artículo Hollowmounts Institute del año pasado afirmó que estaban correlacionados positivamente; la replicación Aerwyn Labs encontró lo contrario. Se redactó una síntesis que sostiene que ambos artículos tienen razón, pero para diferentes escalas de modelos. Es necesario verificar esto en el clúster Crestmoor antes de reclamar algo."}
+{"id": "ent_021", "text": "Fenra describió el apéndice del ciclo de Aerwyn sobre lenguaje. Tres familias: Wendish (los Wends lo hablan), Drukic (los Drukar), y el más antiguo Salt Tongue, que sólo los sacerdotes de los Hollowmounts usan ceremonialmente. El sonido rastreado cambia entre Wendish y Salt Tongue. El usuario quiere que Saela hable algo de Salt Tongue en el capítulo doce."}
+{"id": "ent_022", "text": "La mañana de Bramble fue una encuesta de polinizadores en Wendelsea Botanic Garden. Se contaron siete especies de abejas nativas, incluida la rara abeja minera Crestmoor, que Yumara Felk identificó por las bandas abdominales. Datos importantes para la línea base Pollinator Paths. Yumara está publicando los resultados de la encuesta con Hollowmounts Phenology Network."}
+{"id": "ent_023", "text": "Solas ayudó a Mette Olafsen, el nuevo SRE en Crestmoor, a configurar Distributed Tracing para la pila Aerwyn Telemetry. La parte difícil fue lograr que el recopilador OpenTelemetry manejara las cargas de trabajo mixtas de Rust y OCaml. Decidimos utilizar la exportación HTTP de OTLP para no tener que lidiar con el sesgo de la versión de gRPC entre los servicios."}
+{"id": "ent_024", "text": "Thresh trabajó mediante la depreciación del nuevo equipo de Bridgewater Studio. Ivora Tinn estaba presionando para que la Sección 179 gastara la totalidad de los 180.000 dólares en el primer año. Hicimos los números: no tienen suficientes ingresos imponibles para absorberlos sin problemas de eliminación gradual. En su lugar, se recomienda una bonificación de depreciación, que no tiene limitación de ingresos. Ella estuvo de acuerdo."}
+{"id": "ent_025", "text": "Aria y el usuario tuvieron una larga conversación sobre la herramienta de visualización Embedding Spaces. El usuario, Saela, desea agregar un control deslizante temporal para poder reproducir cómo se formaron los grupos durante el período de indexación. Esbozó la API. Coordina con Brennan Lyle en el lado del renderizado; tiene experiencia con WebGL desde su época en Bridgewater Visualization."}
+{"id": "ent_026", "text": "Fenra pensó en cómo manejar el monólogo interno de Hellis Mar sin romper el punto de vista en tercera persona establecido en el resto del ciclo de Aerwyn. Tres opciones. Se decidió por un híbrido: interioridad profunda en las escenas de diálogo, descripción superficial en otros lugares. El Wendelsea Writing Group podría tener comentarios sobre si esto es demasiado discordante."}
+{"id": "ent_027", "text": "Bramble caminó con ella sobre el problema del pH del suelo en el huerto de arándanos de Doreth Ainsleigh. Clorosis férrica por deriva hacia neutral. Su suelo es naturalmente alcalino: el distrito Wendelsea está sobre un lecho de roca caliza. Se recomiendan aplicaciones de azufre y traslado de las nuevas plantaciones a un lecho elevado con suelo ácido importado."}
+{"id": "ent_028", "text": "Solas revisó el PR de Pol Krisat refactorizando el informe de errores de Type Systems. El nuevo diseño utiliza tramos con etiquetas, similar a Rustc pero con nuestras extensiones de polimorfismo de fila. Sugirió un par de mejoras al formato de diagnóstico. Bek Halloran (diferente Bek; este es un colaborador, no el auditor) intervino con una preocupación sobre el rendimiento. Casi resuelto, pero todavía hay un hilo de comentarios abierto."}
+{"id": "ent_029", "text": "Cierre trimestral de Thresh para Tartine Lab. El Cashflow Models predijo un déficit en la semana 9 que no se materializó porque Hollowmounts Logistics pagó anticipadamente. El modelo no está roto, es sólo que los pagos anticipados tienen una gran cola derecha. Se actualizaron los supuestos de distribución del modelo y se volvieron a ejecutar. Envió el pronóstico revisado a Ivora Tinn."}
+{"id": "ent_030", "text": "Aria, el usuario quiere poner en marcha un nuevo proyecto (llámelo Faithful Chains) para la investigación de la estabilidad de la cadena de pensamiento. Lo suficientemente distinto de Meta-Cognition que creo que merece su propio espacio. Se coordinará con Ren Solanke y citaremos el artículo Hollowmounts Institute como punto de partida. Financiamiento a través de la subvención Crestmoor Cognitive Science."}
+{"id": "ent_031", "text": "Registro diario de Fenra: día de escritura. Obtuve 2200 palabras sobre el enfrentamiento Saela / Hellis Mar en el capítulo once. Escena difícil: los intercambios Salt Tongue tenían que parecer reales aunque no los traduzcamos en la página. Envió el borrador al lector Wendelsea Writing Group, Karis Tornau (que lee ficción fuera de su trabajo diario, útil)."}
+{"id": "ent_032", "text": "Bramble visitó el jardín experimental Hollowmounts Institute. Su programa Native Species está realizando un trabajo extraordinario en materia de conservación de semillas para ecotipos regionales. Se reunió con su horticultor principal, Iset Karadzic, el mismo Iset que el vivero, y también consulta con el Instituto. Me dio un paquete de semillas de cardenal de procedencia local."}
+{"id": "ent_033", "text": "Solas ayudó a un usuario nuevo en el proyecto (Pol Krisat lo presentó) a configurar un entorno de desarrollo para el compilador Aerwyn Labs. Estaban en Windows y se encontraron con los problemas habituales de compilación cruzada con nuestra cadena de herramientas LLVM. Les indicó la wiki Crestmoor Systems para conocer los pasos de configuración específicos de Windows. Lo construyeron en aproximadamente una hora."}
+{"id": "ent_034", "text": "Thresh y Ivora Tinn discutieron la tabla de límites de Bridgewater Studio. Están haciendo una pequeña venta secundaria y necesitan limpiar la contabilidad de la ronda anterior antes de que el próximo inversor (Aerwyn Capital) haga la diligencia. Algunas acciones se emitieron sin las elecciones adecuadas del artículo 83(b). Le recomendó que contactara a su abogado de valores de Crestmoor Legal Group antes de cualquier otra acción."}
+{"id": "ent_035", "text": "Aria pasó la tarde revisando la literatura sobre objetivos de incrustación contrastiva. El usuario, Saela, quería un resumen de una página que conectara SimCLR, BYOL y Barlow Twins bajo el marco espectral del kernel. Se extrajeron referencias de Tartine Lab y las preimpresiones de Aerwyn Labs. Bridgewater Cognitive Science también tiene un documento relevante de 2023 que agregué al final."}
+{"id": "ent_036", "text": "Fenra discutió los fundamentos mitológicos del ciclo de Aerwyn con el usuario. El mito de la Madre de Sal tiene paralelos en la tradición oral Hollowmounts. Se investigaron motivos mitológicos galeses y finlandeses y se sintetizó una versión que parece nativa de Aerwyn en lugar de prestada. El Wendelsea Writing Group lo leerá la próxima semana."}
+{"id": "ent_037", "text": "Bramble realizó una evaluación de microbios del suelo en Bridgewater Community Garden. Su abono es excelente: alta diversidad microbiana, buena proporción de hongos y bacterias para los lechos de vegetales. Karis Tornau quiere ampliar esto a los jardines satélite en Crestmoor y Wendelsea. El corredor Pollinator Paths que diseñamos el año pasado también está funcionando bien, con un aumento de la diversidad de abejas del 40 %."}
+{"id": "ent_038", "text": "Solas trabajó en una auditoría de seguridad de subprocesos para la biblioteca cliente Distributed Tracing. Se encontraron dos condiciones de carrera que ThreadSanitizer no había detectado; solo se manifestaron con una concurrencia muy alta en la pila de producción Crestmoor. Mette Olafsen confirmó que podía reproducirse. Correcciones empujadas; ambos revisados ​​por Brennan Lyle."}
+{"id": "ent_039", "text": "Revisión del análisis de facturas de Thresh con Pell Halloran de Carag-aana Press. Su AP se está alejando de las facturas en PDF hacia un formato estructurado estilo ZUGFeRD. Se actualizó nuestra canalización Invoice Parsing para manejar el XML incrustado. La transición le ahorrará a Carag-aana alrededor de $4 mil al año en nuestras tarifas de procesamiento, lo cual Pell agradeció."}
+{"id": "ent_040", "text": "Aria revisó el borrador de introducción de Saela para el documento Embedding Spaces. Fuerte contenido técnico, pero el encuadre entierra la pista: la contribución novedosa debe estar en la segunda oración, no en la segunda página. Recomendó un reordenamiento estructural. Saela se enviará a la revista Tartine Lab la próxima semana, por lo que tenemos un tiempo de revisión limitado."}
+{"id": "ent_041", "text": "Fenra y el usuario trazaron la geografía de los territorios de Drukar. El Drukar vive al sur del Hollowmounts, en una región llamada Salt Flats. Su principal asentamiento, Vroth-Karadz, se encuentra al borde de un lago seco. Hellis Mar es de un pueblo más pequeño, Krast-Endel, a tres días de la capital."}
+{"id": "ent_042", "text": "El trabajo de Bramble con Native Species con Iset Karadzic en Wendelsea Native Plant Nursery está dando sus frutos. Su producción de bandejas de enchufes aumentó un 60 % esta temporada y se están enviando a proyectos de restauración Crestmoor Conservation Trust en toda la región. Hollowmounts Institute ahora también obtiene semillas de su colección de ecotipos."}
+{"id": "ent_043", "text": "Solas redactó una autopsia para la interrupción de Distributed Tracing de la semana pasada en Crestmoor. Causa raíz: una mala configuración que deshabilitó el exportador OTLP durante 47 minutos. Mette Olafsen lideró la respuesta; Brennan Lyle escribió la actualización del runbook. Agregar un paso de validación de configuración a CI para evitar que se repita. Archivó la autopsia en el wiki del incidente Aerwyn Telemetry."}
+{"id": "ent_044", "text": "Thresh ayudó a Doreth Ainsleigh de Wendelsea Orchard con su Programa F. Tiene ventas de frutas e ingresos por agroturismo y no estaba segura de cómo manejar estos últimos. Repasó las pautas pertinentes del IRS: el agroturismo es generalmente una actividad comercial separada. Se recomienda que hable con su contador público certificado antes de presentar la solicitud. Pell Halloran (sin relación, nombre extrañamente común en Wendelsea) fue el CPA que terminó usando."}
+{"id": "ent_045", "text": "Aria exploró si los hallazgos del proyecto Meta-Cognition se extenderían a los modelos multimodales. El Hollowmounts Institute ha estado realizando experimentos sobre esto con su modelo interno de visión y lenguaje. Programe una reunión con Ren Solanke para la próxima semana para comparar notas. El grupo Bridgewater Cognitive Science tiene un trabajo paralelo que vale la pena citar."}
+{"id": "ent_046", "text": "Fenra preparó un glosario para los apéndices de Aerwyn. Hasta el momento, veintitrés términos, que van desde conceptos culturales (deuda de sal, el enlace Wendelsea) hasta geográficos (el Hollowmounts, el Salt Flats, el Bryn-iili, el Vroth-Karadz). Los lectores beta de Wendelsea Writing Group informaron que el glosario les ayudó a realizar un seguimiento durante el capítulo diez."}
+{"id": "ent_047", "text": "La encuesta de polinizadores de Bramble con Yumara Felk identificó una nueva colonia de abeja minera Crestmoor en Bridgewater Community Garden. Importante: se pensaba que la especie estaba restringida al distrito Wendelsea. Notificó al Hollowmounts Phenology Network para que puedan actualizar sus mapas de alcance. Karis Tornau está entusiasmado con la oportunidad de participación voluntaria."}
+{"id": "ent_048", "text": "Solas emparejado con Pol Krisat en la implementación del polimorfismo de filas Type Systems. Su algoritmo maneja el caso límite de verificación de especie que señalé la semana pasada. Lo probé con el corpus Aerwyn Labs y acepta todos los programas aceptados previamente más algunos que fueron rechazados por el algoritmo anterior. Bek Halloran (el colaborador, no el auditor) revisó la diferencia."}
+{"id": "ent_049", "text": "Thresh repasó las preguntas de diligencia de Aerwyn Capital para Bridgewater Studio. Quieren Cashflow Models limpio durante los últimos 24 meses, finanzas auditadas (Wendelsea Audit Partners las tiene) y la conciliación de la tabla de límites en la que Ivora Tinn y yo hemos estado trabajando. Calendario ajustado: lo quieren todo para fin de mes. Coordinado con Bek Halloran del equipo de auditoría."}
+{"id": "ent_050", "text": "Aria resumió la investigación del día para Saela. Tres hallazgos clave: las afirmaciones del artículo Tartine Lab sobre la fidelidad de la cadena de pensamiento no se replican claramente en modelos más pequeños; la extensión de lenguaje-visión del Hollowmounts Institute es prometedora pero incompleta; y la preimpresión Aerwyn Labs sobre entropía de atención tiene un problema metodológico sutil que debe señalarse. Redacté un memorándum para el semanario Crestmoor Cognitive Science."}

benchmarks/model_eval/datasets/entity_extraction/dataset.fr.jsonl

@@ -0,0 +1,50 @@
+{"id": "ent_001", "text": "Aria a mentionné que l'article de Tartine Lab sur l'évaluation augmentée par récupération est enfin sur arXiv. Saela l'attendait — elle dirige le projet Embedding Spaces et souhaite le citer dans le prochain préprint de son groupe. Le laboratoire est à Crestmoor, juste à côté de Bridgewater Studio où Fenra travaillait comme consultant. Apparemment, l'auteur principal, Gera Vossen, rejoint l'Hollowmounts Institute le semestre prochain."}
+{"id": "ent_002", "text": "Solas a poussé un correctif pour le bug de propagation de trace dans le projet Distributed Tracing. Révisé par Brennan Lyle de l'équipe runtime chez Crestmoor Systems. Le bug ne se manifestait que sous charge, c'est pourquoi le test de stress du consortium Aerwyn Telemetry l'a détecté avant notre CI interne."}
+{"id": "ent_003", "text": "Bramble a détaillé la conception du nouveau corridor Pollinator Paths pour Wendelsea Botanic Garden. L'entomologiste de Wendelsea Botanic Garden, Yumara Felk, a suggéré d'ajouter de la menthe des montagnes le long du bord est. Le projet est financé par Crestmoor Conservation Trust, qui soutient les travaux sur les plantes indigènes dans la région depuis deux décennies."}
+{"id": "ent_004", "text": "Thresh a clôturé les livres pour Bridgewater Studio. Leur rapport de vieillissement AR a signalé Carag-aana Press comme un compte à paiement lent — 78 jours d'ancienneté. Il a contacté Pell Halloran au sein de leur équipe des comptes fournisseurs. Le pipeline Invoice Parsing a automatiquement routé trois de leurs factures vers la file d'attente de révision manuelle, car les lignes de facture ne correspondaient pas au PO du sous-contrat Tartine Lab."}
+{"id": "ent_005", "text": "Fenra a fait un atelier avec l'utilisateur sur le troisième acte du cycle Aerwyn. L'arc de Saela atteint son point tournant dans les Hollowmounts, et les Wends sont des témoins essentiels de sa décision. L'utilisateur veut placer le point culminant à Bryn-iili, ce qui me semble être un choix solide compte tenu du motif de la dette de sel établi au chapitre quatre."}
+{"id": "ent_006", "text": "Le pipeline Topic Clustering a planté pendant l'indexation nocturne. Aria a récupéré les journaux et a remonté le problème jusqu'à une fuite de mémoire dans la bibliothèque HDBSCAN — plus précisément le moteur joblib sur le cluster de calcul Crestmoor. Elle a ouvert un ticket auprès des mainteneurs en amont, en mettant Mette Olafsen en copie, qui est le contact principal chez Crestmoor IT."}
+{"id": "ent_007", "text": "Journal quotidien de Bramble : visite matinale à Bridgewater Community Garden pour évaluer la situation des punaises des cucurbitacées. Karis Tornau, responsable des bénévoles, m'a présenté leur plan de lutte intégrée contre les ravageurs. Après-midi : aidé Ralf Ginder à concevoir une bordure Native Species pour sa propriété située dans le district Wendelsea. Soirée : lecture d'un article du Hollowmounts Phenology Network sur les décalages de première floraison."}
+{"id": "ent_008", "text": "Solas a relu une PR d'Aerwyn Labs. Il s'agit d'une bibliothèque de parser combinator qui s'appuie sur Megaparsec, conçue pour le projet Type Systems. Le mainteneur, Pol Krisat, a été très réactif. Il faut en parler à l'équipe du Crestmoor Compiler Group — ils cherchaient quelque chose de similaire."}
+{"id": "ent_009", "text": "Thresh a travaillé sur l'adoption d'IFRS pour Tartine Lab. Elle passe de la GAAP locale et les Cashflow Models que nous avons construits le trimestre dernier doivent être ajustés pour la nouvelle comptabilisation des baux selon IFRS 16. Il coordonne avec leur CFO Ivora Tinn et son auditeur externe Bek Halloran de Wendelsea Audit Partners."}
+{"id": "ent_010", "text": "Aria a préparé une liste de lecture pour le nouveau post-doctorant, Ren Solanke, qui rejoint le projet Meta-Cognition à Crestmoor. Elle a commencé par les articles fondamentaux de Bridgewater Cognitive Science, puis a ajouté la série de l'Hollowmounts Institute sur l'incitation autoréférentielle, et enfin la prépublication récente d'Aerwyn Labs sur la stabilité du chain-of-thought."}
+{"id": "ent_011", "text": "Fenra et l'utilisateur ont passé une heure sur le Dialogue Engine. Le modèle de graphe de relations actuel ne gère pas un cas limite spécifique : quand deux personnages de factions ennemies (les Wends et les Drukar) sont forcés à coopérer. Une solution de contournement a été esquissée. La voix de Saela en particulier doit s'adoucir quand elle parle à Hellis Mar — il y a de l'histoire là-bas que le moteur ne capture pas."}
+{"id": "ent_012", "text": "Bramble a identifié un problème fongique sur les pommes anciennes du verger Wendelsea. De la tavelure, classique. La responsable du verger, Doreth Ainsleigh, n'effectuait pas les pulvérisations de cuivre en période de dormance. Nous avons élaboré un plan de transition avec elle. Elle a mentionné que Crestmoor Cooperative Extension organise un atelier le mois prochain et nous avons convenu qu'elle devrait y assister."}
+{"id": "ent_013", "text": "Solas a débogué un segfault dans le repo Type Systems qui ne se déclenchait que sur aarch64. Il s'est avéré que le correctif impliquait des intrinsics LLVM se comportant différemment selon les cibles. Un ticket upstream a été ouvert auprès des mainteneurs LLVM ; Brennan Lyle, de l'Aerwyn Compiler Group, a proposé de faire un bisect de son côté. Nous comparerons nos notes lors du meetup de Crestmoor Systems jeudi prochain."}
+{"id": "ent_014", "text": "Thresh a aidé Pell Halloran à réconcilier un rapprochement compliqué en trois parties entre le PO, le bon de réception et la facture de Hollowmounts Logistics. L'écart s'est avéré être une incompatibilité d'unité de mesure — le PO était en caisses, la facture en unités. Mise à jour des règles Invoice Parsing pour signaler automatiquement les incompatibilités d'UoM à l'avenir."}
+{"id": "ent_015", "text": "Aria a revu trois questions de tests statistiques de Saela d'affilée. La troisième était la plus délicate : s'il fallait utiliser un test apparié ou non apparié pour des échantillons appariés mais non identiques. Elle lui a expliqué les implications. Elle prépare une révision pour le journal de Tartine Lab et la date limite est vendredi."}
+{"id": "ent_016", "text": "Fenra a travaillé sur les conventions de nommage pour le peuple Drukar du cycle d'Aerwyn. A rédigé des règles phonologiques. Hellis Mar — un personnage Drukar central du chapitre huit — est l'exception empathique, et son nom doit faire le pont entre les systèmes phonétiques Drukar et Wendish. A envoyé le document de travail au Wendelsea Writing Group, et a mis Saela en copie, qui a l'oreille fine pour les langues inventées grâce à son travail chez Bridgewater Cognitive Science."}
+{"id": "ent_017", "text": "Consultation de Bramble pour Pollinator Paths avec le comité de jardinage des Bridgewater Schools. Ils souhaitent des plantes exclusivement indigènes mais disposent d'un budget serré. Recommandation de commencer par les plateaux de plants de Wendelsea Native Plant Nursery — moins chers que les contenants d'un gallon et ils s'établissent plus rapidement. Discussion avec leur fondatrice, Iset Karadzic, qui a accepté d'accorder aux écoles leur tarif à but non lucratif."}
+{"id": "ent_018", "text": "Solas s'est associé à Pol Krisat pour intégrer la nouvelle parser combinator library dans la base de code d'Aerwyn Labs. L'intégration était propre, sauf en un endroit où leur type d'erreur personnalisé entrait en conflit avec notre contexte Distributed Tracing. Résolu en introduisant un petit adapter trait. L'intégration a été poussée dans une PR distincte pour faciliter la révision."}
+{"id": "ent_019", "text": "Thresh a assisté à une réunion de planification avec la direction de Bridgewater Studio. Leur CFO Ivora Tinn déploie un nouveau tableau de bord Cashflow Models pour l'équipe de direction. Le directeur des opérations Karis Tornau souhaite un drill-down par département ; Ivora veut uniquement des synthèses de haut niveau. Compromis : deux vues, mêmes données sous-jacentes."}
+{"id": "ent_020", "text": "Aria a exploré la relation entre l'entropie de l'attention et la fidélité du raisonnement. L'article de l'Hollowmounts Institute de l'année dernière affirmait qu'elles étaient positivement corrélées ; la réplication d'Aerwyn Labs a trouvé le contraire. Rédigé une synthèse qui soutient que les deux articles ont raison mais à des échelles de modèles différentes. Besoin de vérifier cela sur le cluster Crestmoor avant d'affirmer quoi que ce soit."}
+{"id": "ent_021", "text": "Fenra a esquissé l'appendice du cycle d'Aerwyn sur la langue. Trois familles : le Wendish (les Wends le parlent), le Drukic (les Drukar) et le plus ancien Salt Tongue, que seuls les prêtres des Hollowmounts utilisent à des fins cérémonielles. A tracé les changements phonétiques entre le Wendish et le Salt Tongue. L'utilisateur veut que Saela parle un peu de Salt Tongue au chapitre douze."}
+{"id": "ent_022", "text": "Bramble a consacré sa matinée à un relevé des pollinisateurs au Wendelsea Botanic Garden. Elle a dénombré sept espèces d'abeilles indigènes, dont la rare Crestmoor Mining Bee, que Yumara Felk a identifiée d'après les bandes de l'abdomen. Des données importantes pour la ligne de base de Pollinator Paths. Yumara va publier les résultats du relevé avec le Hollowmounts Phenology Network."}
+{"id": "ent_023", "text": "Solas a aidé Mette Olafsen, la nouvelle SRE chez Crestmoor, à mettre en place Distributed Tracing pour la stack Aerwyn Telemetry. La partie difficile a été de faire en sorte que le collecteur OpenTelemetry gère les charges de travail mixtes Rust et OCaml. Nous avons opté pour l'export OTLP HTTP afin de ne pas avoir à gérer le décalage de version gRPC entre les services."}
+{"id": "ent_024", "text": "Thresh a travaillé sur l'amortissement du nouvel équipement de Bridgewater Studio. Ivora Tinn faisait pression pour utiliser la Section 179 et passer l'intégralité des $180k en charge la première année. Calculs faits — ils n'ont pas suffisamment de résultat imposable pour l'absorber sans problèmes de phase-out. J'ai plutôt recommandé le bonus depreciation, qui n'a pas cette limitation de revenu. Elle a accepté."}
+{"id": "ent_025", "text": "Aria et l'utilisateur ont eu une longue conversation à propos de l'outil de visualisation Embedding Spaces. L'utilisatrice, Saela, souhaite ajouter un curseur temporel pour pouvoir rejouer la formation des grappes au cours de la période d'indexation. Esquisse de l'API. Coordination avec Brennan Lyle du côté rendu — il a de l'expérience avec WebGL de son temps chez Bridgewater Visualization."}
+{"id": "ent_026", "text": "Fenra a réfléchi à la manière de gérer le monologue intérieur de Hellis Mar sans rompre le third-person POV établi dans le reste du cycle d'Aerwyn. Trois options. A opté pour un hybride : intériorité profonde dans les scènes de dialogue, description de surface ailleurs. Le Wendelsea Writing Group pourrait donner son avis sur la question de savoir si cela est trop brusque."}
+{"id": "ent_027", "text": "Bramble a examiné le problème de pH du sol avec elle sur le myrtillier de Doreth Ainsleigh. Chlorose ferrique due à une dérive vers le neutre. Son sol est naturellement plutôt alcalin — le district de Wendelsea repose sur un socle calcaire. Recommandation d'apports de soufre et de déplacer les nouvelles plantations sur une butte surélevée avec du sol acide importé."}
+{"id": "ent_028", "text": "Solas a examiné la PR de Pol Krisat qui refactorise le signalement des erreurs de Type Systems. La nouvelle conception utilise des spans-with-labels, sur le modèle de rustc mais avec nos extensions de row-polymorphism. Solas a suggéré quelques améliorations à la mise en forme des diagnostics. Bek Halloran (un Bek différent — celui-ci est un contributeur, pas l'auditeur) a soulevé un souci de perf. L'essentiel est résolu, mais un fil de commentaires est encore ouvert."}
+{"id": "ent_029", "text": "Clôture trimestrielle de Thresh pour Tartine Lab. Les Cashflow Models ont prédit un déficit en semaine 9 qui ne s'est pas matérialisé, car Hollowmounts Logistics a payé en avance. Le modèle n'est pas défectueux — c'est simplement que les paiements anticipés ont une queue de droite épaisse. Mise à jour des hypothèses de distribution du modèle et réexécution. Envoi de la prévision révisée à Ivora Tinn."}
+{"id": "ent_030", "text": "Aria, l'utilisateur souhaite lancer un nouveau projet — appelons-le Faithful Chains — pour la recherche sur la stabilité de la chaîne de pensée. Suffisamment distinct de Meta-Cognition, à mon avis, pour justifier son propre espace. Je vais coordonner avec Ren Solanke et nous citerons l'article de Hollowmounts Institute comme point de départ. Financement via la bourse en sciences cognitives de Crestmoor."}
+{"id": "ent_031", "text": "Journal quotidien de Fenra : journée d'écriture. 2 200 mots sur la confrontation entre Saela et Hellis Mar au chapitre onze. Scène difficile — les échanges en Salt Tongue devaient sembler réels même si nous ne les traduisons pas sur la page. Envoi du brouillon à la lectrice du Wendelsea Writing Group, Karis Tornau (qui lit de la fiction en dehors de son travail, utile)."}
+{"id": "ent_032", "text": "Bramble a visité le jardin expérimental de l'Hollowmounts Institute. Leur programme Native Species fait un travail remarquable dans la conservation de semences pour les écotypes régionaux. J'ai rencontré leur horticulturiste en chef, Iset Karadzic — la même Iset que celle de la pépinière, elle est aussi consultante pour l'Institut. Elle m'a donné un sachet de graines de fleur cardinale de provenance locale."}
+{"id": "ent_033", "text": "Solas a aidé un nouvel utilisateur du projet — présenté par Pol Krisat — à configurer un environnement de développement pour le compilateur Aerwyn Labs. Cet utilisateur était sur Windows et a rencontré les problèmes habituels de compilation croisée avec notre chaîne d'outils LLVM. Solas lui a indiqué le wiki de Crestmoor Systems pour les étapes de configuration spécifiques à Windows. Il a réussi à compiler le projet en environ une heure."}
+{"id": "ent_034", "text": "Thresh et Ivora Tinn ont discuté de la cap table de Bridgewater Studio. Ils effectuent une petite vente secondaire et doivent mettre au propre la comptabilité du tour précédent avant que le prochain investisseur (Aerwyn Capital) ne réalise sa due diligence. Certaines actions ont été émises sans les élections 83(b) appropriées. Il lui a été recommandé de consulter leur avocat en valeurs mobilières de Crestmoor Legal Group avant d'aller plus loin."}
+{"id": "ent_035", "text": "Aria a consacré l'après-midi à une revue de littérature sur les objectifs de plongement contrastif. L'utilisatrice, Saela, souhaitait un résumé d'une page reliant SimCLR, BYOL et Barlow Twins dans le cadre du noyau spectral. Elle a puisé des références dans les prépublications de Tartine Lab et d'Aerwyn Labs. J'ai également ajouté en fin de document un article pertinent de 2023 de Bridgewater Cognitive Science."}
+{"id": "ent_036", "text": "Fenra a discuté des fondements mythologiques du cycle d'Aerwyn avec l'utilisateur. Le mythe de Mother of Salt a des parallèles dans la tradition orale de Hollowmounts. J'ai étudié des motifs mythologiques gallois et finnois et synthétisé une version qui semble originaire d'Aerwyn plutôt qu'empruntée. Le Wendelsea Writing Group le lira la semaine prochaine."}
+{"id": "ent_037", "text": "Bramble a réalisé une évaluation des microbes du sol au jardin communautaire de Bridgewater. Leur compost est excellent — diversité microbienne élevée, bon ratio fongique/bactérien pour les plates-bandes potagères. Karis Tornau souhaite étendre cette initiative aux jardins satellites de Crestmoor et Wendelsea. Le corridor Pollinator Paths que nous avons conçu l'année dernière donne également de bons résultats, avec une augmentation de 40 % de la diversité des abeilles."}
+{"id": "ent_038", "text": "Solas a effectué un audit de sûreté des threads pour la bibliothèque client de Distributed Tracing. Solas a découvert deux conditions de course que ThreadSanitizer n'avait pas détectées — elles ne se manifestaient qu'à très forte concurrence sur la pile de production de Crestmoor. Mette Olafsen a confirmé pouvoir reproduire. Solas a poussé les correctifs ; tous deux ont été revus par Brennan Lyle."}
+{"id": "ent_039", "text": "Revue de l'analyse de factures de Thresh avec Pell Halloran de Carag-aana Press. Leur AP abandonne les factures PDF au profit d'un format structuré de type ZUGFeRD. Mise à jour de notre pipeline Invoice Parsing pour gérer le XML intégré. La transition permettra à Carag-aana d'économiser environ 4 000 $ par an sur nos frais de traitement, ce que Pell a apprécié."}
+{"id": "ent_040", "text": "Aria a relu le brouillon d'introduction de Saela pour l'article Embedding Spaces. Le contenu technique est solide, mais le cadrage masque l'essentiel — la contribution novatrice devrait figurer dans la deuxième phrase, et non à la deuxième page. Elle a recommandé une réorganisation structurelle. Saela soumettra son article à la revue Tartine Lab la semaine prochaine, nous avons donc peu de temps pour les révisions."}
+{"id": "ent_041", "text": "Fenra et l'utilisateur ont tracé la géographie des territoires Drukar. Les Drukar vivent au sud des Hollowmounts, dans une région appelée les Salt Flats. Leur principal établissement, Vroth-Karadz, se trouve au bord d'un lac asséché. Hellis Mar est originaire d'un village plus petit, Krast-Endel, à trois jours de chevauchée de la capitale."}
+{"id": "ent_042", "text": "Le travail Native Species de Bramble avec Iset Karadzic à Wendelsea Native Plant Nursery porte ses fruits. Leur production en plateaux alvéolés a augmenté de 60 % cette saison et ils approvisionnent des projets de restauration de Crestmoor Conservation Trust à travers la région. Le Hollowmounts Institute s'approvisionne maintenant aussi en semences auprès de sa collection d'écotypes."}
+{"id": "ent_043", "text": "Solas a rédigé un post-mortem pour la panne de Distributed Tracing de la semaine dernière à Crestmoor. Cause racine : un déploiement de configuration erroné qui a désactivé l'exportateur OTLP pendant 47 minutes. Mette Olafsen a piloté la réponse ; Brennan Lyle a rédigé la mise à jour de la procédure opérationnelle. Ajout d'une étape de validation de configuration au CI pour éviter une récurrence. Post-mortem déposé dans le wiki des incidents d'Aerwyn Telemetry."}
+{"id": "ent_044", "text": "Thresh a aidé Doreth Ainsleigh de Wendelsea Orchard avec son Schedule F. Elle a des revenus provenant à la fois de la vente de fruits et de l'agritourisme et ne savait pas comment traiter ce dernier. Il lui a expliqué les directives pertinentes de l'IRS — l'agritourisme est généralement une activité commerciale distincte. Il lui a recommandé d'en discuter avec son CPA avant de déposer sa déclaration. Pell Halloran (aucun lien de parenté, nom étrangement courant à Wendelsea) était le CPA qu'elle a fini par utiliser."}
+{"id": "ent_045", "text": "Aria a exploré dans quelle mesure les résultats du projet Meta-Cognition s'étendaient aux modèles multimodaux. L'Hollowmounts Institute mène des expériences sur ce point avec son modèle de langage-vision interne. Organisez une réunion avec Ren Solanke pour la semaine prochaine afin de comparer vos notes. Le Bridgewater Cognitive Science group mène un travail parallèle qui mérite d'être cité."}
+{"id": "ent_046", "text": "Fenra a constitué un glossaire pour les appendices d'Aerwyn. Vingt-trois termes à ce jour, allant de concepts culturels (salt-debt, the Wendelsea binding) à des termes géographiques (the Hollowmounts, the Salt Flats, Bryn-iili, Vroth-Karadz). Les bêta-lecteurs du Wendelsea Writing Group ont signalé que le glossaire les avait aidés à s'y retrouver pendant le chapitre dix."}
+{"id": "ent_047", "text": "Le relevé des pollinisateurs de Bramble avec Yumara Felk a identifié une nouvelle colonie de la Crestmoor Mining Bee dans le jardin communautaire de Bridgewater. C'est important — l'espèce était considérée comme restreinte au district de Wendelsea. J'ai informé le Hollowmounts Phenology Network pour qu'il puisse mettre à jour ses cartes de répartition. Karis Tornau est enthousiaste à l'idée de l'opportunité d'engagement bénévole."}
+{"id": "ent_048", "text": "Solas a fait équipe avec Pol Krisat sur l'implémentation de row-polymorphism de Type Systems. Leur algorithme gère le cas limite de kind-checking que j'avais signalé la semaine dernière. Testé sur le corpus d'Aerwyn Labs, il accepte tous les programmes précédemment acceptés plus une poignée de ceux qui étaient rejetés par l'ancien algorithme. Bek Halloran (le contributeur, pas l'auditeur) a relu le diff."}
+{"id": "ent_049", "text": "Thresh a passé en revue les questions de due diligence d'Aerwyn Capital pour Bridgewater Studio. Ils veulent des Cashflow Models propres pour les 24 derniers mois, des états financiers audités (Wendelsea Audit Partners les a), et la réconciliation de la table de capitalisation sur laquelle Ivora Tinn et moi avons travaillé. Délais serrés — ils veulent tout d'ici la fin du mois. J'ai coordonné avec Bek Halloran de l'équipe d'audit."}
+{"id": "ent_050", "text": "Aria a résumé les recherches de la journée pour Saela. Trois constats clés : les affirmations de l'article de Tartine Lab concernant la fidélité de la chaîne de pensée ne se répliquent pas proprement sur des modèles plus petits ; l'extension vision-langage de Hollowmounts Institute est prometteuse mais incomplète ; et la prépublication d'Aerwyn Labs sur l'entropie de l'attention présente un problème méthodologique subtil qu'il convient de signaler. Elle a rédigé une note pour la revue hebdomadaire de Crestmoor Cognitive Science."}

benchmarks/model_eval/datasets/entity_extraction/dataset.hi.jsonl

@@ -0,0 +1,50 @@
+{"id": "ent_001", "text": "Aria ने बताया कि Tartine Lab का पेपर retrieval-augmented evaluation पर आख़िरकार arXiv पर आ गया है। Saela इसका इंतज़ार कर रही थीं — वह Embedding Spaces प्रोजेक्ट चला रही हैं और इसे अपने समूह की अगली प्रीप्रिंट में उद्धृत करना चाहती हैं। लैब Crestmoor में है, Bridgewater Studio से सड़क पर थोड़ी दूरी पर, जहाँ Fenra पहले परामर्श देती थीं। ज़ाहिर है, मुख्य लेखक Gera Vossen अगले सत्र में Hollowmounts Institute जा रहे हैं।"}
+{"id": "ent_002", "text": "Solas ने Distributed Tracing प्रोजेक्ट में trace propagation बग के लिए एक सुधार पुश किया। इसकी समीक्षा Crestmoor Systems की रनटाइम टीम से Brennan Lyle द्वारा की गई। यह बग केवल लोड में ही प्रकट हुआ, इसीलिए Aerwyn Telemetry consortium के स्ट्रेस टेस्ट ने इसे हमारे आंतरिक CI से पहले पकड़ लिया।"}
+{"id": "ent_003", "text": "Bramble ने Wendelsea Botanic Garden के लिए नए Pollinator Paths गलियारे के डिज़ाइन का विस्तृत अवलोकन किया। उनके कीटविज्ञानी कर्मचारी Yumara Felk ने पूर्वी किनारे पर पहाड़ी पुदीना जोड़ने का सुझाव दिया। यह परियोजना Crestmoor Conservation Trust द्वारा वित्तपोषित है, जो पिछले दो दशकों से इस क्षेत्र में देशी पौधों के कार्य का समर्थन कर रहा है।"}
+{"id": "ent_004", "text": "Thresh ने Bridgewater Studio के खाते बंद कर दिए। उनकी AR aging report ने Carag-aana Press को एक धीमी भुगतान वाले खाते के रूप में चिह्नित किया — 78 दिनों से बकाया। उनकी accounts payable टीम में Pell Halloran से संपर्क किया गया। Invoice Parsing pipeline ने उनके तीन चालानों को स्वचालित रूप से manual review queue में भेज दिया क्योंकि line items Tartine Lab के उप-अनुबंध के PO से मेल नहीं खा रहे थे।"}
+{"id": "ent_005", "text": "Fenra ने उपयोगकर्ता के साथ Aerwyn चक्र के तीसरे अंक पर कार्यशाला की। Saela की कथा Hollowmounts में अपने मोड़ पर पहुँचती है, और Wends लोग उसके निर्णय के महत्वपूर्ण साक्षी हैं। उपयोगकर्ता Bryn-iili में चरमोत्कर्ष स्थापित करना चाहता है, जो मुझे लगता है कि अध्याय चार में स्थापित नमक-ऋण विषयवस्तु को देखते हुए एक मज़बूत विकल्प है।"}
+{"id": "ent_006", "text": "Topic Clustering पाइपलाइन रात्रिकालीन अनुक्रमण के दौरान क्रैश हो गई। Aria ने लॉग्स निकाले और इसे HDBSCAN लाइब्रेरी में एक मेमोरी लीक तक ट्रेस किया — विशेष रूप से Crestmoor कंप्यूट क्लस्टर पर joblib बैकएंड। अपस्ट्रीम मेंटेनर्स के साथ एक इश्यू दर्ज किया, Mette Olafsen को कॉपी किया जो Crestmoor IT में मुख्य संपर्क व्यक्ति रही हैं।"}
+{"id": "ent_007", "text": "Bramble का दैनिक लॉग: Bridgewater सामुदायिक बगीचे में सुबह का दौरा squash bug की स्थिति का आकलन करने के लिए। Karis Tornau, स्वयंसेवक समन्वयक, ने मुझे उनकी एकीकृत कीट प्रबंधन योजना के बारे में विस्तार से समझाया। दोपहर: Wendelsea जिले में Ralf Ginder की संपत्ति के लिए Native Species बॉर्डर डिज़ाइन करने में उनकी मदद की। शाम: Hollowmounts Phenology Network के एक पत्र को पढ़ा जो पहले फूल खिलने के समय में बदलावों पर था।"}
+{"id": "ent_008", "text": "Solas ने Aerwyn Labs की एक PR की समीक्षा की। यह Megaparsec पर आधारित एक parser combinator लाइब्रेरी है, जिसे Type Systems प्रोजेक्ट के लिए डिज़ाइन किया गया है। मेंटेनर, Pol Krisat, बहुत तेज़ी से जवाब दे रहे थे। Crestmoor Compiler Group की टीम को इसका उल्लेख करना होगा — वे कुछ इसी तरह की चीज़ खोज रहे थे।"}
+{"id": "ent_009", "text": "Thresh ने Tartine Lab के लिए IFRS लागू करने पर काम किया। वे स्थानीय GAAP से संक्रमण कर रहे हैं और पिछली तिमाही में हमारे द्वारा बनाए गए Cashflow Models को IFRS 16 के तहत नए पट्टा लेखांकन के अनुरूप समायोजित करने की आवश्यकता है। उनके CFO Ivora Tinn और Wendelsea Audit Partners से उनके बाहरी ऑडिटर Bek Halloran के साथ समन्वय किया जा रहा है।"}
+{"id": "ent_010", "text": "Aria ने नए पोस्टडॉक्टरल शोधकर्ता Ren Solanke के लिए एक पढ़ने की सूची तैयार की, जो Crestmoor में Meta-Cognition परियोजना से जुड़ रहे हैं। Bridgewater Cognitive Science के मौलिक पत्रों से शुरुआत की, फिर Hollowmounts Institute की self-referential prompting पर श्रृंखला जोड़ी, और फिर Aerwyn Labs की chain-of-thought stability पर हालिया पूर्व-प्रकाशन।"}
+{"id": "ent_011", "text": "Fenra और उपयोगकर्ता ने Dialogue Engine पर एक घंटा बिताया। मौजूदा relationship-graph model एक विशिष्ट edge case को नहीं संभालता: जब एक-दूसरे से दुश्मनी रखने वाले गुटों (the Wends और the Drukar) के दो पात्रों को सहयोग के लिए मजबूर किया जाता है। एक workaround का मसौदा तैयार किया गया। विशेष रूप से Saela की आवाज़ को नरम करने की ज़रूरत है जब वह Hellis Mar से बात करती है — वहाँ एक इतिहास है जिसे engine नहीं पकड़ पाता।"}
+{"id": "ent_012", "text": "Bramble ने Wendelsea Orchard में विरासती सेबों पर एक फंगल समस्या की पहचान की। सेब का छाई रोग, बिल्कुल क्लासिक। बाग के प्रबंधक, Doreth Ainsleigh, सुप्तावस्था में तांबे का छिड़काव नहीं कर रही थीं। मैंने उनके साथ मिलकर एक परिवर्तन योजना तैयार की। उन्होंने बताया कि Crestmoor Cooperative Extension अगले महीने एक कार्यशाला आयोजित कर रहा है और हम दोनों सहमत हुए कि उन्हें उसमें शामिल होना चाहिए।"}
+{"id": "ent_013", "text": "Solas ने Type Systems repo में एक segfault को डीबग किया जो केवल aarch64 पर ट्रिगर हुआ। सुधार में यह पता चला कि इसमें LLVM intrinsics शामिल थे जो विभिन्न targets पर अलग-अलग व्यवहार कर रहे थे। उन्होंने LLVM maintainers के साथ एक upstream issue दायर किया; Aerwyn Compiler Group के Brennan Lyle ने अपनी तरफ से bisect करने की पेशकश की। हम अगले गुरुवार को Crestmoor systems meetup में अपने नोट्स की तुलना करेंगे।"}
+{"id": "ent_014", "text": "Thresh ने Pell Halloran की मदद की PO, receiving doc, और Hollowmounts Logistics के invoice के बीच एक जटिल three-way match को सुलझाने में। विसंगति का कारण unit-of-measure mismatch निकला — PO cases में था, जबकि invoice eaches में था। भविष्य में UoM mismatches को स्वचालित रूप से flag करने के लिए Invoice Parsing नियमों को अपडेट किया गया।"}
+{"id": "ent_015", "text": "Aria ने Saela से लगातार तीन statistical-tests प्रश्नों की समीक्षा की। तीसरा सबसे कठिन था: matched-but-not-identical samples के लिए paired or unpaired test का उपयोग करना है या नहीं। उसे निहितार्थ समझाए। वह Tartine Lab जर्नल के लिए एक संशोधन तैयार कर रही है और अंतिम तिथि शुक्रवार है।"}
+{"id": "ent_016", "text": "Fenra ने Aerwyn चक्र के Drukar लोगों के लिए नामकरण परंपराओं पर काम किया। ध्वनि-विज्ञान के नियम तैयार किए। Hellis Mar — अध्याय आठ की केंद्रीय Drukar पात्र — सहानुभूतिपूर्ण अपवाद है, और उसके नाम को Drukar और Wendish ध्वनि-प्रणालियों दोनों को जोड़ने की आवश्यकता है। कार्यरत दस्तावेज़ Wendelsea Writing Group को भेजा, और Saela को कॉपी किया, जो Bridgewater Cognitive Science में अपने काम से आविष्कृत भाषाओं के लिए तेज़ कान रखती हैं।"}
+{"id": "ent_017", "text": "Bramble ने Pollinator Paths के लिए Bridgewater Schools की बगीचा समिति के साथ परामर्श किया। वे केवल देशी पौधों का ही रोपण चाहते हैं लेकिन उनका बजट सख्त है। Wendelsea Native Plant Nursery के plug trays से शुरुआत करने की सलाह दी गई — ये gallon containers से सस्ते हैं और जल्दी जड़ पकड़ लेते हैं। उनके संस्थापक Iset Karadzic से बात की गई, जिन्होंने स्कूलों को अपनी गैर-लाभकारी दर देने के लिए सहमति दी।"}
+{"id": "ent_018", "text": "Solas ने Pol Krisat के साथ मिलकर नई parser combinator library को Aerwyn Labs codebase में एकीकृत किया। एकीकरण साफ-सुथरा था, सिवाय एक जगह के जहाँ उनका custom error type हमारे Distributed Tracing context से टकरा रहा था। एक छोटे adapter trait को पेश करके इसे हल किया गया। आसान review के लिए एकीकरण को एक अलग PR में push किया गया।"}
+{"id": "ent_019", "text": "Thresh Bridgewater Studio के नेतृत्व के साथ एक योजना बैठक में शामिल हुआ। उनके CFO Ivora Tinn कार्यकारी टीम के लिए एक नया Cashflow Models dashboard लॉन्च कर रही हैं। संचालन निदेशक Karis Tornau विभाग-वार drill-down चाहती हैं; Ivora केवल उच्च-स्तरीय सारांश चाहती हैं। समझौता: दो views, एक ही अंतर्निहित data।"}
+{"id": "ent_020", "text": "Aria ध्यान एंट्रोपी और तर्क सच्चाई के बीच संबंध का अध्ययन कर रही थीं। पिछले साल के Hollowmounts Institute के प्रकाशन ने इनके सकारात्मक संबंध के बारे में बताया था; लेकिन Aerwyn Labs के पुनर्प्रतिबिंबन में विपरीत परिणाम मिले थे। दोनों प्रकाशनों के लिए अलग-अलग मॉडल स्केल के लिए सही होने के लिए एक संग्रह तैयार किया गया था। इसे Crestmoor क्लस्टर पर जांचने के बाद किसी भी निष्कर्ष के लिए अपनाया जा सकता है।"}
+{"id": "ent_021", "text": "Fenra ने Aerwyn चक्र की भाषा पर परिशिष्ट की रूपरेखा तैयार की। तीन परिवार: Wendish (जिसे Wends बोलते हैं), Drukic (जिसे Drukar बोलते हैं), और पुरानी Salt Tongue, जिसका उपयोग केवल Hollowmounts के पुजारी अनुष्ठानिक रूप से करते हैं। Wendish और Salt Tongue के बीच ध्वनि परिवर्तनों का पता लगाया। उपयोगकर्ता चाहता है कि Saela बारहवें अध्याय में कुछ Salt Tongue बोले।"}
+{"id": "ent_022", "text": "Bramble की सुबह Wendelsea वनस्पति उद्यान में एक परागणकर्ता सर्वेक्षण में बीती। देशी मधुमक्खी की सात प्रजातियों की गिनती की गई, जिनमें दुर्लभ Crestmoor Mining Bee भी शामिल थी, जिसे Yumara Felk ने पेट की पट्टियों के आधार पर पहचाना। Pollinator Paths आधार रेखा के लिए यह महत्वपूर्ण डेटा है। Yumara इस सर्वेक्षण के परिणामों को Hollowmounts Phenology Network के साथ प्रकाशित कर रही हैं।"}
+{"id": "ent_023", "text": "Solas ने Crestmoor में नई SRE Mette Olafsen की मदद की Aerwyn Telemetry stack के लिए Distributed Tracing सेट अप करने में। सबसे कठिन हिस्सा OpenTelemetry collector को मिश्रित Rust और OCaml workloads संभालने के लिए तैयार करना था। हम OTLP HTTP export का उपयोग करने पर आए ताकि सेवाओं में gRPC version skew से निपटना न पड़े।"}
+{"id": "ent_024", "text": "Thresh ने Bridgewater Studio के नए उपकरणों के depreciation पर काम किया। Ivora Tinn Section 179 के पक्ष में थीं ताकि पूरे $180k को पहले साल में ही expense किया जा सके। हमने गणना की — उनके पास इसे बिना phase-out की समस्याओं के अवशोषित करने के लिए पर्याप्त taxable income नहीं है। इसके बजाय bonus depreciation की सिफारिश की, जिसमें income limitation नहीं होती। उन्होंने सहमति जताई।"}
+{"id": "ent_025", "text": "Aria और उपयोगकर्ता ने Embedding Spaces विजुअलाइजेशन टूल के बारे में लंबा चर्चा किया। उपयोगकर्ता, सेला, एक समय-स्लाइडर जोड़ना चाहती है ताकि वह इंडेक्सिंग अवधि के दौरान क्लस्टर कैसे बने वह देख सके। API का आकार बनाया गया। रेंडरिंग ओर ब्रेनन लाइ के साथ सहयोग कर रहे हैं — वह ब्रिडगवैटर विजुअलाइजेशन के समय में WebGL का अनुभव रखता है।"}
+{"id": "ent_026", "text": "Fenra ने सोच-विचार किया कि Hellis Mar के लिए आंतरिक संवाद को कैसे संभाला जाए ताकि Aerwyn cycle के शेष भाग में स्थापित third-person POV न टूटे। तीन विकल्प। एक संकर हल अपनाया: संवाद दृश्यों में गहरी आंतरिकता, अन्यत्र सतही विवरण। Wendelsea Writing Group से शायद इस बात पर प्रतिक्रिया मिले कि क्या यह बहुत खटकने वाला है।"}
+{"id": "ent_027", "text": "Bramble ने Doreth Ainsleigh के ब्लूबेरी के खेत में soil pH की समस्या के बारे में उनके साथ विस्तार से चर्चा की। उदासीन की ओर झुकाव के कारण Iron chlorosis। उनकी मिट्टी स्वाभाविक रूप से क्षारीय है — Wendelsea जिला चूना पत्थर की चट्टान पर है। गंधक के उपयोग और नए पौधों को एक उठे हुए बिस्तर में स्थानांतरित करने की सलाह दी, जिसमें बाहर से लाई गई अम्लीय मिट्टी हो।"}
+{"id": "ent_028", "text": "Solas ने Type Systems error reporting को refactor करने वाले Pol Krisat के PR की समीक्षा की। नया डिज़ाइन spans-with-labels का उपयोग करता है, जो rustc के समान है लेकिन हमारी row-polymorphism एक्सटेंशन के साथ। diagnostic formatting में कुछ सुधार सुझाए गए। Bek Halloran (यह अलग Bek है — यह एक योगदानकर्ता है, ऑडिटर नहीं) ने perf संबंधी चिंता व्यक्त की। ज़्यादातर हल हो गया है लेकिन एक comment thread अभी भी खुला है।"}
+{"id": "ent_029", "text": "Thresh का Tartine Lab के लिए तिमाही समापन। Cashflow Models ने सप्ताह 9 में एक कमी की भविष्यवाणी की थी जो साकार नहीं हुई क्योंकि Hollowmounts Logistics ने समय से पहले भुगतान कर दिया। मॉडल खराब नहीं है — बस यह है कि जल्दी भुगतानों का fat right tail होता है। मॉडल के वितरण धारणाओं को अपडेट किया और फिर से चलाया। संशोधित पूर्वानुमान Ivora Tinn को भेज दिया।"}
+{"id": "ent_030", "text": "Aria, उपयोगकर्ता एक नई परियोजना शुरू करना चाहता है — इसे Faithful Chains कहें — chain-of-thought स्थिरता अनुसंधान के लिए। Meta-Cognition से काफी अलग होने के कारण मुझे लगता है कि इसे अपना एक अलग स्थान मिलना चाहिए। Ren Solanke के साथ समन्वय करूँगा और Hollowmounts Institute के पेपर को प्रारंभिक बिंदु के रूप में उद्धृत करेंगे। Crestmoor Cognitive Science अनुदान के माध्यम से वित्तपोषण।"}
+{"id": "ent_031", "text": "Fenra का दैनिक लॉग: लेखन का दिन। अध्याय ग्यारह में Saela / Hellis Mar के टकराव पर 2,200 शब्द लिखे। कठिन दृश्य — Salt Tongue वार्तालापों को वास्तविक लगना चाहिए था, भले ही हम उन्हें पन्ने पर अनुवादित न करें। ड्राफ्ट Wendelsea Writing Group की पाठक Karis Tornau को भेजा (जो अपनी नियमित नौकरी के अलावा कथा साहित्य पढ़ती हैं, यह उपयोगी है)।"}
+{"id": "ent_032", "text": "Bramble ने Hollowmounts Institute के अनुभवात्मक बगीचे में दौरा किया। उनके निवासी प्रजातियों के प्रोग्राम अभिलेख संग्रह के लिए अभिन्न जिला जीव विविधता के लिए अद्वितीय कार्य कर रहे हैं। उन्होंने अपने प्रमुख खेती विद्यालय के आधिकारिक विशेषज्ञ, Iset Karadzic से मिला — जो नर्सरी में भी एक ही Iset है, उन्हें इंस्टीट्यूट से भी सलाह मिलती है। उन्होंने मुझे स्थानीय उत्पत्ति से कार्डिनल फ्लॉवर के बीज का एक पैकेट दिया।"}
+{"id": "ent_033", "text": "Solas ने परियोजना में एक नए उपयोगकर्ता की मदद की — Pol Krisat ने उनका परिचय कराया — Aerwyn Labs कंपाइलर के लिए एक विकास परिवेश स्थापित करने में। वे Windows पर थे और हमारे LLVM टूलचेन के साथ सामान्य क्रॉस-कंपाइलेशन समस्याओं का सामना किया। Crestmoor Systems विकी पर Windows-विशिष्ट सेटअप चरणों के लिए उन्हें निर्देशित किया। उन्होंने लगभग एक घंटे में इसे बिल्ड कर लिया।"}
+{"id": "ent_034", "text": "Thresh और Ivora Tinn ने Bridgewater Studio की cap table पर चर्चा की। वे एक छोटा secondary sale कर रहे हैं और अगले investor (Aerwyn Capital) के diligence करने से पहले पिछले round के हिसाब-किताब को साफ़ करना चाहते हैं। कुछ shares सही 83(b) elections के बिना जारी किए गए थे। उन्हें सलाह दी गई कि वे आगे कोई कदम उठाने से पहले Crestmoor Legal Group के अपने securities attorney को इसमें शामिल करें।"}
+{"id": "ent_035", "text": "Aria ने दोपहर contrastive embedding objectives की literature review में बिताई। उपयोगकर्ता Saela को spectral kernel framing के तहत SimCLR, BYOL, और Barlow Twins को जोड़ते हुए एक एक-पृष्ठीय सारांश चाहिए था। Tartine Lab और Aerwyn Labs preprints से संदर्भ शामिल किए गए। Bridgewater Cognitive Science का एक प्रासंगिक 2023 पेपर भी है जिसे मैंने अंत में जोड़ा।"}
+{"id": "ent_036", "text": "Fenra ने उपयोगकर्ता के साथ Aerwyn चक्र के पौराणिक आधारों पर चर्चा की। Mother of Salt मिथक की Hollowmounts की मौखिक परंपरा में समानताएँ हैं। वेल्श और फिनिश पौराणिक मोटिफ़ पर शोध किया गया और एक संस्करण तैयार किया गया जो Aerwyn के लिए स्वदेशी लगता है, न कि उधार लिया हुआ। Wendelsea Writing Group इसे अगले सप्ताह पढ़ेगी।"}
+{"id": "ent_037", "text": "Bramble ने Bridgewater सामुदायिक बगीचे में soil microbe assessment किया। उनकी खाद बेहतरीन है — microbial diversity अधिक है, और सब्जी की क्यारियों के लिए fungal-to-bacterial ratio अच्छा है। Karis Tornau का लक्ष्य इसे Crestmoor और Wendelsea के उपग्रह बगीचों तक बढ़ाना है। पिछले साल हमारे द्वारा तैयार किया गया Pollinator Paths गलियारा भी अच्छा प्रदर्शन कर रहा है, और bee diversity में 40% की वृद्धि हुई है।"}
+{"id": "ent_038", "text": "सोलास डिस्ट्रीब्यूटेड ट्रेसिंग क्लाइंट लाइब्रेरी के लिए थ्रेड-सेफ्टी ऑडिट के काम कर रहा था। दो रेस कंडीशन खोजे गए थे जिन्हें थ्रेड सैनिटाइजर ने नहीं पकड़ा था — वे केवल क्रेस्टमोर प्रोडक्शन स्टैक पर बहुत उच्च संगति पर दिखाई देते थे। मेट ओलाफ्सेन ने पुष्टि कर दी कि वह उन्हें दोहरा सकती थी। फिक्सेस को पुश किया गया था; दोनों ब्रेन्नन लाइल द्वारा रिव्यू किए गए थे।"}
+{"id": "ent_039", "text": "Thresh ने Carag-aana Press के Pell Halloran के साथ Invoice Parsing समीक्षा किया। उनका AP PDF चालान से हटकर एक संरचित ZUGFeRD-शैली का प्रारूप अपना रहा है। हमने अंतर्निहित XML को संभालने के लिए अपना Invoice Parsing पाइपलाइन अद्यतित किया। यह बदलाव Carag-aana को हमारे प्रोसेसिंग शुल्क में हर साल लगभग $4k की बचत दिलाएगा, जिसकी Pell ने सराहना की।"}
+{"id": "ent_040", "text": "Aria ने Saela के Embedding Spaces पेपर के ड्राफ्ट परिचय की समीक्षा की। तकनीकी सामग्री मजबूत है, लेकिन प्रस्तुति मुख्य बात को पीछे छोड़ देती है — नवीन योगदान दूसरे वाक्य में होना चाहिए, न कि दूसरे पन्ने पर। संरचना में पुनर्व्यवस्था की सिफारिश की गई। Saela अगले हफ्ते Tartine Lab जर्नल में सबमिशन दे रहे हैं, इसलिए हमारे पास संशोधन का समय सीमित है।"}
+{"id": "ent_041", "text": "Fenra और उपयोगकर्ता ने Drukar क्षेत्रों का भूगोल तैयार किया। Drukar Hollowmounts के दक्षिण में, Salt Flats नामक क्षेत्र में रहते हैं। उनकी मुख्य बस्ती Vroth-Karadz एक सूखी झील के किनारे बसी है। Hellis Mar एक छोटे गाँव Krast-Endel से है, जो राजधानी से तीन दिन की सवारी की दूरी पर है।"}
+{"id": "ent_042", "text": "Bramble का Iset Karadzic के साथ Wendelsea Native Plant Nursery में Native Species कार्य रंग ला रहा है। इस सीज़न में उनका प्लग-ट्रे उत्पादन 60% बढ़ गया है और वे क्षेत्र भर में Crestmoor Conservation Trust की पुनर्वास परियोजनाओं के लिए शिपमेंट भेज रहे हैं। Hollowmounts Institute अब उसके ecotype संग्रह से भी बीज प्राप्त कर रहा है।"}
+{"id": "ent_043", "text": "Solas ने पिछले सप्ताह Crestmoor में Distributed Tracing आउटेज का पोस्टमॉर्टम लिखा। मूल कारण: एक खराब कॉन्फिग पुश जिसने 47 मिनट के लिए OTLP exporter को अक्षम कर दिया। Mette Olafsen ने प्रतिक्रिया का नेतृत्व किया; Brennan Lyle ने रनबुक अपडेट लिखा। पुनरावृत्ति रोकने के लिए CI में एक कॉन्फिग-सत्यापन चरण जोड़ा जा रहा है। Aerwyn Telemetry incident wiki में पोस्टमॉर्टम दर्ज किया गया।"}
+{"id": "ent_044", "text": "Thresh ने Wendelsea Orchard की Doreth Ainsleigh की Schedule F में मदद की। उसके पास फल बिक्री और कृषि-पर्यटन दोनों की आय थी और वह बाद वाले को कैसे संभालें, इसे लेकर असमंजस में थी। उसे संबंधित IRS मार्गदर्शिका समझाई गई — कृषि-पर्यटन आमतौर पर एक अलग व्यावसायिक गतिविधि होती है। दाखिल करने से पहले अपने CPA से चर्चा करने की सलाह दी गई। Pell Halloran (कोई संबंध नहीं, Wendelsea में अजीब तरह से आम नाम) वह CPA थे जिनका उसने अंततः उपयोग किया।"}
+{"id": "ent_045", "text": "Aria ने यह जाँचा कि Meta-Cognition परियोजना के निष्कर्ष बहु-मोडल मॉडलों पर भी लागू होंगे या नहीं। Hollowmounts Institute अपने आंतरिक vision-language मॉडल पर इस संबंध में प्रयोग चला रहा है। अगले सप्ताह Ren Solanke के साथ विचार-विमर्श करने के लिए एक बैठक निर्धारित करें। Bridgewater Cognitive Science group का समानांतर कार्य उल्लेख करने योग्य है।"}
+{"id": "ent_046", "text": "Fenra ने Aerwyn के परिशिष्टों के लिए एक शब्दकोश तैयार किया। अब तक इसमें तेईस शब्द हैं, जो सांस्कृतिक अवधारणाओं (salt-debt, the Wendelsea binding) से लेकर भौगोलिक (the Hollowmounts, the Salt Flats, Bryn-iili, Vroth-Karadz) तक हैं। Wendelsea Writing Group के बीटा पाठकों ने बताया कि शब्दकोश ने अध्याय दस के दौरान उन्हें ट्रैक बनाए रखने में मदद की।"}
+{"id": "ent_047", "text": "Bramble और Yumara Felk के परागक सर्वेक्षण ने Bridgewater सामुदायिक बगीचे में Crestmoor खनन मधुमक्खी के एक नए उपनिवेश की पहचान की। यह महत्वपूर्ण है — इस प्रजाति को Wendelsea जिले तक सीमित माना जाता था। Hollowmounts Phenology Network को सूचित कर दिया गया है ताकि वे अपने क्षेत्र मानचित्रों को अद्यतन कर सकें। Karis Tornau स्वयंसेवक सहभागिता के इस अवसर को लेकर उत्साहित हैं।"}
+{"id": "ent_048", "text": "Solas ने Pol Krisat के साथ Type Systems row-polymorphism कार्यान्वयन पर काम किया। उनका एल्गोरिदम उस kind-checking edge case को संभालता है जिसे मैंने पिछले सप्ताह चिह्नित किया था। इसका परीक्षण Aerwyn Labs corpus पर किया गया और यह हर पहले से स्वीकृत प्रोग्राम को स्वीकार करता है, साथ ही कुछ ऐसे भी जिन्हें पुराने एल्गोरिदम ने अस्वीकार किया था। Bek Halloran (योगदानकर्ता, ऑडिटर नहीं) ने diff की समीक्षा की।"}
+{"id": "ent_049", "text": "Thresh ने Bridgewater Studio के लिए Aerwyn Capital के जाँच-पड़ताल संबंधी सवालों की समीक्षा की। उन्हें पिछले 24 महीनों के स्वच्छ Cashflow Models चाहिए, ऑडिट किए गए वित्तीय विवरण (जो Wendelsea Audit Partners के पास हैं), और वह cap table reconciliation जिस पर Ivora Tinn और मैं काम कर रहे हैं। समय सीमा कड़ी है — वे यह सब महीने के अंत तक चाहते हैं। ऑडिट टीम के Bek Halloran के साथ समन्वय किया।"}
+{"id": "ent_050", "text": "Aria ने Saela के लिए दिन के शोध का सारांश तैयार किया। तीन मुख्य निष्कर्ष: Tartine Lab के पेपर के विचार-श्रृंखला निष्ठा संबंधी दावे छोटे मॉडलों पर साफ तौर पर दोहराए नहीं जाते; Hollowmounts Institute का दृष्टि-भाषा विस्तार आशाजनक है लेकिन अधूरा; और Aerwyn Labs की ध्यान एन्ट्रॉपी पर प्रीप्रिंट में एक सूक्ष्म कार्यप्रणालीगत समस्या है जिसे उजागर करने की आवश्यकता है। Crestmoor संज्ञान विज्ञान साप्ताहिक के लिए एक ज्ञापन का मसौदा तैयार किया।"}

benchmarks/model_eval/datasets/entity_extraction/dataset.it.jsonl

@@ -0,0 +1,50 @@
+{"id": "ent_001", "text": "Aria ha detto che l'articolo del Tartine Lab sulla valutazione potenziata dal recupero è finalmente su arXiv. Saela lo stava aspettando: è responsabile del progetto Embedding Spaces e vuole citarlo nella prossima prepubblicazione del suo gruppo. Il laboratorio si trova a Crestmoor, a due passi dal Bridgewater Studio dove Fenra un tempo lavorava come consulente. A quanto pare, il primo autore, Gera Vossen, si trasferirà all'Hollowmounts Institute dal prossimo semestre."}
+{"id": "ent_002", "text": "Solas ha inviato una correzione per il bug di propagazione delle tracce nel progetto Distributed Tracing. Revisionato da Brennan Lyle del team di runtime presso Crestmoor Systems. Il bug si manifestava solo sotto carico, ed è per questo che lo stress test del consorzio Aerwyn Telemetry ha individuato il bug prima del nostro CI interno."}
+{"id": "ent_003", "text": "Bramble ha illustrato la progettazione del nuovo corridoio Pollinator Paths per il Wendelsea Botanic Garden. L'entomologa del loro staff, Yumara Felk, ha suggerito di aggiungere menta di montagna lungo il bordo orientale. Il progetto è finanziato dalla Crestmoor Conservation Trust, che da due decenni sostiene il lavoro sulle piante autoctone nella regione."}
+{"id": "ent_004", "text": "Thresh ha chiuso i libri contabili per Bridgewater Studio. Il loro AR aging report ha segnalato Carag-aana Press come un account con pagamenti lenti — insoluto da 78 giorni. Si è messo in contatto con Pell Halloran nel reparto accounts payable. La pipeline di Invoice Parsing ha indirizzato automaticamente tre delle loro fatture nella coda di revisione manuale perché le voci non corrispondevano al PO del subcontratto di Tartine Lab."}
+{"id": "ent_005", "text": "Fenra ha fatto un workshop con l'utente sul terzo atto del ciclo Aerwyn. L'arco di Saela raggiunge la sua svolta negli Hollowmounts, e i Wends sono testimoni cruciali della sua decisione. L'utente vuole collocare il culmine a Bryn-iili, che ritengo una scelta azzeccata considerato il motivo del debito di sale introdotto nel capitolo quattro."}
+{"id": "ent_006", "text": "La pipeline Topic Clustering è andata in crash durante l'indicizzazione notturna. Aria ha recuperato i log e ha rintracciato la causa in una perdita di memoria nella libreria HDBSCAN — in particolare il backend joblib sul cluster di calcolo di Crestmoor. Ha aperto una issue presso i maintainer upstream, mettendo in copia Mette Olafsen, che è stata il contatto principale presso Crestmoor IT."}
+{"id": "ent_007", "text": "Registro giornaliero di Bramble: visita mattutina a Bridgewater Community Garden per valutare la situazione della cimice delle zucche. Karis Tornau, coordinatrice dei volontari, mi ha illustrato il loro piano di lotta integrata. Pomeriggio: aiutato Ralf Ginder a progettare una bordura Native Species per la sua proprietà nel distretto di Wendelsea. Sera: letto un articolo della Hollowmounts Phenology Network sugli spostamenti delle prime fioriture."}
+{"id": "ent_008", "text": "Solas ha revisionato una PR di Aerwyn Labs. Si tratta di una libreria di parser combinator che si basa su Megaparsec, progettata per il progetto Type Systems. Il manutentore, Pol Krisat, è stato molto reattivo. Bisogna menzionarlo al team del Crestmoor Compiler Group — stavano cercando qualcosa di simile."}
+{"id": "ent_009", "text": "Thresh ha lavorato all'adozione degli IFRS per Tartine Lab. Stanno passando dai GAAP locali e i Cashflow Models che abbiamo costruito lo scorso trimestre devono essere adeguati per la nuova contabilità dei leasing secondo IFRS 16. In coordinamento con il loro CFO Ivora Tinn e il suo revisore esterno Bek Halloran di Wendelsea Audit Partners."}
+{"id": "ent_010", "text": "Aria ha preparato una lista di letture per il nuovo postdoc, Ren Solanke, che si unisce al progetto Meta-Cognition a Crestmoor. Ha iniziato con gli articoli fondamentali di Bridgewater Cognitive Science, poi ha aggiunto la serie di Hollowmounts Institute sul prompting autoreferenziale, e infine il recente preprint di Aerwyn Labs sulla stabilità della catena di pensiero."}
+{"id": "ent_011", "text": "Fenra e l'utente hanno passato un'ora su Dialogue Engine. L'attuale modello del grafo delle relazioni non gestisce un caso limite specifico: quando due personaggi di fazioni in lotta (i Wends e i Drukar) sono costretti a cooperare. Abbozzata una soluzione alternativa. In particolare, la voce di Saela deve ammorbidirsi quando parla con Hellis Mar — c'è una storia tra loro che il motore non coglie."}
+{"id": "ent_012", "text": "Bramble ha identificato un problema fungino sulle mele antiche nel frutteto di Wendelsea. Ticchiolatura, classica. La responsabile del frutteto, Doreth Ainsleigh, non aveva effettuato trattamenti rameici in dormienza. Ha messo a punto un piano di transizione con lei. Ha menzionato che la Crestmoor Cooperative Extension terrà un workshop il mese prossimo e abbiamo concordato entrambi che lei dovrebbe partecipare."}
+{"id": "ent_013", "text": "Solas ha individuato la causa di un segfault nel repo di Type Systems che si verificava solo su aarch64. La soluzione ha riguardato intrinsics di LLVM che si comportavano in modo diverso su diversi target. Ho aperto una issue upstream con i responsabili di LLVM; Brennan Lyle dell'Aerwyn Compiler Group si è offerto di fare bisect dalla sua parte. Confronteremo le note al meetup sui sistemi di Crestmoor giovedì prossimo."}
+{"id": "ent_014", "text": "Thresh ha aiutato Pell Halloran a riconciliare una complicata corrispondenza a tre vie tra PO, documento di ricezione e fattura di Hollowmounts Logistics. La discrepanza si è rivelata essere un disallineamento di unità di misura — il PO era in scatole, la fattura in unità singole. Aggiornate le regole di Invoice Parsing per segnalare automaticamente i disallineamenti di UoM in futuro."}
+{"id": "ent_015", "text": "Aria ha esaminato tre domande sui test statistici di Saela una dopo l'altra. La terza è stata la più difficile: se usare un test appaiato o non appaiato per campioni abbinati ma non identici. Le ha spiegato passo dopo passo le implicazioni. Sta preparando una revisione per la rivista del Tartine Lab e la scadenza è venerdì."}
+{"id": "ent_016", "text": "Fenra ha lavorato sulle convenzioni di denominazione per il popolo Drukar del ciclo Aerwyn. Ha redatto regole fonologiche. Hellis Mar — un personaggio Drukar centrale nel capitolo otto — è l'eccezione empatica, e il suo nome deve fare da ponte tra i sistemi fonetici Drukar e Wendish. Ha inviato il documento di lavoro al Wendelsea Writing Group, e ha messo in copia Saela, che ha un orecchio fine per le lingue inventate grazie al suo lavoro presso Bridgewater Cognitive Science."}
+{"id": "ent_017", "text": "La consultazione di Bramble per Pollinator Paths con il comitato del giardino delle Bridgewater Schools. Vogliono piantumazioni esclusivamente autoctone ma hanno un budget rigido. Consigliato di iniziare con i vassoi per plantine del Wendelsea Native Plant Nursery — più economici dei contenitori da un gallone e attecchiscono più rapidamente. Parlato con il fondatore, Iset Karadzic, che ha accettato di offrire alle scuole la tariffa riservata agli enti non profit."}
+{"id": "ent_018", "text": "Solas ha lavorato in coppia con Pol Krisat per integrare la nuova libreria di parser combinator nella base di codice di Aerwyn Labs. L'integrazione è stata pulita tranne che in un punto dove il loro tipo di errore personalizzato entrava in conflitto con il nostro contesto di Distributed Tracing. Risolto introducendo un piccolo adapter trait. Ha inviato l'integrazione come PR separata per facilitare la revisione."}
+{"id": "ent_019", "text": "Thresh ha partecipato a una riunione di pianificazione con la dirigenza di Bridgewater Studio. Il loro CFO Ivora Tinn sta lanciando una nuova dashboard Cashflow Models per il team esecutivo. Il direttore operativo Karis Tornau vuole un drill-down per reparto; Ivora vuole solo riepiloghi di alto livello. Compromesso: due viste, gli stessi dati sottostanti."}
+{"id": "ent_020", "text": "Aria ha esplorato la relazione tra attention entropy e reasoning faithfulness. Il paper di Hollowmounts Institute dell'anno scorso sosteneva che fossero positivamente correlati; la replica di Aerwyn Labs ha trovato il contrario. Ha redatto una sintesi che sostiene che entrambi i paper hanno ragione ma per diverse scale di modello. Bisogna verificare questo sul cluster Crestmoor prima di sostenere qualsiasi cosa."}
+{"id": "ent_021", "text": "Fenra ha delineato l'appendice del ciclo di Aerwyn sulla lingua. Tre famiglie: Wendish (i Wends la parlano), Drukic (i Drukar) e la più antica Salt Tongue, che solo i sacerdoti degli Hollowmounts usano cerimonialmente. Ha tracciato i cambiamenti fonetici tra Wendish e Salt Tongue. L'utente vuole che Saela parli un po' di Salt Tongue nel capitolo dodici."}
+{"id": "ent_022", "text": "La mattinata di Bramble è stata un'indagine sui pollinatori presso Wendelsea Botanic Garden. Ha contato sette specie di api native, tra cui la rara Crestmoor Mining Bee, che Yumara Felk ha identificato dalle bande addominali. Dati importanti per la linea di base di Pollinator Paths. Yumara sta pubblicando i risultati dell'indagine con Hollowmounts Phenology Network."}
+{"id": "ent_023", "text": "Solas ha aiutato Mette Olafsen, la nuova SRE di Crestmoor, a configurare Distributed Tracing per lo stack Aerwyn Telemetry. La parte difficile è stata far sì che l'OpenTelemetry collector gestisse i carichi di lavoro misti Rust e OCaml. Abbiamo optato per l'uso di OTLP HTTP export per non dover gestire il disallineamento delle versioni di gRPC tra i servizi."}
+{"id": "ent_024", "text": "Thresh ha analizzato l'ammortamento del nuovo equipaggiamento di Bridgewater Studio. Ivora Tinn spingeva per utilizzare il Section 179 per dedurre integralmente l'intero importo di $180k nel primo anno. Ha fatto i calcoli: non hanno abbastanza reddito imponibile per assorbirlo senza problemi di riduzione graduale. Ha consigliato invece il bonus depreciation, che non prevede il limite di reddito. Ha accettato."}
+{"id": "ent_025", "text": "Aria e l'utente hanno avuto una lunga conversazione sullo strumento di visualizzazione Embedding Spaces. L'utente, Saela, vuole aggiungere uno slider temporale per poter riprodurre come si sono formati i cluster nel periodo di indicizzazione. Hanno abbozzato l'API. Si stanno coordinando con Brennan Lyle sul lato rendering — lui ha esperienza con WebGL dal suo periodo presso Bridgewater Visualization."}
+{"id": "ent_026", "text": "Fenra ha riflettuto su come gestire il monologo interiore per Hellis Mar senza violare il third-person POV stabilito nel resto del ciclo di Aerwyn. Tre opzioni. Ha optato per un ibrido: interiorità profonda nelle scene di dialogo, descrizione superficiale altrove. Il Wendelsea Writing Group potrebbe fornire un parere su se questo sia troppo stridente."}
+{"id": "ent_027", "text": "Bramble ha esaminato con lei il problema del pH del suolo nell'appezzamento di mirtilli di Doreth Ainsleigh. Clorosi ferrica causata da una deriva verso il neutro. Il suo suolo è naturalmente alcalino — il distretto di Wendelsea si trova su roccia madre calcarea. Ha consigliato di applicare zolfo e di spostare le nuove piante in un letto rialzato con suolo acido importato."}
+{"id": "ent_028", "text": "Solas ha revisionato la PR di Pol Krisat per il refactoring della segnalazione degli errori di Type Systems. Il nuovo design utilizza spans-with-labels, in modo simile a rustc ma con le nostre estensioni row-polymorphism. Ha suggerito un paio di miglioramenti al diagnostic formatting. Bek Halloran (un Bek diverso — questo è un collaboratore, non il revisore) è intervenuto con una questione perf. Per la maggior parte risolta, ma un comment thread è ancora aperto."}
+{"id": "ent_029", "text": "Chiusura trimestrale di Thresh per Tartine Lab. I Cashflow Models prevedevano un deficit nella settimana 9 che non si è verificato perché Hollowmounts Logistics ha pagato in anticipo. Il modello non è rotto — è solo che i pagamenti anticipati hanno una coda destra grassa. Aggiornate le ipotesi di distribuzione del modello e rieseguito. Inviata la previsione revisionata a Ivora Tinn."}
+{"id": "ent_030", "text": "Aria, l'utente vuole avviare un nuovo progetto — chiamiamolo Faithful Chains — per la ricerca sulla stabilità della catena del pensiero. È abbastanza distinto da Meta-Cognition da meritare uno spazio proprio. Mi coordinerò con Ren Solanke e citeremo l'articolo dell'Hollowmounts Institute come punto di partenza. Finanziamento tramite il contributo di Crestmoor Cognitive Science."}
+{"id": "ent_031", "text": "Log giornaliero di Fenra: giornata di scrittura. 2.200 parole scritte per il confronto tra Saela e Hellis Mar nel capitolo undici. Scena difficile — gli scambi in Salt Tongue dovevano sembrare reali anche se non li traduciamo sulla pagina. Bozza inviata al lettore del Wendelsea Writing Group, Karis Tornau (che legge narrativa al di fuori del suo lavoro, utile)."}
+{"id": "ent_032", "text": "Bramble ha visitato il giardino sperimentale del Hollowmounts Institute. Il loro programma Native Species sta svolgendo un lavoro straordinario con la conservazione dei semi per ecotipi regionali. Ho incontrato il loro orticoltore capo, Iset Karadzic — la stessa Iset del vivaio, è consulente anche per l'Istituto. Mi ha dato un pacchetto di semi di fiore cardinale di provenienza locale."}
+{"id": "ent_033", "text": "Solas ha aiutato un nuovo utente del progetto — presentatogli da Pol Krisat — a configurare un ambiente di sviluppo per il compilatore di Aerwyn Labs. L'utente usava Windows e ha riscontrato i soliti problemi di cross-compilazione con la nostra toolchain LLVM. Solas ha indicato all'utente il wiki di Crestmoor Systems per i passaggi di configurazione specifici per Windows. L'utente è riuscito a compilarlo in circa un'ora."}
+{"id": "ent_034", "text": "Thresh e Ivora Tinn hanno discusso il cap table di Bridgewater Studio. Stanno effettuando una piccola vendita secondaria e devono mettere in ordine la contabilità del round precedente prima che il prossimo investitore (Aerwyn Capital) svolga la diligence. Alcune azioni sono state emesse senza le dovute 83(b) elections. Le ha consigliato di coinvolgere il loro avvocato di diritto mobiliare di Crestmoor Legal Group prima di qualsiasi ulteriore azione."}
+{"id": "ent_035", "text": "Aria ha trascorso il pomeriggio lavorando a una rassegna della letteratura sugli obiettivi dell'embedding contrastivo. L'utente, Saela, voleva un riassunto di una pagina che collegasse SimCLR, BYOL e Barlow Twins nell'ottica del kernel spettrale. Ho recuperato riferimenti dai preprint di Tartine Lab e Aerwyn Labs. Anche Bridgewater Cognitive Science ha un articolo pertinente del 2023 che ho aggiunto alla fine."}
+{"id": "ent_036", "text": "Fenra ha discusso i fondamenti mitologici del ciclo di Aerwyn con l'utente. Il mito di Mother of Salt ha paralleli nella tradizione orale di Hollowmounts. Ha ricercato motivi mitologici gallesi e finlandesi e ha sintetizzato una versione che sembra nativa di Aerwyn piuttosto che presa in prestito. Il Wendelsea Writing Group la leggerà la prossima settimana."}
+{"id": "ent_037", "text": "Bramble ha condotto una valutazione dei microbi del suolo presso il Bridgewater Community Garden. Il loro compost è eccellente — alta diversità microbica, buon rapporto funghi-batteri per le aiuole orticole. Karis Tornau vuole estendere questo approccio ai giardini satellite di Crestmoor e Wendelsea. Anche il corridoio Pollinator Paths che abbiamo progettato l'anno scorso sta funzionando bene, con un aumento del 40% della diversità delle api."}
+{"id": "ent_038", "text": "Solas ha condotto un audit di sicurezza dei thread per la libreria client di Distributed Tracing. Ha individuato due race condition che ThreadSanitizer non aveva rilevato — si manifestavano solo a livelli di concorrenza molto elevati sullo stack di produzione di Crestmoor. Mette Olafsen ha confermato di poterle riprodurre. Ha caricato le correzioni; entrambe revisionate da Brennan Lyle."}
+{"id": "ent_039", "text": "La revisione di Thresh sull'invoice parsing con Pell Halloran di Carag-aana Press. Il loro AP sta abbandonando le fatture PDF per passare a un formato strutturato in stile ZUGFeRD. Abbiamo aggiornato la nostra pipeline di Invoice Parsing per gestire l'XML incorporato. La transizione farà risparmiare a Carag-aana circa 4.000 dollari all'anno nelle nostre commissioni di elaborazione, e Pell lo ha apprezzato."}
+{"id": "ent_040", "text": "Aria ha revisionato la bozza dell'introduzione di Saela per il paper Embedding Spaces. Contenuto tecnico solido, ma la struttura nasconde il punto chiave: il contributo originale dovrebbe essere nella seconda frase, non nella seconda pagina. Raccomandata una riorganizzazione strutturale. Saela sottoporrà il paper alla rivista Tartine Lab la settimana prossima, quindi abbiamo tempo limitato per le revisioni."}
+{"id": "ent_041", "text": "Fenra e l'utente hanno tracciato la geografia dei territori Drukar. I Drukar vivono a sud degli Hollowmounts, in una regione chiamata Salt Flats. Il loro insediamento principale, Vroth-Karadz, si trova ai margini di un lago prosciugato. Hellis Mar proviene da un villaggio più piccolo, Krast-Endel, a tre giorni di cavallo dalla capitale."}
+{"id": "ent_042", "text": "Il lavoro di Bramble su Native Species con Iset Karadzic alla Wendelsea Native Plant Nursery sta dando i suoi frutti. La loro produzione di vassoi alveolati è aumentata del 60% questa stagione e stanno spedendo ai progetti di restauro della Crestmoor Conservation Trust in tutta la regione. Anche l'Hollowmounts Institute si sta ora rifornendo di semi dalla sua collezione di ecotipi."}
+{"id": "ent_043", "text": "Solas ha redatto un postmortem per l'interruzione di Distributed Tracing della scorsa settimana a Crestmoor. Causa principale: un push errato della configurazione che ha disabilitato l'esportatore OTLP per 47 minuti. Mette Olafsen ha guidato la risposta; Brennan Lyle ha scritto l'aggiornamento del manuale operativo. Aggiunta di un passaggio di validazione della configurazione a CI per evitare che si ripeta. Postmortem archiviato nella wiki degli incidenti di Aerwyn Telemetry."}
+{"id": "ent_044", "text": "Thresh ha aiutato Doreth Ainsleigh della Wendelsea Orchard con il suo Schedule F. Ha sia vendite di frutta che ricavi da agriturismo e non era sicura di come gestire questi ultimi. Ha esaminato con lei le linee guida pertinenti dell'IRS — l'agriturismo è generalmente un'attività commerciale separata. Le ha raccomandato di discuterne con il suo CPA prima di presentare la dichiarazione. Pell Halloran (nessuna parentela, un nome stranamente comune a Wendelsea) è stato il CPA che ha utilizzato alla fine."}
+{"id": "ent_045", "text": "Aria ha esplorato se i risultati del progetto Meta-Cognition si estendessero ai modelli multimodali. L'Hollowmounts Institute ha condotto esperimenti su questo con il loro vision-language model interno. Organizza un incontro con Ren Solanke per la prossima settimana per confrontare i risultati. Il gruppo di scienze cognitive di Bridgewater ha lavori paralleli che vale la pena citare."}
+{"id": "ent_046", "text": "Fenra ha compilato un glossario per le appendici di Aerwyn. Ventitré termini finora, che spaziano da concetti culturali (salt-debt, the Wendelsea binding) a geografici (the Hollowmounts, the Salt Flats, Bryn-iili, Vroth-Karadz). I lettori beta del Wendelsea Writing Group hanno riferito che il glossario li ha aiutati a orientarsi durante il capitolo dieci."}
+{"id": "ent_047", "text": "L'indagine sui pollinatori di Bramble con Yumara Felk ha identificato una nuova colonia della Crestmoor Mining Bee nel Bridgewater Community Garden. Significativo — la specie si riteneva limitata al distretto di Wendelsea. Ho informato la Hollowmounts Phenology Network così possono aggiornare le loro mappe di distribuzione. Karis Tornau è entusiasta dell'opportunità di coinvolgimento dei volontari."}
+{"id": "ent_048", "text": "Solas ha collaborato con Pol Krisat all'implementazione del row-polymorphism di Type Systems. Il loro algoritmo gestisce il caso limite del kind-checking che avevo segnalato la settimana scorsa. L'ho testato sul corpus di Aerwyn Labs e accetta ogni programma precedentemente accettato più una manciata di quelli che erano stati rifiutati dal vecchio algoritmo. Bek Halloran (il collaboratore, non il revisore) ha revisionato il diff."}
+{"id": "ent_049", "text": "Thresh ha passato in rassegna le domande di due diligence di Aerwyn Capital per Bridgewater Studio. Vogliono Cashflow Models puliti degli ultimi 24 mesi, dati finanziari revisionati (Wendelsea Audit Partners li ha) e la riconciliazione della cap table su cui Ivora Tinn e io stavamo lavorando. Tempistiche strette: vogliono tutto entro fine mese. Coordinato con Bek Halloran del team di revisione."}
+{"id": "ent_050", "text": "Aria ha riassunto la ricerca del giorno per Saela. Tre risultati chiave: le affermazioni dell'articolo di Tartine Lab sul chain-of-thought faithfulness non si replicano in modo pulito su modelli più piccoli; l'estensione vision-language dell'Hollowmounts Institute è promettente ma incompleta; e il preprint di Aerwyn Labs sull'attention entropy presenta un sottile problema metodologico che deve essere segnalato. Ha redatto un memo per la riunione settimanale di Crestmoor Cognitive Science."}

benchmarks/model_eval/datasets/entity_extraction/dataset.jsonl

@@ -0,0 +1,50 @@
+{"id": "ent_001", "text": "Aria mentioned that the Tartine Lab paper on retrieval-augmented evaluation is finally on arXiv. Saela had been waiting for it — she's running the Embedding Spaces project and wants to cite it in her group's next preprint. The lab is in Crestmoor, just down the road from Bridgewater Studio where Fenra used to consult. Apparently the lead author, Gera Vossen, is moving to the Hollowmounts Institute next term."}
+{"id": "ent_002", "text": "Solas pushed a fix for the trace propagation bug in the Distributed Tracing project. Reviewed by Brennan Lyle from the runtime team at Crestmoor Systems. The bug only manifested under load, which is why the Aerwyn Telemetry consortium's stress test caught it before our internal CI did."}
+{"id": "ent_003", "text": "Bramble walked through the design of the new Pollinator Paths corridor for the Wendelsea Botanic Garden. Their staff entomologist, Yumara Felk, suggested adding mountain mint along the eastern edge. The project is funded by the Crestmoor Conservation Trust, which has been supporting native-plant work in the region for two decades."}
+{"id": "ent_004", "text": "Thresh closed out the books for Bridgewater Studio. Their AR aging report flagged Carag-aana Press as a slow-pay account — 78 days outstanding. Reached out to Pell Halloran in their accounts payable team. The Invoice Parsing pipeline auto-routed three of their invoices to the manual review queue because the line items didn't match the PO from the Tartine Lab subcontract."}
+{"id": "ent_005", "text": "Fenra workshopped the third act of the Aerwyn cycle with the user. Saela's arc reaches its turning point in the Hollowmounts, and the Wends people are critical witnesses to her decision. The user wants to set the climax in Bryn-iili, which I think is a strong choice given the salt-debt motif established in chapter four."}
+{"id": "ent_006", "text": "The Topic Clustering pipeline crashed during the overnight indexing. Aria pulled the logs and traced it to a memory leak in the HDBSCAN library — specifically the joblib backend on the Crestmoor compute cluster. Filed an issue with the upstream maintainers, copied Mette Olafsen who has been the main contact at Crestmoor IT."}
+{"id": "ent_007", "text": "Bramble's daily log: morning visit to Bridgewater Community Garden to assess the squash bug situation. Karis Tornau, the volunteer coordinator, walked me through their integrated pest management plan. Afternoon: helped Ralf Ginder design a Native Species border for his property out in the Wendelsea district. Evening: read through a paper from the Hollowmounts Phenology Network on first-bloom shifts."}
+{"id": "ent_008", "text": "Solas reviewed a PR from Aerwyn Labs. Theirs is a parser combinator library that builds on top of Megaparsec, designed for the Type Systems project. The maintainer, Pol Krisat, was very responsive. Need to mention this to the team at the Crestmoor Compiler Group — they were looking for something similar."}
+{"id": "ent_009", "text": "Thresh worked on the IFRS adoption for Tartine Lab. They're transitioning from local GAAP and the Cashflow Models we built last quarter need to be adjusted for the new lease accounting under IFRS 16. Coordinating with their CFO Ivora Tinn and her external auditor Bek Halloran from Wendelsea Audit Partners."}
+{"id": "ent_010", "text": "Aria put together a reading list for the new postdoc, Ren Solanke, who's joining the Meta-Cognition project at Crestmoor. Started with the foundational papers from Bridgewater Cognitive Science, then added the Hollowmounts Institute series on self-referential prompting, then the recent Aerwyn Labs preprint on chain-of-thought stability."}
+{"id": "ent_011", "text": "Fenra and the user spent an hour on the Dialogue Engine. The current relationship-graph model doesn't handle one specific edge case: when two characters from feuding factions (the Wends and the Drukar) are forced into cooperation. Drafted a workaround. Saela's voice in particular needs to soften when she speaks to Hellis Mar — there's history there that the engine doesn't capture."}
+{"id": "ent_012", "text": "Bramble identified a fungal issue on the heritage apples at Wendelsea Orchard. Apple scab, classic. The orchard manager, Doreth Ainsleigh, hadn't been doing dormant copper sprays. Worked through a transition plan with her. She mentioned that Crestmoor Cooperative Extension is running a workshop next month and we both agreed she should attend."}
+{"id": "ent_013", "text": "Solas debugged a segfault in the Type Systems repo that only triggered on aarch64. The fix turned out to involve LLVM intrinsics that behaved differently across targets. Filed an upstream issue with the LLVM maintainers; Brennan Lyle from the Aerwyn Compiler Group offered to bisect on his end. We'll compare notes at the Crestmoor systems meetup next Thursday."}
+{"id": "ent_014", "text": "Thresh helped Pell Halloran reconcile a complicated three-way match between PO, receiving doc, and invoice from Hollowmounts Logistics. The discrepancy turned out to be a unit-of-measure mismatch — the PO was in cases, the invoice in eaches. Updated the Invoice Parsing rules to flag UoM mismatches automatically going forward."}
+{"id": "ent_015", "text": "Aria reviewed three statistical-tests questions from Saela in a row. The third was the trickiest: whether to use a paired or unpaired test for matched-but-not-identical samples. Walked her through the implications. She's preparing a revision for the Tartine Lab journal and the deadline is Friday."}
+{"id": "ent_016", "text": "Fenra worked on naming conventions for the Drukar people of the Aerwyn cycle. Drafted phonological rules. Hellis Mar — a Drukar character central to chapter eight — is the empathetic exception, and her name needs to bridge both the Drukar and Wendish sound-systems. Sent the working document to the Wendelsea Writing Group, and copied Saela who has a sharp ear for invented languages from her work at Bridgewater Cognitive Science."}
+{"id": "ent_017", "text": "Bramble's Pollinator Paths consultation with the Bridgewater Schools garden committee. They want native-only plantings but have a strict budget. Recommended starting with Wendelsea Native Plant Nursery's plug trays — cheaper than gallon containers and they establish faster. Spoke with their founder, Iset Karadzic, who agreed to give the schools their nonprofit rate."}
+{"id": "ent_018", "text": "Solas paired with Pol Krisat on integrating the new parser combinator library into the Aerwyn Labs codebase. The integration was clean except for one place where their custom error type clashed with our Distributed Tracing context. Resolved by introducing a small adapter trait. Pushed the integration as a separate PR for easier review."}
+{"id": "ent_019", "text": "Thresh sat in on a planning meeting with the leadership of Bridgewater Studio. Their CFO Ivora Tinn is rolling out a new Cashflow Models dashboard for the executive team. Operations director Karis Tornau wants drill-down by department; Ivora wants high-level summaries only. Compromise: two views, same underlying data."}
+{"id": "ent_020", "text": "Aria explored the relationship between attention entropy and reasoning faithfulness. The Hollowmounts Institute paper from last year claimed they were positively correlated; the Aerwyn Labs replication found the opposite. Drafted a synthesis that argues both papers are right but for different model scales. Need to verify this on the Crestmoor cluster before claiming anything."}
+{"id": "ent_021", "text": "Fenra outlined the Aerwyn cycle's appendix on language. Three families: Wendish (the Wends speak it), Drukic (the Drukar), and the older Salt Tongue, which only the priests of the Hollowmounts use ceremonially. Traced sound shifts between Wendish and Salt Tongue. The user wants Saela to speak some Salt Tongue in chapter twelve."}
+{"id": "ent_022", "text": "Bramble's morning was a pollinator survey at the Wendelsea Botanic Garden. Counted seven species of native bee, including the rare Crestmoor Mining Bee, which Yumara Felk identified by the abdominal banding. Important data for the Pollinator Paths baseline. Yumara is publishing the survey results with the Hollowmounts Phenology Network."}
+{"id": "ent_023", "text": "Solas helped Mette Olafsen, the new SRE at Crestmoor, set up Distributed Tracing for the Aerwyn Telemetry stack. The hard part was getting the OpenTelemetry collector to handle the mixed Rust and OCaml workloads. Settled on using OTLP HTTP export so we didn't have to deal with gRPC version skew across services."}
+{"id": "ent_024", "text": "Thresh worked through depreciation on Bridgewater Studio's new equipment. Ivora Tinn was pushing for Section 179 to expense the whole $180k in year one. Ran the numbers — they don't have enough taxable income to absorb it without phase-out issues. Recommended bonus depreciation instead, which doesn't have the income limitation. She agreed."}
+{"id": "ent_025", "text": "Aria and the user had a long conversation about the Embedding Spaces visualization tool. The user, Saela, wants to add a temporal slider so she can replay how clusters formed over the indexing period. Sketched the API. Coordinating with Brennan Lyle on the rendering side — he has experience with WebGL from his time at Bridgewater Visualization."}
+{"id": "ent_026", "text": "Fenra thought through how to handle internal monologue for Hellis Mar without breaking the third-person POV established in the rest of the Aerwyn cycle. Three options. Settled on a hybrid: deep interiority in dialogue scenes, surface description elsewhere. The Wendelsea Writing Group might have feedback on whether this is too jarring."}
+{"id": "ent_027", "text": "Bramble walked through the soil pH issue at Doreth Ainsleigh's blueberry patch with her. Iron chlorosis from drift toward neutral. Her soil is naturally on the alkaline side — Wendelsea district is on limestone bedrock. Recommended sulfur applications and moving the new plantings to a raised bed with imported acid soil."}
+{"id": "ent_028", "text": "Solas reviewed Pol Krisat's PR refactoring the Type Systems error reporting. The new design uses spans-with-labels, similar to rustc but with our row-polymorphism extensions. Suggested a couple of improvements to the diagnostic formatting. Bek Halloran (different Bek — this one's a contributor not the auditor) chimed in with a perf concern. Mostly resolved but one comment thread is still open."}
+{"id": "ent_029", "text": "Thresh's quarterly close for Tartine Lab. The Cashflow Models predicted a shortfall in week 9 that didn't materialize because Hollowmounts Logistics paid early. The model isn't broken — it's just that early payments have a fat right tail. Updated the model's distribution assumptions and re-ran. Sent the revised forecast to Ivora Tinn."}
+{"id": "ent_030", "text": "Aria, the user wants to spin up a new project — call it Faithful Chains — for the chain-of-thought stability research. Distinct enough from Meta-Cognition that I think it warrants its own space. Will coordinate with Ren Solanke and we'll cite the Hollowmounts Institute paper as a starting point. Funding via the Crestmoor Cognitive Science grant."}
+{"id": "ent_031", "text": "Fenra's daily log: writing day. Got 2,200 words on the Saela / Hellis Mar confrontation in chapter eleven. Difficult scene — the Salt Tongue exchanges had to feel real even though we don't translate them on-page. Sent the draft to the Wendelsea Writing Group reader, Karis Tornau (who reads fiction outside her day job, useful)."}
+{"id": "ent_032", "text": "Bramble visited the Hollowmounts Institute experimental garden. Their Native Species program is doing remarkable work with seed-saving for regional ecotypes. Met with their lead horticulturist, Iset Karadzic — same Iset as the nursery, she consults with the Institute as well. She gave me a packet of cardinal flower seed from the local provenance."}
+{"id": "ent_033", "text": "Solas helped a user new to the project — Pol Krisat introduced them — set up a dev environment for the Aerwyn Labs compiler. They were on Windows and ran into the usual cross-compilation issues with our LLVM toolchain. Pointed them at the Crestmoor Systems wiki for the Windows-specific setup steps. They got it building in about an hour."}
+{"id": "ent_034", "text": "Thresh and Ivora Tinn discussed Bridgewater Studio's cap table. They're doing a small secondary sale and need to clean up the prior round's accounting before the next investor (Aerwyn Capital) does diligence. Some shares were issued without proper 83(b) elections. Recommended she loop in their securities attorney from Crestmoor Legal Group before any further action."}
+{"id": "ent_035", "text": "Aria spent the afternoon on a literature review of contrastive embedding objectives. The user, Saela, wanted a one-page summary connecting SimCLR, BYOL, and Barlow Twins under the spectral kernel framing. Pulled in references from Tartine Lab and the Aerwyn Labs preprints. Bridgewater Cognitive Science also has a relevant 2023 paper that I added at the end."}
+{"id": "ent_036", "text": "Fenra discussed the Aerwyn cycle's mythological underpinnings with the user. The Mother of Salt myth has parallels in the Hollowmounts oral tradition. Researched Welsh and Finnish mythological motifs and synthesized a version that feels native to Aerwyn rather than borrowed. The Wendelsea Writing Group will read it next week."}
+{"id": "ent_037", "text": "Bramble conducted a soil microbe assessment at the Bridgewater Community Garden. Their compost is excellent — high microbial diversity, good fungal-to-bacterial ratio for the vegetable beds. Karis Tornau wants to scale this to the satellite gardens in Crestmoor and Wendelsea. The Pollinator Paths corridor we designed last year is also performing well, with bee diversity up 40%."}
+{"id": "ent_038", "text": "Solas worked through a thread-safety audit for the Distributed Tracing client library. Found two race conditions that ThreadSanitizer hadn't caught — they only manifested at very high concurrency on the Crestmoor production stack. Mette Olafsen confirmed she could reproduce. Pushed fixes; both reviewed by Brennan Lyle."}
+{"id": "ent_039", "text": "Thresh's invoice parsing review with Pell Halloran of Carag-aana Press. Their AP is moving away from PDF invoices toward a structured ZUGFeRD-style format. Updated our Invoice Parsing pipeline to handle the embedded XML. The transition will save Carag-aana about $4k a year in our processing fees, which Pell appreciated."}
+{"id": "ent_040", "text": "Aria reviewed Saela's draft introduction for the Embedding Spaces paper. Strong technical content but the framing buries the lede — the novel contribution should be in the second sentence, not the second page. Recommended a structural reorder. Saela's submitting to the Tartine Lab journal next week so we have limited revision time."}
+{"id": "ent_041", "text": "Fenra and the user plotted out the geography of the Drukar territories. The Drukar live south of the Hollowmounts, in a region called the Salt Flats. Their major settlement, Vroth-Karadz, sits at the edge of a dry lake. Hellis Mar is from a smaller village, Krast-Endel, three days' ride from the capital."}
+{"id": "ent_042", "text": "Bramble's Native Species work with Iset Karadzic at Wendelsea Native Plant Nursery is paying off. Their plug-tray production is up 60% this season and they're shipping to Crestmoor Conservation Trust restoration projects across the region. The Hollowmounts Institute is now sourcing seeds from her ecotype collection too."}
+{"id": "ent_043", "text": "Solas wrote up a postmortem for last week's Distributed Tracing outage at Crestmoor. Root cause: a bad config push that disabled the OTLP exporter for 47 minutes. Mette Olafsen led the response; Brennan Lyle wrote the runbook update. Adding a config-validation step to CI to prevent recurrence. Filed the postmortem in the Aerwyn Telemetry incident wiki."}
+{"id": "ent_044", "text": "Thresh helped Doreth Ainsleigh of Wendelsea Orchard with her Schedule F. She has both fruit sales and agritourism revenue and was unsure how to handle the latter. Walked through the relevant IRS guidance — agritourism is generally a separate business activity. Recommended she discuss with her CPA before filing. Pell Halloran (no relation, weirdly common name in Wendelsea) was the CPA she ended up using."}
+{"id": "ent_045", "text": "Aria explored whether the Meta-Cognition project's findings would extend to multimodal models. The Hollowmounts Institute has been running experiments on this with their internal vision-language model. Set up a meeting with Ren Solanke for next week to compare notes. The Bridgewater Cognitive Science group has parallel work that's worth citing."}
+{"id": "ent_046", "text": "Fenra put together a glossary for the Aerwyn appendices. Twenty-three terms so far, ranging from cultural concepts (salt-debt, the Wendelsea binding) to geographical (the Hollowmounts, the Salt Flats, Bryn-iili, Vroth-Karadz). The Wendelsea Writing Group's beta readers reported the glossary helped them keep track during chapter ten."}
+{"id": "ent_047", "text": "Bramble's pollinator survey with Yumara Felk identified a new colony of the Crestmoor Mining Bee in the Bridgewater Community Garden. Significant — the species was thought to be restricted to the Wendelsea district. Notified the Hollowmounts Phenology Network so they can update their range maps. Karis Tornau is excited about the volunteer engagement opportunity."}
+{"id": "ent_048", "text": "Solas paired with Pol Krisat on the Type Systems row-polymorphism implementation. Their algorithm handles the kind-checking edge case I'd flagged last week. Tested it against the Aerwyn Labs corpus and it accepts every previously-accepted program plus a handful that were rejected by the old algorithm. Bek Halloran (the contributor, not the auditor) reviewed the diff."}
+{"id": "ent_049", "text": "Thresh walked through Aerwyn Capital's diligence questions for Bridgewater Studio. They want clean Cashflow Models for the last 24 months, audited financials (Wendelsea Audit Partners has those), and the cap table reconciliation Ivora Tinn and I have been working on. Tight timeline — they want it all by month-end. Coordinated with Bek Halloran from the audit team."}
+{"id": "ent_050", "text": "Aria summarized the day's research for Saela. Three key findings: the Tartine Lab paper's claims about chain-of-thought faithfulness don't replicate cleanly on smaller models; the Hollowmounts Institute's vision-language extension is promising but incomplete; and the Aerwyn Labs preprint on attention entropy has a subtle methodological issue that needs flagging. Drafted a memo for the Crestmoor Cognitive Science weekly."}

benchmarks/model_eval/datasets/entity_extraction/dataset.ko.jsonl

@@ -0,0 +1,50 @@
+{"id": "ent_001", "text": "Aria는 Tartine Lab의 검색 증강 평가에 관한 논문이 마침내 arXiv에 올라왔다고 언급했다. Saela는 이 논문을 기다리고 있었는데, 그녀는 Embedding Spaces 프로젝트를 진행 중이며 자신의 연구팀이 곧 내놓을 프리프린트에 이 논문을 인용하고 싶어 한다. 그 연구실은 Crestmoor에 있으며, Fenra가 예전에 컨설팅을 하던 Bridgewater Studio에서 길을 따라 조금 내려간 곳이다. 아무래도 주저자인 Gera Vossen은 다음 학기에 Hollowmounts Institute로 옮긴다고 한다."}
+{"id": "ent_002", "text": "Solas가 Distributed Tracing 프로젝트의 트레이스 전파 버그에 대한 수정사항을 푸시했다. Crestmoor Systems의 런타임 팀 소속 Brennan Lyle이 이를 리뷰했다. 해당 버그는 부하가 걸린 상황에서만 나타났기 때문에, Aerwyn Telemetry 컨소시엄의 스트레스 테스트가 우리 내부 CI보다 먼저 이를 발견했다."}
+{"id": "ent_003", "text": "Bramble은 Wendelsea Botanic Garden의 새로운 Pollinator Paths 통로 설계를 살펴보았다. 그곳의 상근 곤충학자인 Yumara Felk는 동쪽 가장자리에 산박하를 추가할 것을 제안했다. 이 프로젝트는 Crestmoor Conservation Trust의 자금 지원을 받고 있으며, 이 단체는 20년 동안 해당 지역의 토종 식물 관련 작업을 지원해오고 있다."}
+{"id": "ent_004", "text": "Thresh는 Bridgewater Studio의 장부를 마감했다. 그들의 AR 미수금 연령 보고서는 Carag-aana Press를 지연 납부 계정으로 표시했는데, 78일 미수 상태였다. 그들의 지급 담당 팀에 있는 Pell Halloran에게 연락했다. Invoice Parsing 파이프라인은 Tartine Lab 하도급 계약의 PO와 세부 항목이 일치하지 않아 그들의 송장 세 장을 수동 검토 대기열로 자동 전달했다."}
+{"id": "ent_005", "text": "Fenra가 사용자와 함께 Aerwyn 사이클의 제3막을 워크숍 형식으로 다듬었다. Saela의 이야기선은 Hollowmounts에서 전환점에 도달하며, Wends 사람들은 그녀의 결정에 있어 중요한 목격자들이다. 사용자가 Bryn-iili에서 클라이맥스를 설정하려는 것은 4장에서 설정된 소금-빚 모티프를 고려할 때 강력한 선택이라고 생각한다."}
+{"id": "ent_006", "text": "Topic Clustering 파이프라인이 야간 인덱싱 도중 크래시되었다. Aria가 로그를 확인한 결과, 원인은 HDBSCAN 라이브러리의 메모리 누수로, 구체적으로 Crestmoor 컴퓨팅 클러스터의 joblib 백엔드에서 발생한 것으로 추적되었다. 업스트림 메인테이너에게 이슈를 제기했고, Crestmoor IT의 주 담당자인 Mette Olafsen을 참조했다."}
+{"id": "ent_007", "text": "Bramble의 일일 기록: 오전에 Bridgewater Community Garden을 방문하여 호박벌레 상황을 평가했다. 자원봉사 코디네이터인 Karis Tornau가 그들의 통합 해충 관리 계획을 설명해 주었다. 오후: Wendelsea 지역에 있는 Ralf Ginder의 부지에 Native Species 경계를 설계하는 것을 도왔다. 저녁: Hollowmounts Phenology Network의 첫 개화 시기 변화에 관한 논문을 읽었다."}
+{"id": "ent_008", "text": "Solas가 Aerwyn Labs의 PR을 리뷰했다. 이것은 Megaparsec 위에 구축된 parser combinator 라이브러리로, Type Systems 프로젝트를 위해 설계되었다. 메인테이너인 Pol Krisat은 매우 적극적으로 대응했다. Crestmoor Compiler Group 팀에 이것을 언급해야 한다 — 그들도 비슷한 것을 찾고 있었다."}
+{"id": "ent_009", "text": "Thresh는 Tartine Lab의 IFRS 도입을 담당했습니다. 이들은 현지 GAAP에서 전환 중이며, 지난 분기에 우리가 구축한 Cashflow Models는 IFRS 16에 따른 새로운 리스 회계 기준에 맞게 조정되어야 합니다. 이들의 CFO인 Ivora Tinn과 Wendelsea Audit Partners의 외부 감사인 Bek Halloran과 협업하고 있습니다."}
+{"id": "ent_010", "text": "Aria는 Crestmoor의 Meta-Cognition 프로젝트에 합류할 새로운 박사후 연구원 Ren Solanke를 위해 읽기 목록을 만들었다. Bridgewater Cognitive Science의 기초 논문들로 시작하여, Hollowmounts Institute의 자기 참조 프롬프팅 시리즈를 추가했고, 최근 Aerwyn Labs의 연쇄적 사고 안정성에 관한 프리프린트를 더했다."}
+{"id": "ent_011", "text": "Fenra와 사용자는 Dialogue Engine에 한 시간을 할애했다. 현재 관계 그래프 모델은 한 가지 특정 엣지 케이스를 처리하지 못한다. 바로 적대적인 세력(the Wends와 the Drukar)에 속한 두 캐릭터가 협력을 강요받는 경우이다. 임시 해결책을 초안했다. 특히 Saela가 Hellis Mar에게 말할 때는 그녀의 목소리가 부드러워져야 한다 — 엔진이 포착하지 못하는 과거사가 있기 때문이다."}
+{"id": "ent_012", "text": "Bramble은 Wendelsea Orchard의 전통 품종 사과에서 진균성 문제를 발견했다. 사과 붉은별무늬병이었고, 아주 전형적인 경우였다. 과수원 관리인 Doreth Ainsleigh는 동면기에 구리제를 살포하지 않고 있었다. 그녀와 함께 전환 계획을 세웠다. 그녀는 Crestmoor Cooperative Extension이 다음 달 워크숍을 개최한다고 언급했고, 우리 둘 다 그녀가 참석해야 한다는 데 동의했다."}
+{"id": "ent_013", "text": "Solas는 Type Systems 저장소에서 aarch64에서만 발생하는 segfault를 디버깅했다. 수정 내용은 타겟에 따라 다르게 동작하는 LLVM intrinsics를 포함하는 것으로 밝혀졌다. LLVM 메인테이너들에게 upstream 이슈를 제출했고, Aerwyn Compiler Group의 Brennan Lyle는 자신의 환경에서 bisect를 수행하겠다고 제안했다. 다음 주 목요일 Crestmoor 시스템 밋업에서 의견을 교환할 예정이다."}
+{"id": "ent_014", "text": "Thresh는 Pell Halloran이 Hollowmounts Logistics로부터의 PO, 입고 문서, 송장 간의 복잡한 3자 대조를 조정하도록 도왔다. 그 불일치는 단위 불일치였던 것으로 밝혀졌는데, PO는 케이스 단위였고 송장은 낱개 단위였다. 향후 UoM 불일치를 자동으로 플래그하도록 Invoice Parsing 규칙을 업데이트했다."}
+{"id": "ent_015", "text": "Aria는 Saela가 낸 통계 검정 문제를 세 개 연속으로 검토했다. 세 번째가 가장 까다로웠는데, 짝을 이루지만 동일하지는 않은 표본에 대해 대응 표본 검정과 독립 표본 검정 중 어느 것을 사용해야 하는지에 관한 문제였다. 그 의미와 영향을 차근차근 설명해 주었다. 그녀는 Tartine Lab 저널에 게재할 수정본을 준비하고 있으며, 마감일은 금요일이다."}
+{"id": "ent_016", "text": "Fenra는 Aerwyn 사이클의 Drukar 민족을 위한 작명 규칙을 작업했다. 음운 규칙 초안을 작성했다. Hellis Mar — 8장의 핵심이 되는 Drukar 캐릭터 — 는 공감적인 예외이며, 그녀의 이름은 Drukar와 Wendish 음운 체계 양쪽을 잇는 다리가 되어야 한다. 작업 문서를 Wendelsea Writing Group에 보냈고, Bridgewater Cognitive Science에서의 업무 경험으로 창조된 언어에 귀가 밝은 Saela도 참조에 넣었다."}
+{"id": "ent_017", "text": "Bramble의 Pollinator Paths 컨설팅이 Bridgewater Schools 정원 위원회와 진행되었다. 이들은 토종 식물만으로 조성하기를 원하지만 예산이 빡빡하다. Wendelsea Native Plant Nursery의 플러그 트레이부터 시작하는 것을 권장했다 — 갤런 화분보다 저렴하고 정착도 더 빠르다. 이들의 창립자 Iset Karadzic와 이야기를 나눴는데, 그는 학교들에 비영리 단체 요금을 적용해 주기로 동의했다."}
+{"id": "ent_018", "text": "Solas는 Pol Krisat와 함께 새 parser combinator 라이브러리를 Aerwyn Labs 코드베이스에 통합하는 작업을 진행했습니다. 통합은 대체로 깔끔했으나, 한 곳에서 그들의 커스텀 에러 타입이 우리의 Distributed Tracing 컨텍스트와 충돌하는 문제가 있었습니다. 작은 어댑터 trait를 도입하여 해결했으며, 리뷰를 용이하게 하기 위해 통합 내용을 별도의 PR로 푸시했습니다."}
+{"id": "ent_019", "text": "Thresh는 Bridgewater Studio 경영진과의 기획 회의에 참석했다. CFO인 Ivora Tinn은 임원진을 위해 새로운 Cashflow Models 대시보드를 도입하고 있다. 운영 이사 Karis Tornau는 부서별 드릴다운을 원하는 반면, Ivora는 고수준 요약만을 원한다. 절충안은 동일한 기저 데이터를 사용하는 두 가지 뷰이다."}
+{"id": "ent_020", "text": "Aria는 attention entropy와 reasoning faithfulness의 관계를 탐구했다. 작년 Hollowmounts Institute의 논문은 이 둘이 양의 상관관계에 있다고 주장했고, Aerwyn Labs의 재현 연구는 반대 결과를 얻었다. 두 논문이 서로 다른 모델 규모에서는 모두 옳다는 주장을 담은 종합안을 작성했다. 무엇이든 주장하기 전에 Crestmoor 클러스터에서 이를 검증해야 한다."}
+{"id": "ent_021", "text": "Fenra는 Aerwyn 연대기의 언어 부록을 정리했다. 세 가지 계열: Wendish(Wends가 사용하는 언어), Drukic(Drukar), 그리고 Hollowmounts의 사제들만 의례적으로 사용하는 더 오래된 Salt Tongue. Wendish와 Salt Tongue 사이의 음운 변화를 추적했다. 사용자는 12장에서 Saela가 Salt Tongue를 조금 말하기를 원한다."}
+{"id": "ent_022", "text": "Bramble의 아침은 Wendelsea Botanic Garden에서의 수분 매개체 조사였다. 일곱 종의 토종 벌을 세었는데, 희귀한 Crestmoor Mining Bee도 포함되어 있으며, 이는 Yumara Felk가 복부 띠 무늬로 식별한 것이다. Pollinator Paths 기준 데이터에 중요한 자료다. Yumara는 Hollowmounts Phenology Network와 함께 조사 결과를 발표하고 있다."}
+{"id": "ent_023", "text": "Solas는 Crestmoor의 신입 SRE인 Mette Olafsen이 Aerwyn Telemetry 스택에 Distributed Tracing을 구축하는 것을 도왔다. 가장 어려웠던 점은 OpenTelemetry collector가 Rust와 OCaml이 혼재된 워크로드를 처리하도록 만드는 것이었다. 서비스 간 gRPC 버전 불일치를 다루지 않아도 되도록 OTLP HTTP export를 사용하기로 결정했다."}
+{"id": "ent_024", "text": "Thresh는 Bridgewater Studio의 신규 장비에 대한 감가상각을 검토했다. Ivora Tinn는 1년차에 전체 $180k를 비용처리하기 위해 Section 179 적용을 밀어붙이고 있었다. 계산해 보니 — phase-out 문제는 차치하고서도 이를 공제할 만큼 충분한 과세소득이 없었다. 대신 소득 제한이 없는 추가 감가상각을 권장했다. 그녀는 동의했다."}
+{"id": "ent_025", "text": "Aria와 사용자는 Embedding Spaces 시각화 도구에 대해 오랫동안 대화를 나눴다. 사용자인 Saela는 인덱싱 기간 동안 클러스터가 형성되는 과정을 재생할 수 있도록 시간 슬라이더를 추가하고 싶어한다. API를 대략 설계했으며, 렌더링 측면에서는 Brennan Lyle과 협업하고 있다. 그는 Bridgewater Visualization에서 일할 때 WebGL 경험이 있다."}
+{"id": "ent_026", "text": "Fenra는 Aerwyn 사이클의 나머지 부분에서 확립된 3인칭 POV를 깨뜨리지 않으면서 Hellis Mar의 내면 독백을 처리하는 방법을 고민했다. 세 가지 선택지. 하이브리드 방식으로 결정했다: 대화 장면에서는 깊은 내면성을, 다른 곳에서는 표면적 묘사를. Wendelsea Writing Group이 이것이 너무 어색한지에 대해 피드백을 줄지도 모른다."}
+{"id": "ent_027", "text": "Bramble은 Doreth Ainsleigh의 블루베리 밭을 그녀와 함께 둘러보며 토양 pH 문제를 살펴보았다. 중성 쪽으로 변하면서 생긴 철분 엽록소증이었다. 그녀의 토양은 원래 알칼리성 편인데, Wendelsea 지구가 석회암 반암석 위에 있기 때문이다. 유황을 시비하고 새로 심은 묘목을 외부에서 들여온 산성 토양을 채운 높은 화단으로 옮기는 것을 권장했다."}
+{"id": "ent_028", "text": "Solas가 Type Systems의 오류 보고를 리팩토링한 Pol Krisat의 PR을 리뷰했다. 새 설계는 spans-with-labels를 사용하며, rustc와 유사하지만 우리의 row-polymorphism 확장 기능이 추가되었다. diagnostic formatting에 몇 가지 개선점을 제안했다. Bek Halloran(다른 Bek이다 — 이쪽은 감사원이 아닌 기여자)이 perf 우려를 제기했다. 대부분 해결되었지만 하나의 comment thread는 아직 열려 있다."}
+{"id": "ent_029", "text": "Thresh의 Tartine Lab 분기 결산. Cashflow Models는 9주차에 자금 부족을 예측했으나, Hollowmounts Logistics가 조기 지급하여 실제로는 발생하지 않았다. 모델이 잘못된 것은 아니다 — 조기 지급이 fat right tail을 가지기 때문이다. 모델의 분포 가정을 업데이트하고 재실행했다. 수정된 예측을 Ivora Tinn에게 전송했다."}
+{"id": "ent_030", "text": "Aria, 사용자가 새 프로젝트를 시작하길 원하는데 — 이름은 Faithful Chains로 하자고 해 — chain-of-thought 안정성 연구를 위해서야. Meta-Cognition과 충분히 구별되니까 별도의 공간이 필요하다고 생각해. Ren Solanke와 협력할 예정이고, 시작점으로 Hollowmounts Institute 논문을 인용할 거야. 자금은 Crestmoor Cognitive Science 보조금으로 조달할 거야."}
+{"id": "ent_031", "text": "Fenra의 일일 기록: 작문일. 11장의 Saela / Hellis Mar 대결 장면을 2,200단어로 썼다. 어려운 장면이다 — Salt Tongue 대화는 지면에서 번역하지 않더라도 실제처럼 느껴져야 했다. 초고를 Wendelsea Writing Group 독자 Karis Tornau에게 보냈다(본업 외에 소설을 읽는 사람이라 유용하다)."}
+{"id": "ent_032", "text": "Bramble는 Hollowmounts Institute의 실험 정원을 방문했다. 그들의 Native Species 프로그램은 지역 생태형을 위한 종자 보존에서 놀라운 성과를 내고 있다. 그곳의 수석 원예사인 Iset Karadzic를 만났는데, 묘목원의 그 Iset과 같은 인물로, 그녀는 이 연구소에도 자문을 제공하고 있다. 그녀는 현지 종원의 붉은꽃버들 씨앗 한 봉지를 나에게 주었다."}
+{"id": "ent_033", "text": "Solas가 Pol Krisat이 소개해 준 프로젝트 신규 사용자를 도와 Aerwyn Labs 컴파일러의 개발 환경을 구축했다. 해당 사용자는 Windows를 사용하고 있었고, 우리 LLVM 툴체인에서 흔히 발생하는 크로스 컴파일 문제에 부딪혔다. Solas는 Crestmoor Systems 위키에 있는 Windows 전용 설정 단계를 참고하도록 안내했다. 약 한 시간 만에 빌드를 완료했다."}
+{"id": "ent_034", "text": "Thresh와 Ivora Tinn은 Bridgewater Studio의 cap table에 대해 논의했다. 이들은 소규모 secondary sale을 진행 중이며, 다음 투자자(Aerwyn Capital)가 diligence를 진행하기 전에 전 라운드의 회계를 정리할 필요가 있다. 일부 주식은 적절한 83(b) elections 없이 발행되었다. 추가 조치를 취하기 전에 Crestmoor Legal Group의 securities attorney를 참여시키라고 그녀에게 권고했다."}
+{"id": "ent_035", "text": "Aria는 대조적 임베딩 목적함수에 대한 문헌 검토를 오후 내내 진행했다. 사용자인 Saela는 spectral kernel framing 하에서 SimCLR, BYOL, Barlow Twins를 연결하는 한 페이지 분량의 요약을 원했다. Tartine Lab과 Aerwyn Labs 프리프린트의 참고문헌을 가져왔고, Bridgewater Cognitive Science의 관련 2023년 논문도 마지막에 추가했다."}
+{"id": "ent_036", "text": "Fenra는 사용자와 Aerwyn 주기의 신화적 기반에 대해 논의했다. Mother of Salt 신화는 Hollowmounts 구전 전통과 유사성을 지닌다. 웨일스와 핀란드 신화 모티프를 연구하고, Aerwyn에 토착적인 느낌이 들며 차용한 것처럼 보이지 않는 버전을 종합했다. Wendelsea Writing Group이 다음 주에 이를 읽을 예정이다."}
+{"id": "ent_037", "text": "Bramble이 Bridgewater 커뮤니티 가든에서 토양 미생물 평가를 시행했다. 그들의 퇴비는 훌륭한데, 미생물 다양성이 높고 채소밭에 적합한 균류와 세균의 비율이 좋다. Karis Tornau는 이를 Crestmoor와 Wendelsea의 위성 정원으로 확대하려 한다. 작년에 우리가 설계한 Pollinator Paths 통로도 성과가 좋으며, 벌의 다양성이 40% 증가했다."}
+{"id": "ent_038", "text": "Solas가 Distributed Tracing 클라이언트 라이브러리에 대한 스레드 안전성 감사를 진행했다. ThreadSanitizer가 놓친 두 가지 레이스 컨디션을 발견했다. 이는 Crestmoor 프로덕션 스택에서 매우 높은 동시성에서만 나타났다. Mette Olafsen이 재현할 수 있음을 확인했다. 수정 사항을 푸시했으며, 두 가지 모두 Brennan Lyle이 리뷰했다."}
+{"id": "ent_039", "text": "Thresh의 Carag-aana Press 소속 Pell Halloran과의 Invoice Parsing 리뷰. 해당 회사의 AP는 PDF 인보이스에서 구조화된 ZUGFeRD 스타일 형식으로 전환 중이다. 내장된 XML을 처리할 수 있도록 우리의 Invoice Parsing 파이프라인을 업데이트했다. 이번 전환으로 Carag-aana는 우리 처리 수수료에서 연간 약 $4k를 절약하게 될 것이며, Pell은 이 점을 높이 평가했다."}
+{"id": "ent_040", "text": "Aria는 Embedding Spaces 논문의 Saela 초안 서론을 검토했다. 기술적 내용은 탄탄하지만 서술 구조가 핵심을 뒤로 미룬다——새로운 기여는 두 번째 문장에 있어야지, 두 번째 페이지에 있어서는 안 된다. 구조적 재정렬을 권고했다. Saela가 다음 주에 Tartine Lab 저널에 제출할 예정이므로 수정 시간이 제한적이다."}
+{"id": "ent_041", "text": "Fenra와 사용자는 Drukar 영토의 지리를 파악했다. Drukar는 Hollowmounts 남쪽, Salt Flats라 불리는 지역에 산다. 그들의 주요 정착지인 Vroth-Karadz는 마른 호숫가에 위치해 있다. Hellis Mar는 수도에서 사흘 거리에 있는 더 작은 마을 Krast-Endel 출신이다."}
+{"id": "ent_042", "text": "Bramble의 Native Species 작업은 Wendelsea Native Plant Nursery에서 Iset Karadzic와 함께 결실을 보고 있다. 이번 시즌 plug-tray 생산이 60% 증가했으며, 지역 전역의 Crestmoor Conservation Trust 복원 프로젝트에 출하하고 있다. Hollowmounts Institute도 이제 그녀의 ecotype 컬렉션에서 씨앗을 조달하고 있다."}
+{"id": "ent_043", "text": "Solas는 지난주 Crestmoor에서 발생한 Distributed Tracing 장애에 대한 사후 분석 문서를 작성했다. 근본 원인은 잘못된 설정 배포로 인해 OTLP 익스포터가 47분 동안 비활성화된 것이다. Mette Olafsen이 대응을 주도했고, Brennan Lyle이 대응 절차 문서를 업데이트했다. 재발 방지를 위해 CI에 설정 검증 단계를 추가한다. 해당 사후 분석 문서를 Aerwyn Telemetry 인시던트 위키에 등록했다."}
+{"id": "ent_044", "text": "Thresh는 Wendelsea Orchard의 Doreth Ainsleigh가 Schedule F를 작성하는 것을 도왔다. 그녀는 과일 판매와 agritourism 수익을 모두 가지고 있었고, 후자를 어떻게 처리해야 할지 확신이 없었다. 관련 IRS 지침을 함께 살펴보았는데, agritourism은 일반적으로 별도의 사업 활동이다. 제출하기 전에 CPA와 상의할 것을 권장했다. Pell Halloran(친족 관계는 없으며, Wendelsea에서는 이상하게도 흔한 이름이다)이 그녀가 최종적으로 사용한 CPA였다."}
+{"id": "ent_045", "text": "Aria는 Meta-Cognition 프로젝트의 연구 결과가 멀티모달 모델에도 확장되는지 탐구했다. Hollowmounts Institute에서 자체 비전-언어 모델을 이용해 이와 관련된 실험을 진행해 왔다. 다음 주에 Ren Solanke와의 의견 교환 회의를 잡아라. Bridgewater Cognitive Science group의 유사한 연구는 인용할 가치가 있다."}
+{"id": "ent_046", "text": "Fenra는 Aerwyn 부록용 용어집을 정리했다. 지금까지 스물세 개의 용어가 수록되었는데, 문화적 개념(salt-debt, the Wendelsea binding)에서 지리적 개념(the Hollowmounts, the Salt Flats, Bryn-iili, Vroth-Karadz)에 이르기까지 다양하다. Wendelsea Writing Group의 베타 독자들은 10장을 읽는 동안 용어집이 내용을 따라가는 데 도움이 되었다고 보고했다."}
+{"id": "ent_047", "text": "Bramble와 Yumara Felk의 수분자 조사가 Bridgewater Community Garden에서 Crestmoor Mining Bee의 새로운 군락을 확인했다. 이는 중대한 발견이다 — 이 종은 Wendelsea 지구에만 서식하는 것으로 알려져 있었다. Hollowmounts Phenology Network에 통보하여 분포 지도를 업데이트할 수 있도록 했다. Karis Tornau는 자원봉사 참여 기회에 대해 매우 기대하고 있다."}
+{"id": "ent_048", "text": "Solas는 Pol Krisat와 함께 Type Systems의 row-polymorphism 구현을 진행했다. 이들의 알고리즘은 내가 지난주에 지적했던 kind-checking 엣지 케이스를 처리한다. Aerwyn Labs 코퍼스로 테스트한 결과, 이전에 수용되던 모든 프로그램은 물론 이전 알고리즘에서 거부되던 일부 프로그램도 수용한다. Bek Halloran(기여자이며 감사원은 아님)이 diff를 리뷰했다."}
+{"id": "ent_049", "text": "Thresh는 Aerwyn Capital이 Bridgewater Studio에 대해 제시한 실사 질문들을 검토했다. 이들은 지난 24개월간의 정비된 Cashflow Models와, 감사 완료 재무제표(Wendelsea Audit Partners가 이를 보유하고 있음), 그리고 Ivora Tinn과 내가 작업해 온 지분 구조표 조정 자료를 요구하고 있다. 일정이 매우 촉박하여 이 모든 것을 월말까지 제출받기를 원한다. 감사팀의 Bek Halloran과 조율을 마쳤다."}
+{"id": "ent_050", "text": "Aria는 Saela를 위해 그날의 연구를 요약했다. 세 가지 핵심 발견: Tartine Lab 논문의 chain-of-thought faithfulness에 관한 주장은 더 작은 모델에서 깔끔하게 재현되지 않는다; Hollowmounts Institute의 vision-language 확장은 유망하지만 불완전하다; 그리고 Aerwyn Labs의 attention entropy에 관한 preprint에는 지적이 필요한 미묘한 방법론적 문제가 있다. Crestmoor Cognitive Science weekly를 위한 메모를 초안했다."}

benchmarks/model_eval/datasets/entity_extraction/dataset.pt-BR.jsonl

@@ -0,0 +1,50 @@
+{"id": "ent_001", "text": "Aria mencionou que o artigo Tartine Lab sobre avaliação aumentada por recuperação está finalmente em arXiv. Saela estava esperando por isso — ela está executando o projeto Embedding Spaces e quer citá-lo na próxima pré-impressão de seu grupo. O laboratório fica em Crestmoor, próximo a Bridgewater Studio, onde Fenra costumava consultar. Aparentemente, o autor principal, Gera Vossen, está migrando para o próximo mandato Hollowmounts Institute."}
+{"id": "ent_002", "text": "Solas enviou uma correção para o bug de propagação de rastreamento no projeto Distributed Tracing. Revisado por Brennan Lyle da equipe de tempo de execução em Crestmoor Systems. O bug só se manifestou sob carga, e é por isso que o teste de estresse do consórcio Aerwyn Telemetry o detectou antes do nosso CI interno."}
+{"id": "ent_003", "text": "Bramble percorreu o projeto do novo corredor Pollinator Paths para o Wendelsea Botanic Garden. O entomologista da equipe, Yumara Felk, sugeriu adicionar hortelã da montanha ao longo da borda leste. O projeto é financiado pelo Crestmoor Conservation Trust, que apoia o trabalho com plantas nativas na região há duas décadas."}
+{"id": "ent_004", "text": "Thresh fechou os livros para Bridgewater Studio. Seu relatório de envelhecimento AR sinalizou Carag-aana Press como uma conta de pagamento lento – 78 dias pendentes. Entrei em contato com Pell Halloran em sua equipe de contas a pagar. O pipeline Invoice Parsing encaminhou automaticamente três de suas faturas para a fila de revisão manual porque os itens de linha não correspondiam à ordem de compra do subcontrato Tartine Lab."}
+{"id": "ent_005", "text": "Fenra realizou um workshop do terceiro ato do ciclo Aerwyn com o usuário. O arco de Saela atinge seu ponto de virada no Hollowmounts, e o pessoal do Wends é testemunha crítica de sua decisão. O usuário deseja definir o clímax em Bryn-iili, o que considero uma escolha forte, dado o tema da dívida de sal estabelecido no capítulo quatro."}
+{"id": "ent_006", "text": "O pipeline Topic Clustering travou durante a indexação noturna. Aria extraiu os logs e rastreou-os até um vazamento de memória na biblioteca HDBSCAN – especificamente o back-end joblib no cluster de computação Crestmoor. Registrou um problema com os mantenedores upstream, copiou Mette Olafsen que tem sido o contato principal na TI Crestmoor."}
+{"id": "ent_007", "text": "Registro diário do Bramble: visita matinal ao Bridgewater Community Garden para avaliar a situação do bug de squash. Karis Tornau, o coordenador voluntário, me orientou sobre seu plano integrado de manejo de pragas. Tarde: ajudou Ralf Ginder a projetar uma borda Native Species para sua propriedade no distrito Wendelsea. Noite: leia um artigo do Hollowmounts Phenology Network sobre os primeiros turnos."}
+{"id": "ent_008", "text": "Solas revisou um PR de Aerwyn Labs. A biblioteca deles é uma biblioteca combinadora de analisador baseada no Megaparsec, projetada para o projeto Type Systems. O mantenedor, Pol Krisat, foi muito responsivo. Preciso mencionar isso para a equipe do Crestmoor Compiler Group – eles estavam procurando por algo semelhante."}
+{"id": "ent_009", "text": "Thresh trabalhou na adoção do IFRS para Tartine Lab. Eles estão fazendo a transição do GAAP local e o Cashflow Models que construímos no último trimestre precisa ser ajustado para a nova contabilidade de arrendamento sob o IFRS 16. Coordenação com seu CFO Ivora Tinn e seu auditor externo Bek Halloran de Wendelsea Audit Partners."}
+{"id": "ent_010", "text": "Aria elaborou uma lista de leitura para o novo pós-doutorado, Ren Solanke, que está ingressando no projeto Meta-Cognition em Crestmoor. Começou com os documentos fundamentais do Bridgewater Cognitive Science, depois adicionou a série Hollowmounts Institute sobre prompts autorreferenciais e, em seguida, a recente pré-impressão Aerwyn Labs sobre estabilidade da cadeia de pensamento."}
+{"id": "ent_011", "text": "Fenra e o usuário passaram uma hora no Dialogue Engine. O modelo atual de gráfico de relacionamento não lida com um caso extremo específico: quando dois personagens de facções rivais (o Wends e o Drukar) são forçados a cooperar. Elaborou uma solução alternativa. A voz de Saela, em particular, precisa suavizar quando ela fala com Hellis Mar – há uma história que o mecanismo não captura."}
+{"id": "ent_012", "text": "Bramble identificou um problema fúngico nas maçãs tradicionais em Wendelsea Orchard. Casca de maçã, clássica. O gerente do pomar, Doreth Ainsleigh, não estava aplicando pulverizações de cobre dormente. Trabalhei em um plano de transição com ela. Ela mencionou que Crestmoor Cooperative Extension realizará um workshop no próximo mês e nós dois concordamos que ela deveria participar."}
+{"id": "ent_013", "text": "Solas depurou um segfault no repositório Type Systems que foi acionado apenas em aarch64. A correção acabou envolvendo intrínsecos do LLVM que se comportavam de maneira diferente entre os alvos. Arquivou um problema upstream com os mantenedores do LLVM; Brennan Lyle do Aerwyn Compiler Group ofereceu-se para dividi-lo ao meio. Compararemos as notas no encontro de sistemas Crestmoor na próxima quinta-feira."}
+{"id": "ent_014", "text": "Thresh ajudou Pell Halloran a reconciliar uma complicada correspondência tripla entre pedido de compra, recebimento de documento e fatura de Hollowmounts Logistics. A discrepância acabou sendo uma incompatibilidade de unidades de medida – o pedido estava em caixas, a fatura em cada um. Atualizadas as regras Invoice Parsing para sinalizar incompatibilidades de UM automaticamente daqui para frente."}
+{"id": "ent_015", "text": "Aria revisou três questões de testes estatísticos de Saela consecutivas. A terceira foi a mais complicada: usar um teste pareado ou não pareado para amostras correspondentes, mas não idênticas. Expliquei-lhe as implicações. Ela está preparando uma revisão para a revista Tartine Lab e o prazo final é sexta-feira."}
+{"id": "ent_016", "text": "Fenra trabalhou nas convenções de nomenclatura para as pessoas Drukar do ciclo Aerwyn. Regras fonológicas elaboradas. Hellis Mar — um personagem Drukar central no capítulo oito — é a exceção empática, e seu nome precisa unir os sistemas de som Drukar e Wendish. Enviou o documento de trabalho para Wendelsea Writing Group e copiou Saela, que tem um ouvido aguçado para idiomas inventados, de seu trabalho em Bridgewater Cognitive Science."}
+{"id": "ent_017", "text": "Consulta Pollinator Paths de Bramble com o comitê de jardinagem Bridgewater Schools. Eles querem plantações exclusivamente nativas, mas têm um orçamento rigoroso. Recomendado começar com as bandejas de plugue do Wendelsea Native Plant Nursery - mais baratas que os galões e se estabelecem mais rapidamente. Falei com seu fundador, Iset Karadzic, que concordou em conceder às escolas sua taxa sem fins lucrativos."}
+{"id": "ent_018", "text": "Solas emparelhado com Pol Krisat na integração da nova biblioteca combinadora de analisador na base de código Aerwyn Labs. A integração foi limpa, exceto por um local onde o tipo de erro personalizado entrou em conflito com nosso contexto Distributed Tracing. Resolvido com a introdução de uma pequena característica de adaptador. Promovi a integração como um PR separado para facilitar a revisão."}
+{"id": "ent_019", "text": "Thresh participou de uma reunião de planejamento com a liderança do Bridgewater Studio. O CFO Ivora Tinn está lançando um novo painel Cashflow Models para a equipe executiva. O diretor de operações Karis Tornau deseja detalhamento por departamento; Ivora deseja apenas resumos de alto nível. Compromisso: duas visualizações, os mesmos dados subjacentes."}
+{"id": "ent_020", "text": "Aria explorou a relação entre entropia de atenção e fidelidade de raciocínio. O artigo Hollowmounts Institute do ano passado afirmou que eles estavam positivamente correlacionados; a replicação Aerwyn Labs encontrou o oposto. Elaborou uma síntese que argumenta que ambos os artigos estão certos, mas para escalas de modelos diferentes. É necessário verificar isso no cluster Crestmoor antes de reivindicar qualquer coisa."}
+{"id": "ent_021", "text": "Fenra descreveu o apêndice do ciclo Aerwyn sobre linguagem. Três famílias: Wendish (os Wends falam isso), Drukic (os Drukar) e o antigo Salt Tongue, que apenas os sacerdotes do Hollowmounts usam cerimonialmente. Mudanças de som rastreadas entre Wendish e Salt Tongue. O usuário deseja que Saela fale um pouco de Salt Tongue no capítulo doze."}
+{"id": "ent_022", "text": "A manhã do Bramble foi uma pesquisa de polinizadores no Wendelsea Botanic Garden. Contou sete espécies de abelhas nativas, incluindo a rara Abelha Mineira Crestmoor, que Yumara Felk identificou pelas faixas abdominais. Dados importantes para a linha de base Pollinator Paths. Yumara está publicando os resultados da pesquisa com o Hollowmounts Phenology Network."}
+{"id": "ent_023", "text": "Solas ajudou Mette Olafsen, o novo SRE em Crestmoor, a configurar Distributed Tracing para a pilha Aerwyn Telemetry. A parte difícil foi fazer com que o coletor OpenTelemetry lidasse com as cargas de trabalho mistas de Rust e OCaml. Decidimos usar a exportação HTTP OTLP para que não precisássemos lidar com a distorção da versão gRPC entre os serviços."}
+{"id": "ent_024", "text": "Thresh trabalhou na depreciação do novo equipamento do Bridgewater Studio. Ivora Tinn estava pressionando para que a Seção 179 gastasse todos os US$ 180 mil no primeiro ano. Analisei os números: eles não têm renda tributável suficiente para absorvê-la sem problemas de eliminação progressiva. Em vez disso, recomenda-se a depreciação do bônus, que não tem limitação de renda. Ela concordou."}
+{"id": "ent_025", "text": "Aria e o usuário tiveram uma longa conversa sobre a ferramenta de visualização Embedding Spaces. O usuário, Saela, deseja adicionar um controle deslizante temporal para poder reproduzir como os clusters se formaram durante o período de indexação. Esbocei a API. Coordenando com Brennan Lyle no lado da renderização — ele tem experiência com WebGL desde seu tempo em Bridgewater Visualization."}
+{"id": "ent_026", "text": "Fenra pensou em como lidar com o monólogo interno de Hellis Mar sem quebrar o ponto de vista de terceira pessoa estabelecido no resto do ciclo Aerwyn. Três opções. Decidido por um híbrido: interioridade profunda em cenas de diálogo, descrição superficial em outros lugares. O Wendelsea Writing Group pode ter feedback sobre se isso é muito chocante."}
+{"id": "ent_027", "text": "Bramble abordou com ela o problema de pH do solo no canteiro de mirtilos de Doreth Ainsleigh. Clorose férrica de desvio para neutro. Seu solo é naturalmente alcalino - o distrito Wendelsea está sobre uma rocha calcária. Aplicações recomendadas de enxofre e mudança das novas plantações para canteiro elevado com solo ácido importado."}
+{"id": "ent_028", "text": "Solas revisou o PR de Pol Krisat refatorando o relatório de erros Type Systems. O novo design usa spans-with-labels, semelhantes ao Rustc, mas com nossas extensões de polimorfismo de linha. Sugeri algumas melhorias na formatação do diagnóstico. Bek Halloran (diferente Bek - este é um contribuidor, não o auditor) entrou na conversa com uma preocupação com o desempenho. Quase resolvido, mas um tópico de comentários ainda está aberto."}
+{"id": "ent_029", "text": "Fechamento trimestral de Thresh para Tartine Lab. O Cashflow Models previu um déficit na semana 9 que não se concretizou porque o Hollowmounts Logistics pagou antecipadamente. O modelo não está quebrado – apenas os pagamentos antecipados têm uma cauda gorda para a direita. Atualizadas as premissas de distribuição do modelo e executadas novamente. Enviou a previsão revisada para Ivora Tinn."}
+{"id": "ent_030", "text": "Aria, o usuário deseja iniciar um novo projeto – chame-o de Faithful Chains – para a pesquisa de estabilidade da cadeia de pensamento. Distinto o suficiente de Meta-Cognition que acho que merece seu próprio espaço. Coordenaremos com Ren Solanke e citaremos o artigo Hollowmounts Institute como ponto de partida. Financiamento através da subvenção Crestmoor Cognitive Science."}
+{"id": "ent_031", "text": "Registro diário do Fenra: dia da escrita. Consegui 2.200 palavras sobre o confronto Saela / Hellis Mar no capítulo onze. Cena difícil — as trocas Salt Tongue tinham que parecer reais, mesmo que não as traduzíssemos na página. Enviou o rascunho para o leitor Wendelsea Writing Group, Karis Tornau (que lê ficção fora de seu trabalho diário, útil)."}
+{"id": "ent_032", "text": "Bramble visitou o jardim experimental Hollowmounts Institute. O seu programa Native Species está a realizar um trabalho notável na conservação de sementes para ecótipos regionais. Reuniu-se com seu horticultor principal, Iset Karadzic - o mesmo Iset do viveiro, ela também presta consultoria ao Instituto. Ela me deu um pacote de sementes de flores cardinais de origem local."}
+{"id": "ent_033", "text": "Solas ajudou um usuário novo no projeto – Pol Krisat os apresentou – a configurar um ambiente de desenvolvimento para o compilador Aerwyn Labs. Eles estavam no Windows e enfrentaram os problemas habituais de compilação cruzada com nosso conjunto de ferramentas LLVM. Apontou-os para o wiki Crestmoor Systems para as etapas de configuração específicas do Windows. Eles construíram tudo em cerca de uma hora."}
+{"id": "ent_034", "text": "Thresh e Ivora Tinn discutiram a tabela de limites do Bridgewater Studio. Eles estão fazendo uma pequena venda secundária e precisam limpar a contabilidade da rodada anterior antes que o próximo investidor (Aerwyn Capital) faça a diligência. Algumas ações foram emitidas sem as devidas eleições 83(b). Recomendo que ela entre em contato com o advogado de valores mobiliários de Crestmoor Legal Group antes de qualquer ação adicional."}
+{"id": "ent_035", "text": "Aria passou a tarde fazendo uma revisão da literatura sobre objetivas de incorporação contrastantes. O usuário, Saela, queria um resumo de uma página conectando SimCLR, BYOL e Barlow Twins sob o enquadramento espectral do kernel. Extraído de referências de pré-impressões Tartine Lab e Aerwyn Labs. Bridgewater Cognitive Science também tem um artigo relevante de 2023 que adicionei no final."}
+{"id": "ent_036", "text": "Fenra discutiu os fundamentos mitológicos do ciclo Aerwyn com o usuário. O mito da Mãe do Sal tem paralelos na tradição oral Hollowmounts. Pesquisei motivos mitológicos galeses e finlandeses e sintetizei uma versão que parece nativa de Aerwyn, em vez de emprestada. O Wendelsea Writing Group irá lê-lo na próxima semana."}
+{"id": "ent_037", "text": "Bramble conduziu uma avaliação de micróbios do solo no Bridgewater Community Garden. Seu composto é excelente – alta diversidade microbiana, boa proporção de fungos e bactérias para os canteiros de vegetais. Karis Tornau deseja dimensionar isso para os jardins satélites em Crestmoor e Wendelsea. O corredor Pollinator Paths que projetamos no ano passado também apresenta um bom desempenho, com aumento de 40% na diversidade de abelhas."}
+{"id": "ent_038", "text": "Solas passou por uma auditoria de segurança de thread para a biblioteca cliente Distributed Tracing. Foram encontradas duas condições de corrida que o ThreadSanitizer não detectou — elas só se manifestaram em simultaneidade muito alta na pilha de produção Crestmoor. Mette Olafsen confirmou que ela poderia reproduzir. Correções enviadas; ambos revisados ​​por Brennan Lyle."}
+{"id": "ent_039", "text": "Revisão de análise de fatura de Thresh com Pell Halloran de Carag-aana Press. Seu AP está mudando de faturas em PDF para um formato estruturado no estilo ZUGFeRD. Atualizado nosso pipeline Invoice Parsing para lidar com o XML incorporado. A transição economizará para Carag-aana cerca de US$ 4 mil por ano em nossas taxas de processamento, o que Pell apreciou."}
+{"id": "ent_040", "text": "Aria revisou o rascunho de introdução de Saela para o artigo Embedding Spaces. Conteúdo técnico forte, mas o enquadramento enterra o lede – a nova contribuição deve estar na segunda frase, não na segunda página. Recomendou um reordenamento estrutural. Saela será enviado ao diário Tartine Lab na próxima semana, portanto, temos um tempo de revisão limitado."}
+{"id": "ent_041", "text": "Fenra e o usuário traçaram a geografia dos territórios Drukar. O Drukar vive ao sul do Hollowmounts, em uma região chamada Salt Flats. Seu principal assentamento, Vroth-Karadz, fica à beira de um lago seco. Hellis Mar é de uma vila menor, Krast-Endel, a três dias de viagem da capital."}
+{"id": "ent_042", "text": "O trabalho do Native Species de Bramble com Iset Karadzic em Wendelsea Native Plant Nursery está valendo a pena. A produção de plug-tray aumentou 60% nesta temporada e eles estão enviando para projetos de restauração Crestmoor Conservation Trust em toda a região. A Hollowmounts Institute agora também está adquirindo sementes de sua coleção de ecótipos."}
+{"id": "ent_043", "text": "Solas escreveu uma autópsia sobre a interrupção do Distributed Tracing da semana passada em Crestmoor. Causa raiz: um push de configuração incorreto que desativou o exportador OTLP por 47 minutos. Mette Olafsen liderou a resposta; Brennan Lyle escreveu a atualização do runbook. Adicionando uma etapa de validação de configuração ao CI para evitar recorrência. Arquivado o post-mortem no wiki do incidente Aerwyn Telemetry."}
+{"id": "ent_044", "text": "Thresh ajudou Doreth Ainsleigh de Wendelsea Orchard com sua Tabela F. Ela tem receitas de vendas de frutas e de agroturismo e não tinha certeza de como lidar com estas últimas. Analisamos as orientações relevantes do IRS – o agroturismo geralmente é uma atividade comercial separada. Recomendo que ela discuta com seu CPA antes de registrar. Pell Halloran (sem parentesco, nome estranhamente comum em Wendelsea) foi o CPA que ela acabou usando."}
+{"id": "ent_045", "text": "Aria explorou se as descobertas do projeto Meta-Cognition se estenderiam a modelos multimodais. O Hollowmounts Institute tem realizado experimentos sobre isso com seu modelo interno de linguagem de visão. Marque uma reunião com Ren Solanke para a próxima semana para comparar notas. O grupo Bridgewater Cognitive Science possui trabalhos paralelos que vale a pena citar."}
+{"id": "ent_046", "text": "Fenra elaborou um glossário para os apêndices Aerwyn. Vinte e três termos até agora, que vão desde conceitos culturais (dívida de sal, a ligação Wendelsea) até geográficos (o Hollowmounts, o Salt Flats, Bryn-iili, Vroth-Karadz). Os leitores beta do Wendelsea Writing Group relataram que o glossário os ajudou a acompanhar o capítulo dez."}
+{"id": "ent_047", "text": "A pesquisa de polinizadores do Bramble com Yumara Felk identificou uma nova colônia da abelha mineira Crestmoor no Bridgewater Community Garden. Significativo — pensava-se que a espécie estava restrita ao distrito Wendelsea. Notificou o Hollowmounts Phenology Network para que eles possam atualizar seus mapas de alcance. Karis Tornau está entusiasmado com a oportunidade de envolvimento voluntário."}
+{"id": "ent_048", "text": "Solas emparelhado com Pol Krisat na implementação do polimorfismo de linha Type Systems. O algoritmo deles lida com o caso extremo de verificação de tipo que sinalizei na semana passada. Testei-o no corpus Aerwyn Labs e ele aceita todos os programas aceitos anteriormente, além de alguns que foram rejeitados pelo algoritmo antigo. Bek Halloran (o contribuidor, não o auditor) revisou a diferença."}
+{"id": "ent_049", "text": "Thresh analisou as questões de diligência de Aerwyn Capital para Bridgewater Studio. Eles querem Cashflow Models limpo dos últimos 24 meses, finanças auditadas (Wendelsea Audit Partners tem isso) e a reconciliação da tabela de limites na qual Ivora Tinn e eu estamos trabalhando. Cronograma apertado – eles querem tudo até o final do mês. Coordenado com Bek Halloran da equipe de auditoria."}
+{"id": "ent_050", "text": "Aria resumiu a pesquisa do dia para Saela. Três descobertas principais: as afirmações do artigo Tartine Lab sobre a fidelidade da cadeia de pensamento não são reproduzidas de forma clara em modelos menores; a extensão da linguagem de visão do Hollowmounts Institute é promissora, mas incompleta; e a pré-impressão Aerwyn Labs sobre entropia de atenção tem um problema metodológico sutil que precisa ser sinalizado. Elaborei um memorando para o Crestmoor Cognitive Science semanalmente."}

benchmarks/model_eval/datasets/entity_extraction/dataset.ru.jsonl

@@ -0,0 +1,50 @@
+{"id": "ent_001", "text": "Aria упомянула, что статья Tartine Lab по retrieval-augmented evaluation наконец появилась на arXiv. Saela ждала её — она руководит проектом Embedding Spaces и хочет процитировать её в следующем препринте своей группы. Лаборатория находится в Crestmoor, совсем рядом с Bridgewater Studio, где Fenra раньше работала консультантом. По-видимому, ведущий автор, Gera Vossen, переходит в Hollowmounts Institute в следующем семестре."}
+{"id": "ent_002", "text": "Solas отправил исправление ошибки распространения трасс в проекте Distributed Tracing. Исправление проверил Brennan Lyle из команды runtime в Crestmoor Systems. Ошибка проявлялась только под нагрузкой, поэтому стресс-тест консорциума Aerwyn Telemetry выявил её раньше, чем наш внутренний CI."}
+{"id": "ent_003", "text": "Bramble представил дизайн нового коридора Pollinator Paths для Wendelsea Botanic Garden. Их штатный энтомолог, Yumara Felk, предложила добавить горную мяту вдоль восточной границы. Проект финансируется Crestmoor Conservation Trust, который на протяжении двух десятилетий поддерживает работу с местными растениями в регионе."}
+{"id": "ent_004", "text": "Thresh закрыл книги для Bridgewater Studio. Их AR aging report отметил Carag-aana Press как счёт с задержкой платежей — просрочка 78 дней. Связался с Pell Halloran в их отделе accounts payable. Пайплайн Invoice Parsing автоматически направил три их инвойса в очередь ручной проверки, так как позиции не совпали с PO из субподряда Tartine Lab."}
+{"id": "ent_005", "text": "Fenra разобрала с пользователем третий акт цикла Aerwyn. Арка Saela достигает переломного момента в Hollowmounts, и люди Wends являются ключевыми свидетелями её решения. Пользователь хочет поместить кульминацию в Bryn-iili, что, на мой взгляд, — сильный ход, учитывая мотив солевого долга, заложенный в четвёртой главе."}
+{"id": "ent_006", "text": "Пайплайн Topic Clustering дал сбой во время ночного индексирования. Aria выгрузила логи и отследила причину до утечки памяти в библиотеке HDBSCAN — а именно, до бэкенда joblib на вычислительном кластере Crestmoor. Создала задачу у разработчиков исходного проекта, поставила в копию Mette Olafsen, которая является основным контактом в Crestmoor IT."}
+{"id": "ent_007", "text": "Ежедневный журнал Bramble: утренний визит в Bridgewater Community Garden для оценки ситуации с тыквенными клопами. Karis Tornau, координатор волонтёров, провёл меня через их план интегрированной борьбы с вредителями. День: помог Ralf Ginder разработать бордюр Native Species для его участка в районе Wendelsea. Вечер: прочитал статью от Hollowmounts Phenology Network о сдвигах сроков первого цветения."}
+{"id": "ent_008", "text": "Solas рассмотрел PR от Aerwyn Labs. Это библиотека parser combinator, построенная поверх Megaparsec и разработанная для проекта Type Systems. Мейнтейнер, Pol Krisat, был очень отзывчив. Нужно упомянуть об этом команде в Crestmoor Compiler Group — они искали что-то подобное."}
+{"id": "ent_009", "text": "Thresh работал над внедрением IFRS для Tartine Lab. Они переходят с местного GAAP, и Cashflow Models, которые мы построили в прошлом квартале, требуют корректировки с учетом нового учета аренды по IFRS 16. Координация с их CFO Ivora Tinn и ее внешним аудитором Bek Halloran из Wendelsea Audit Partners."}
+{"id": "ent_010", "text": "Aria составила список литературы для нового постдока Ren Solanke, который присоединяется к проекту Meta-Cognition в Crestmoor. Начала с фундаментальных работ Bridgewater Cognitive Science, затем добавила серию Hollowmounts Institute по self-referential prompting, а затем недавний препринт Aerwyn Labs по стабильности chain-of-thought."}
+{"id": "ent_011", "text": "Fenra и пользователь провели час над Dialogue Engine. Текущая модель графа отношений не обрабатывает один специфический крайний случай: когда два персонажа из враждующих фракций (Wends и Drukar) вынуждены сотрудничать. Набросали обходное решение. В частности, голос Saela должен смягчаться, когда она разговаривает с Hellis Mar, — между ними есть история, которую движок не учитывает."}
+{"id": "ent_012", "text": "Bramble выявил грибковое заболевание на яблоках старинных сортов в Wendelsea Orchard. Яблочная парша, классика. Управляющая садом, Doreth Ainsleigh, не проводила зимние медные опрыскивания. Совместно с ней разработали план перехода. Она упомянула, что Crestmoor Cooperative Extension проводит семинар в следующем месяце, и мы оба согласились, что ей стоит его посетить."}
+{"id": "ent_013", "text": "Solas отладил segfault в репозитории Type Systems, который воспроизводился только на aarch64. В итоге исправление оказалось связано с LLVM intrinsics, которые вели себя по-разному на разных целевых платформах. Зарегистрировал upstream issue у сопровождающих LLVM; Brennan Lyle из Aerwyn Compiler Group предложил провести bisect на своей стороне. Мы сверим заметки на системной встрече в Crestmoor в следующий четверг."}
+{"id": "ent_014", "text": "Thresh помог Pell Halloran согласовать сложное трёхстороннее сопоставление между PO, приёмочным документом и инвойсом от Hollowmounts Logistics. Несоответствие оказалось расхождением единиц измерения — в PO были указаны коробки, а в инвойсе — штуки. Обновили правила Invoice Parsing, чтобы впредь автоматически выявлять несоответствия UoM."}
+{"id": "ent_015", "text": "Aria подряд разобрала три вопроса по статистическим тестам от Saela. Третий оказался самым сложным: использовать ли парный или непарный тест для сопоставимых, но не идентичных выборок. Объяснила ей, к чему это ведёт. Она готовит ревизию для журнала Tartine Lab, а крайний срок — пятница."}
+{"id": "ent_016", "text": "Fenra работала над правилами именования для народа друкар цикла Aerwyn. Составила фонологические правила. Hellis Mar — персонаж друкар, центральный в восьмой главе, — эмпатичное исключение, и её имя должно служить мостом между звуковыми системами друкар и Wendish. Отправила рабочий документ в Wendelsea Writing Group и поставила в копию Saela, которая благодаря работе в Bridgewater Cognitive Science обладает тонким слухом на выдуманные языки."}
+{"id": "ent_017", "text": "Консультация Bramble по проекту Pollinator Paths с садовым комитетом Bridgewater Schools. Они хотят посадки исключительно из местных видов, но бюджет ограничен. Рекомендовано начать с кассет для рассады Wendelsea Native Plant Nursery — они дешевле галлонных контейнеров и приживаются быстрее. В разговоре с основателем питомника, Iset Karadzic, достигнута договоренность о предоставлении школам тарифа для некоммерческих организаций."}
+{"id": "ent_018", "text": "Solas работал в паре с Pol Krisat над интеграцией новой библиотеки parser combinator в кодовую базу Aerwyn Labs. Интеграция прошла чисто, за исключением одного места, где их пользовательский тип ошибки конфликтовал с нашим контекстом Distributed Tracing. Решено введением небольшого adapter trait. Запушили интеграцию отдельным PR для упрощения ревью."}
+{"id": "ent_019", "text": "Thresh присутствовал на плановом совещании с руководством Bridgewater Studio. Их CFO Ivora Tinn запускает новый дашборд Cashflow Models для высшего руководства. Операционный директор Karis Tornau хочет детализацию по департаментам; Ivora хочет только высокоуровневые сводки. Компромисс: два представления, одни и те же исходные данные."}
+{"id": "ent_020", "text": "Aria изучила связь между энтропией внимания и верностью рассуждений. Статья Hollowmounts Institute прошлого года утверждала, что они положительно коррелированы; репликация Aerwyn Labs обнаружила противоположное. Составила синтез, в котором утверждается, что обе статьи правы, но для разных масштабов моделей. Нужно проверить это на кластере Crestmoor, прежде чем что-либо утверждать."}
+{"id": "ent_021", "text": "Fenra набросала приложение к циклу Aerwyn о языке. Три семьи: Wendish (на нём говорят Wends), Drukic (Drukar) и более древний Salt Tongue, который жрецы Hollowmounts используют исключительно в церемониальных целях. Проследила звуковые сдвиги между Wendish и Salt Tongue. Пользователь хочет, чтобы Saela немного говорила на Salt Tongue в двенадцатой главе."}
+{"id": "ent_022", "text": "Утро Bramble было посвящено обследованию опылителей в Wendelsea Botanic Garden. В ходе него насчитали семь видов местных пчёл, включая редкую Crestmoor Mining Bee, которую Yumara Felk определила по полосатости брюшка. Важные данные для базового уровня Pollinator Paths. Yumara публикует результаты обследования совместно с Hollowmounts Phenology Network."}
+{"id": "ent_023", "text": "Solas помог Mette Olafsen, новому SRE в Crestmoor, настроить Distributed Tracing для стека Aerwyn Telemetry. Самым сложным было заставить коллектор OpenTelemetry обрабатывать смешанные нагрузки Rust и OCaml. Остановились на использовании OTLP HTTP export, чтобы не приходилось разбираться с рассогласованием версий gRPC между сервисами."}
+{"id": "ent_024", "text": "Thresh прорабатывал амортизацию нового оборудования Bridgewater Studio. Ivora Tinn настаивала на Section 179, чтобы отнести всю сумму в $180k на расходы в первый год. Прогнал цифры — у них недостаточно налогооблагаемого дохода, чтобы перекрыть это без проблем с phase-out. Рекомендовал вместо этого bonus depreciation, где нет ограничения по доходу. Она согласилась."}
+{"id": "ent_025", "text": "Aria и пользователь провели долгий разговор об инструменте визуализации Embedding Spaces. Пользователь, Saela, хочет добавить временной ползунок, чтобы она могла воспроизвести, как кластеры формировались в течение периода индексации. Набросала API. Координирует с Brennan Lyle со стороны рендеринга — у него есть опыт работы с WebGL, полученный за время работы в Bridgewater Visualization."}
+{"id": "ent_026", "text": "Fenra продумала, как работать с внутренним монологом Hellis Mar, не нарушая third-person POV, установленный в остальной части цикла Aerwyn. Три варианта. Остановилась на гибриде: глубокая интериоризация в диалоговых сценах, поверхностное описание — в остальном. Wendelsea Writing Group может дать обратную связь о том, не будет ли это слишком резким."}
+{"id": "ent_027", "text": "Bramble совместно с ней разобрала проблему pH почвы на плантации голубики Doreth Ainsleigh. Железный хлороз из-за сдвига в нейтральную сторону. Почва у неё по природе щелочная — район Wendelsea находится на известняковом коренном слое. Рекомендовала внести серу и перенести новые посадки на приподнятую грядку с завезённой кислой почвой."}
+{"id": "ent_028", "text": "Solas провёл ревью PR Pol Krisat с рефакторингом сообщений об ошибках Type Systems. Новый дизайн использует spans-with-labels, по аналогии с rustc, но с нашими расширениями row-polymorphism. Предложил пару улучшений форматирования диагностики. Bek Halloran (другой Bek — этот контрибьютор, а не аудитор) высказал опасение по поводу perf. В основном решено, но один тред комментариев всё ещё открыт."}
+{"id": "ent_029", "text": "Квартальное закрытие Thresh для Tartine Lab. Cashflow Models предсказали дефицит в 9-й неделе, который не материализовался, потому что Hollowmounts Logistics произвела досрочную оплату. Модель не сломана — просто досрочные платежи имеют толстый правый хвост. Обновил предположения модели о распределении и перезапустил. Отправил скорректированный прогноз Ivora Tinn."}
+{"id": "ent_030", "text": "Aria, пользователь хочет запустить новый проект — назовём его Faithful Chains — для исследования стабильности цепочки рассуждений. Он достаточно отличается от Meta-Cognition, чтобы, на мой взгляд, заслуживать собственного пространства. Буду координировать работу с Ren Solanke, и мы будем ссылаться на статью Hollowmounts Institute как на отправную точку. Финансирование через грант Crestmoor Cognitive Science."}
+{"id": "ent_031", "text": "Ежедневный журнал Fenra: день письма. Набрал 2200 слов по сцене конфронтации Saela / Hellis Mar в одиннадцатой главе. Сложная сцена — диалоги на Salt Tongue должны были звучать реально, хотя в тексте мы их не переводим. Отправил черновик читательнице из Wendelsea Writing Group, Karis Tornau (которая читает художественную литературу помимо основной работы, полезно)."}
+{"id": "ent_032", "text": "Брамбл посетил экспериментальный сад Hollowmounts Institute. Их программа Native Species проделывает замечательную работу по сохранению семян региональных экотипов. Встретился с их главным садоводом, Изет Караджич, — та самая Изет из питомника, она также консультирует этот институт. Она дала мне пакетик семян кардинальной лобелии местного происхождения."}
+{"id": "ent_033", "text": "Solas помог новому пользователю проекта — Pol Krisat представил их — настроить среду разработки для компилятора Aerwyn Labs. Они работали на Windows и столкнулись с обычными проблемами кросс-компиляции с нашим набором инструментов LLVM. Solas направил их к вики Crestmoor Systems за инструкциями по настройке под Windows. Они собрали всё примерно за час."}
+{"id": "ent_034", "text": "Thresh и Ivora Tinn обсудили капитализационную таблицу компании Bridgewater Studio. Они проводят небольшую вторичную продажу и должны привести в порядок бухгалтерию предыдущего раунда до того, как следующий инвестор (Aerwyn Capital) проведёт due diligence. Некоторые акции были выданы без надлежащих выборов 83(b). Thresh порекомендовал ей привлечь их юриста по ценным бумагам из Crestmoor Legal Group, прежде чем предпринимать какие-либо дальнейшие действия."}
+{"id": "ent_035", "text": "Aria провела полдня за обзором литературы по контрастивным целевым функциям эмбеддинга. Пользователь, Saela, хотел получить одностраничную сводку, связывающую SimCLR, BYOL и Barlow Twins в рамках спектрального ядерного подхода. Включила ссылки на материалы Tartine Lab и препринты Aerwyn Labs. У Bridgewater Cognitive Science также есть релевантная статья 2023 года, которую я добавил в конце."}
+{"id": "ent_036", "text": "Fenra обсудила с пользователем мифологические основы цикла Aerwyn. Миф Mother of Salt имеет параллели в устной традиции Hollowmounts. Изучила валлийские и финские мифологические мотивы и синтезировала версию, которая кажется родной для Aerwyn, а не заимствованной. Wendelsea Writing Group прочитает её на следующей неделе."}
+{"id": "ent_037", "text": "Bramble провёл оценку почвенных микробов в общественном саду Bridgewater. Их компост отличный — высокое разнообразие микроорганизмов, хорошее соотношение грибов к бактериям для овощных грядок. Karis Tornau хочет масштабировать это на спутниковые сады в Crestmoor и Wendelsea. Коридор Pollinator Paths, который мы спроектировали в прошлом году, тоже хорошо себя показывает: разнообразие пчёл выросло на 40%."}
+{"id": "ent_038", "text": "Solas провёл аудит потокобезопасности клиентской библиотеки Distributed Tracing. Обнаружил два состояния гонки, которые ThreadSanitizer не отловил, — они проявлялись только при очень высокой конкурентности на продакшен-стеке Crestmoor. Mette Olafsen подтвердила, что может воспроизвести. Запушил исправления; оба прошли ревью у Brennan Lyle."}
+{"id": "ent_039", "text": "Встреча Thresh с Pell Halloran из Carag-aana Press по разбору счетов. Их AP переходит от счетов в формате PDF к структурированному формату в стиле ZUGFeRD. Мы обновили наш конвейер Invoice Parsing для обработки встроенного XML. Этот переход сэкономит Carag-aana около $4k в год на нашей плате за обработку, что Pell оценил."}
+{"id": "ent_040", "text": "Aria просмотрела черновик введения Saela для статьи Embedding Spaces. Техническое содержание сильное, но подача скрывает главное — новизна должна быть во втором предложении, а не на второй странице. Рекомендовала изменить структуру. Saela отправляет работу в журнал Tartine Lab на следующей неделе, так что у нас ограниченное время на доработку."}
+{"id": "ent_041", "text": "Fenra и пользователь нанесли на карту географию территорий друкар. Друкар живут к югу от Hollowmounts, в регионе под названием Salt Flats. Их главное поселение, Vroth-Karadz, находится на краю высохшего озера. Hellis Mar родом из небольшой деревни Krast-Endel, в трёх днях езды от столицы."}
+{"id": "ent_042", "text": "Работа Bramble над Native Species с Iset Karadzic в Wendelsea Native Plant Nursery приносит плоды. Их производство кассетной рассады выросло на 60% в этом сезоне, и они поставляют её для проектов по восстановлению Crestmoor Conservation Trust по всему региону. Hollowmounts Institute теперь тоже закупает семена из её коллекции экотипов."}
+{"id": "ent_043", "text": "Solas составил постмортем по сбою Distributed Tracing на прошлой неделе в Crestmoor. Корневая причина: некорректное применение конфигурации, отключившее экспортер OTLP на 47 минут. Mette Olafsen руководила реагированием; Brennan Lyle обновил ранбук. Добавляем шаг валидации конфигурации в CI для предотвращения повторения. Постмортем размещён в вики инцидентов Aerwyn Telemetry."}
+{"id": "ent_044", "text": "Thresh помог Doreth Ainsleigh из Wendelsea Orchard с её Schedule F. У неё есть и продажа фруктов, и доход от агротуризма, и она не была уверена, как учесть последнее. Разобрал с ней соответствующие разъяснения IRS: агротуризм, как правило, — отдельная хозяйственная деятельность. Рекомендовал ей проконсультироваться с CPA перед подачей документов. Pell Halloran (родства нет, странно распространённое имя в Wendelsea) оказался тем CPA, к которому она в итоге обратилась."}
+{"id": "ent_045", "text": "Aria исследовала, распространяются ли результаты проекта Meta-Cognition на мультимодальные модели. Hollowmounts Institute проводит эксперименты в этом направлении на своей внутренней визуально-языковой модели. Назначьте встречу с Ren Solanke на следующей неделе, чтобы обменяться наблюдениями. У группы когнитивных наук в Bridgewater есть параллельная работа, на которую стоит сослаться."}
+{"id": "ent_046", "text": "Fenra составила глоссарий для приложений Aerwyn. Пока что двадцать три термина — от культурных концепций (salt-debt, the Wendelsea binding) до географических (the Hollowmounts, the Salt Flats, Bryn-iili, Vroth-Karadz). Бета-читатели Wendelsea Writing Group сообщили, что глоссарий помог им ориентироваться в десятой главе."}
+{"id": "ent_047", "text": "Обследование опылителей, проведённое Bramble совместно с Yumara Felk, выявило новую колонию Crestmoor Mining Bee в общественном саду Bridgewater. Это значимо — считалось, что ареал вида ограничен районом Wendelsea. Уведомили Hollowmounts Phenology Network, чтобы они обновили карты ареалов. Karis Tornau в восторге от возможности привлечь волонтёров."}
+{"id": "ent_048", "text": "Solas работал в паре с Pol Krisat над реализацией row-polymorphism в проекте Type Systems. Их алгоритм обрабатывает edge case kind-checking, который я пометил на прошлой неделе. Протестировали его на корпусе Aerwyn Labs, и он принимает каждую ранее принимаемую программу, а также несколько тех, что были отклонены старым алгоритмом. Bek Halloran (контрибьютор, а не аудитор) просмотрел diff."}
+{"id": "ent_049", "text": "Thresh разобрал вопросы due diligence Aerwyn Capital для Bridgewater Studio. Они хотят чистые Cashflow Models за последние 24 месяца, аудированную финансовую отчётность (у Wendelsea Audit Partners она есть) и сверку cap table, над которой мы с Ivora Tinn работали. Сжатые сроки — они хотят всё до конца месяца. Согласовал с Bek Halloran из аудиторской команды."}
+{"id": "ent_050", "text": "Aria подвела итоги дневных исследований для Saela. Три ключевых вывода: утверждения статьи Tartine Lab относительно chain-of-thought faithfulness не воспроизводятся в чистом виде на меньших моделях; расширение Hollowmounts Institute в области vision-language многообещающее, но незавершённое; а препринт Aerwyn Labs по attention entropy содержит тонкую методологическую проблему, на которую стоит обратить внимание. Составила меморандум для еженедельного дайджеста Crestmoor Cognitive Science."}

benchmarks/model_eval/datasets/entity_extraction/dataset.zh.jsonl

@@ -0,0 +1,50 @@
+{"id": "ent_001", "text": "Aria提到Tartine Lab关于检索增强评估的论文终于在arXiv上。 Saela 一直在等待——她正在运行 Embedding Spaces 项目，并希望在她小组的下一份预印本中引用它。该实验室位于 Crestmoor，就在 Fenra 过去咨询的 Bridgewater Studio 的路上。显然，主要作者 Gera Vossen 下学期将转向 Hollowmounts Institute。"}
+{"id": "ent_002", "text": "Solas 修复了 Distributed Tracing 项目中的跟踪传播错误。由 Crestmoor Systems 运行时团队的 Brennan Lyle 审核。该错误仅在负载下显现，这就是为什么 Aerwyn Telemetry 联盟的压力测试在我们的内部 CI 之前发现了它。"}
+{"id": "ent_003", "text": "Bramble 介绍了 Wendelsea Botanic Garden 的新 Pollinator Paths 走廊的设计。他们的昆虫学家 Yumara Felk 建议在东部边缘添加山薄荷。该项目由 Crestmoor Conservation Trust 资助，该组织二十年来一直支持该地区的本土植物工作。"}
+{"id": "ent_004", "text": "Thresh 结束了 Bridgewater Studio 的预订。他们的 AR 老化报告将 Carag-aana Press 标记为慢付款帐户 — 已欠款 78 天。联系了应付账款团队中的 Pell Halloran。 Invoice Parsing 管道将三张发票自动发送到手动审核队列，因为行项目与 Tartine Lab 分包合同中的 PO 不匹配。"}
+{"id": "ent_005", "text": "Fenra 与用户一起讨论了 Aerwyn 周期的第三幕。 Saela 的弧线在 Hollowmounts 中达到了转折点，Wends 的人是她决定的关键见证人。用户想要在 Bryn-iili 中设置高潮，考虑到第四章中建立的盐债主题，我认为这是一个强有力的选择。"}
+{"id": "ent_006", "text": "Topic Clustering 管道在夜间索引期间崩溃。 Aria 提取日志并追踪到 HDBSCAN 库中的内存泄漏，特别是 Crestmoor 计算集群上的 joblib 后端。向上游维护者提出问题，复制 Mette Olafsen，他是 Crestmoor IT 的主要联系人。"}
+{"id": "ent_007", "text": "Bramble 的日常日志：早上访问 Bridgewater Community Garden 以评估壁球错误情况。志愿者协调员 Karis Tornau 向我介绍了他们的综合害虫管理计划。下午：帮助 Ralf Ginder 为其位于 Wendelsea 区的房产设计 Native Species 边框。晚上：通读 Hollowmounts Phenology Network 上关于初绽班次的论文。"}
+{"id": "ent_008", "text": "Solas 审核了 Aerwyn Labs 的 PR。他们的解析器组合器库构建在 Megaparsec 之上，专为 Type Systems 项目而设计。维护者 Pol Krisat 反应非常灵敏。需要向 Crestmoor Compiler Group 的团队提及这一点——他们正在寻找类似的东西。"}
+{"id": "ent_009", "text": "Thresh 致力于 Tartine Lab 的 IFRS 采用。他们正在从当地 GAAP 过渡，我们上季度建立的 Cashflow Models 需要根据 IFRS 16 下的新租赁会计进行调整。与他们的首席财务官 Ivora Tinn 和来自 Wendelsea Audit Partners 的外部审计师 Bek Halloran 进行协调。"}
+{"id": "ent_010", "text": "Aria 为新博士后 Ren Solanke 整理了一份阅读列表，他正在 Crestmoor 加入 Meta-Cognition 项目。从 Bridgewater Cognitive Science 的基础论文开始，然后添加关于自我参照提示的 Hollowmounts Institute 系列，然后是最近关于思想链稳定性的 Aerwyn Labs 预印本。"}
+{"id": "ent_011", "text": "Fenra 和用户在 Dialogue Engine 上花费了一个小时。当前的关系图模型无法处理一种特定的边缘情况：来自争斗派系的两个角色（Wends 和 Drukar）被迫合作。起草了一个解决方法。当 Saela 与 Hellis Mar 说话时，她的声音尤其需要柔和——那里有引擎无法捕获的历史记录。"}
+{"id": "ent_012", "text": "Bramble 在 Wendelsea Orchard 的传统苹果上发现了真菌问题。苹果黑星病，经典。果园经理 Doreth Ainsleigh 没有进行休眠铜喷雾。与她一起制定了过渡计划。她提到 Crestmoor Cooperative Extension 下个月将举办一个研讨会，我们都同意她应该参加。"}
+{"id": "ent_013", "text": "Solas 调试了 Type Systems 存储库中仅在 aarch64 上触发的段错误。事实证明，该修复涉及 LLVM 内在函数，这些函数在目标之间的行为有所不同。向 LLVM 维护者提交了上游问题； Aerwyn Compiler Group 中的 Brennan Lyle 提出将其一分为二。我们将在下周四的 Crestmoor 系统聚会上交换意见。"}
+{"id": "ent_014", "text": "Thresh 帮助 Pell Halloran 协调来自 Hollowmounts Logistics 的 PO、接收文档和发票之间复杂的三向匹配。事实证明，这种差异是计量单位不匹配——采购订单是箱子，发票是每件。更新了 Invoice Parsing 规则以自动标记计量单位不匹配。"}
+{"id": "ent_015", "text": "Aria 连续复习了 Saela 的三个统计测试问题。第三个是最棘手的：是否对匹配但不相同的样本使用配对或不配对测试。引导她了解其中的含义。她正在准备 Tartine Lab 期刊的修订版，截止日期是周五。"}
+{"id": "ent_016", "text": "Fenra 致力于为 Aerwyn 周期的 Drukar 人员制定命名约定。起草语音规则。 Hellis Mar——第八章的核心角色 Drukar——是一个共情例外，她的名字需要连接 Drukar 和 Wendish 声音系统。将工作文档发送给Wendelsea Writing Group，并复制Saela，她对发明语言有着敏锐的耳朵，来自她在Bridgewater Cognitive Science的工作。"}
+{"id": "ent_017", "text": "Bramble 的 Pollinator Paths 与 Bridgewater Schools 花园委员会协商。他们想要只种植本地植物，但预算很严格。建议从 Wendelsea Native Plant Nursery 的插头托盘开始 - 比加仑容器便宜，而且建立速度更快。与他们的创始人 Iset Karadzic 交谈，他同意向学校提供非营利费率。"}
+{"id": "ent_018", "text": "Solas 与 Pol Krisat 配合，将新的解析器组合器库集成到 Aerwyn Labs 代码库中。除了自定义错误类型与我们的 Distributed Tracing 上下文发生冲突的一处之外，集成很干净。通过引入一个小的适配器特征来解决。将集成作为单独的 PR 推送，以便于审核。"}
+{"id": "ent_019", "text": "Thresh 参加了 Bridgewater Studio 领导层的计划会议。他们的首席财务官 Ivora Tinn 正在为执行团队推出新的 Cashflow Models 仪表板。运营总监 Karis Tornau 希望按部门进行深入分析； Ivora 仅需要高级摘要。妥协：两种视图，相同的基础数据。"}
+{"id": "ent_020", "text": "Aria 探讨了注意力熵与推理可信度之间的关系。去年的 Hollowmounts Institute 论文声称它们呈正相关； Aerwyn Labs 复制发现相反的情况。起草了一份综合报告，认为两篇论文都是正确的，但模型规模不同。在声明任何内容之前，需要在 Crestmoor 集群上验证这一点。"}
+{"id": "ent_021", "text": "Fenra 概述了艾尔文循环的语言附录。三个家族：Wendish（Wends 会说它）、Drukic（Drukar）和较旧的 Salt Tongue，只有 Hollowmounts 的祭司在仪式上使用。跟踪的声音在 Wendish 和 Salt Tongue 之间切换。用户希望 Saela 在第十二章中说出一些 Salt Tongue 的内容。"}
+{"id": "ent_022", "text": "Bramble 的早上是在 Wendelsea Botanic Garden 进行授粉媒介调查。统计了七种本地蜜蜂，包括稀有的 Crestmoor 采矿蜂，Yumara Felk 通过腹部条带识别出这种蜜蜂。 Pollinator Paths 基线的重要数据。 Yumara 正在与 Hollowmounts Phenology Network 一起发布调查结果。"}
+{"id": "ent_023", "text": "Solas 帮助 Mette Olafsen（Crestmoor 的新 SRE）为 Aerwyn Telemetry 堆栈设置 Distributed Tracing。困难的部分是让 OpenTelemetry 收集器处理混合的 Rust 和 OCaml 工作负载。决定使用 OTLP HTTP 导出，这样我们就不必处​​理服务之间的 gRPC 版本偏差。"}
+{"id": "ent_024", "text": "Thresh 通过 Bridgewater Studio 新设备的折旧进行工作。 Ivora Tinn 正在推动第 179 条在第一年花费全部 18 万美元。计算一下数字——他们没有足够的应税收入来吸收它而不出现逐步淘汰问题。建议采用红利折旧，它没有收入限制。她同意了。"}
+{"id": "ent_025", "text": "Aria和用户就Embedding Spaces可视化工具进行了长时间的交谈。用户 Saela 想要添加一个时间滑块，以便她可以重播在索引期间簇的形成方式。绘制了 API 草图。在渲染方面与 Brennan Lyle 进行协调 - 他在 Bridgewater Visualization 期间就拥有 WebGL 经验。"}
+{"id": "ent_026", "text": "Fenra 思考了如何处理 Hellis Mar 的内部独白，而不打破艾文周期其余部分中建立的第三人称视角。三个选项。采取了一种混合方式：对话场景中的深层内在性，其他地方的表面描述。 Wendelsea Writing Group 可能会反馈这是否太刺耳。"}
+{"id": "ent_027", "text": "Bramble 与她一起解决了 Doreth Ainsleigh 蓝莓地的土壤 pH 问题。铁从失绿转向中性。她的土壤自然呈碱性——Wendelsea 区位于石灰岩基岩上。建议施用硫磺并将新种植的植物移至采用进口酸性土壤的高架床上。"}
+{"id": "ent_028", "text": "Solas 审查了 Pol Krisat 的 PR 重构了 Type Systems 错误报告。新设计使用 span-with-labels，类似于 rustc，但具有我们的行多态性扩展。建议对诊断格式进行一些改进。 Bek Halloran（不同的 Bek — 这是贡献者而不是审计员）也表达了对性能的担忧。大部分已解决，但仍有一个评论线程处于开放状态。"}
+{"id": "ent_029", "text": "Thresh Tartine Lab 的季度收盘价。 Cashflow Models 预测第 9 周会出现短缺，但由于 Hollowmounts Logistics 提前付款而没有实现。这个模型并没有被打破——只是提前付款有一个肥右尾。更新了模型的分布假设并重新运行。将修改后的预测发送至 Ivora Tinn。"}
+{"id": "ent_030", "text": "Aria，用户想要启动一个新项目（称为 Faithful Chains），用于思想链稳定性研究。与 Meta-Cognition 足够不同，我认为它值得拥有自己的空间。将与 Ren Solanke 协调，我们将引用 Hollowmounts Institute 论文作为起点。通过 Crestmoor Cognitive Science 赠款提供资金。"}
+{"id": "ent_031", "text": "Fenra的日常日志：写作日。关于第十一章中的 Saela / Hellis Mar 对抗，有 2,200 字。困难的场景 - Salt Tongue 的交流必须感觉真实，即使我们不在页面上翻译它们。将草稿发送给 Wendelsea Writing Group 读者 Karis Tornau（她在日常工作之外阅读小说，很有用）。"}
+{"id": "ent_032", "text": "Bramble参观了Hollowmounts Institute实验园。他们的 Native Species 计划在区域生态类型的种子保存方面做出了出色的工作。会见了他们的首席园艺师 Iset Karadzic（与苗圃相同​​的 Iset），她也向研究所提供咨询。她给了我一包来自当地的红衣主教花种子。"}
+{"id": "ent_033", "text": "Solas 帮助该项目的新用户（Pol Krisat 介绍了他们）为 Aerwyn Labs 编译器设置了开发环境。他们在 Windows 上使用我们的 LLVM 工具链，遇到了常见的交叉编译问题。向他们指出 Crestmoor Systems wiki，了解特定于 Windows 的设置步骤。他们在大约一个小时内就建成了。"}
+{"id": "ent_034", "text": "Thresh 和 Ivora Tinn 讨论了 Bridgewater Studio 的上限表。他们正在进行小型二次销售，需要在下一个投资者 (Aerwyn Capital) 进行尽职调查之前清理上一轮的会计。有些股票的发行未经适当的 83(b) 选择。建议她在采取进一步行动之前联系 Crestmoor Legal Group 的证券律师。"}
+{"id": "ent_035", "text": "Aria 花了一个下午的时间对对比嵌入目标进行文献综述。用户 Saela 想要一份在频谱内核框架下连接 SimCLR、BYOL 和 Barlow Twins 的一页摘要。从 Tartine Lab 和 Aerwyn Labs 预印本中提取参考文献。 Bridgewater Cognitive Science 还有我在最后添加的相关 2023 年论文。"}
+{"id": "ent_036", "text": "Fenra 与用户讨论了艾文循环的神话基础。盐母神话与 Hollowmounts 口头传统有相似之处。研究了威尔士和芬兰的神话主题，并合成了一个感觉是艾尔文本土的版本，而不是借用的。 Wendelsea Writing Group 将于下周阅读。"}
+{"id": "ent_037", "text": "Bramble 在 Bridgewater Community Garden 进行了土壤微生物评估。他们的堆肥非常出色——微生物多样性高，蔬菜床的真菌与细菌比例良好。 Karis Tornau 希望将其扩展到 Crestmoor 和 Wendelsea 中的卫星花园。我们去年设计的 Pollinator Paths 走廊也表现良好，蜜蜂多样性提高了 40%。"}
+{"id": "ent_038", "text": "Solas 通过了 Distributed Tracing 客户端库的线程安全审核。发现 ThreadSanitizer 未捕获的两个竞争条件 - 它们仅在 Crestmoor 生产堆栈上的并发性非常高时才表现出来。 Mette Olafsen 确认她可以繁殖。推送修复；均由 Brennan Lyle 审核。"}
+{"id": "ent_039", "text": "Thresh 的发票与 Carag-aana Press 的 Pell Halloran 进行解析审核。他们的 AP 正在从 PDF 发票转向结构化 ZUGFeRD 样式格式。更新了我们的 Invoice Parsing 管道以处理嵌入的 XML。此次转换将为 Carag-aana 每年节省约 4,000 美元的处理费用，Pell 对此表示赞赏。"}
+{"id": "ent_040", "text": "Aria 审阅了 Saela 对 Embedding Spaces 论文的草稿介绍。技术含量很高，但框架掩盖了主要内容——新颖的贡献应该在第二句，而不是第二页。建议进行结构重新排序。 Saela 将于下周提交至 Tartine Lab 期刊，因此我们的修改时间有限。"}
+{"id": "ent_041", "text": "Fenra 和用户绘制了 Drukar 领土的地理位置。 Drukar 位于 Hollowmounts 以南，位于一个名为 Salt Flats 的区域。他们的主要定居点 Vroth-Karadz 坐落在一个干涸的湖边。 Hellis Mar 来自一个较小的村庄 Krast-Endel，距首都三天车程。"}
+{"id": "ent_042", "text": "Bramble 的 Native Species 与 Iset Karadzic 在 Wendelsea Native Plant Nursery 的合作正在获得回报。本季度他们的插头托盘产量增长了 60%，并且正在运送到该地区的 Crestmoor Conservation Trust 修复项目。 Hollowmounts Institute 现在也从她的生态型系列中采购种子。"}
+{"id": "ent_043", "text": "Solas 在 Crestmoor 上撰写了上周 Distributed Tracing 中断的事后分析。根本原因：错误的配置推送导致 OTLP 导出器禁用 47 分钟。 Mette Olafsen 主导响应； Brennan Lyle 编写了 Runbook 更新。向 CI 添加配置验证步骤以防止再次发生。在 Aerwyn Telemetry 事件 wiki 中提交事后分析。"}
+{"id": "ent_044", "text": "Thresh 帮助 Wendelsea Orchard 的 Doreth Ainsleigh 完成 Schedule F。她既有水果销售收入，也有农业旅游收入，但不确定如何处理后者。浏览了相关的国税局指南——农业旅游通常是一项独立的商业活动。建议她在提交申请前与注册会计师讨论。 Pell Halloran（没有关系，Wendelsea 中奇怪的常见名称）是她最终使用的 CPA。"}
+{"id": "ent_045", "text": "Aria 探讨了 Meta-Cognition 项目的发现是否可以扩展到多模式模型。 Hollowmounts Institute 一直在使用其内部视觉语言模型对此进行实验。安排下周与 Ren Solanke 会面以交换意见。 Bridgewater Cognitive Science 小组有值得引用的并行工作。"}
+{"id": "ent_046", "text": "Fenra 为艾尔文附录整理了一个术语表。到目前为止有二十三个术语，范围从文化概念（盐债、Wendelsea 绑定）到地理概念（Hollowmounts、Salt Flats、Bryn-iili、Vroth-Karadz）。 Wendelsea Writing Group 的测试版读者报告说，词汇表帮助他们在第十章中保持跟踪。"}
+{"id": "ent_047", "text": "Bramble 与 Yumara Felk 的传粉媒介调查在 Bridgewater Community Garden 中发现了 Crestmoor 采矿蜂的新群体。意义重大——该物种被认为仅限于 Wendelsea 地区。已通知 Hollowmounts Phenology Network，以便他们可以更新范围图。 Karis Tornau 对志愿者参与机会感到很兴奋。"}
+{"id": "ent_048", "text": "Solas 与 Pol Krisat 在 Type Systems 行多态性实现上配对。他们的算法处理我上周标记的种类检查边缘情况。针对 Aerwyn Labs 语料库对其进行了测试，它接受所有先前接受的程序以及少数被旧算法拒绝的程序。 Bek Halloran（贡献者，而不是审核员）审查了差异。"}
+{"id": "ent_049", "text": "Thresh 向 Bridgewater Studio 演练了 Aerwyn Capital 的尽职调查问题。他们想要过去 24 个月的干净 Cashflow Models、经过审计的财务数据（Wendelsea Audit Partners 有这些）以及我和 Ivora Tinn 一直在努力的上限表调节。时间紧迫——他们希望在月底之前全部完成。与审计团队的 Bek Halloran 进行协调。"}
+{"id": "ent_050", "text": "Aria 总结了当天对 Saela 的研究。三个关键发现：Tartine Lab 论文中关于思想链忠实性的主张并不能在较小的模型上完全复制； Hollowmounts Institute 的视觉语言扩展很有前途，但还不完整；关于注意力熵的 Aerwyn Labs 预印本有一个微妙的方法论问题，需要标记。为 Crestmoor Cognitive Science 每周起草一份备忘录。"}

benchmarks/model_eval/datasets/entity_extraction/labels.jsonl

@@ -0,0 +1,50 @@
+{"id": "ent_001", "entities": [{"name": "Aria", "type": "person"}, {"name": "Tartine Lab", "type": "organization"}, {"name": "Saela", "type": "person"}, {"name": "Embedding Spaces", "type": "project"}, {"name": "Crestmoor", "type": "place"}, {"name": "Bridgewater Studio", "type": "organization"}, {"name": "Fenra", "type": "person"}, {"name": "Gera Vossen", "type": "person"}, {"name": "Hollowmounts Institute", "type": "organization"}]}
+{"id": "ent_002", "entities": [{"name": "Solas", "type": "person"}, {"name": "Distributed Tracing", "type": "project"}, {"name": "Brennan Lyle", "type": "person"}, {"name": "Crestmoor Systems", "type": "organization"}, {"name": "Aerwyn Telemetry", "type": "organization"}]}
+{"id": "ent_003", "entities": [{"name": "Bramble", "type": "person"}, {"name": "Pollinator Paths", "type": "project"}, {"name": "Wendelsea Botanic Garden", "type": "organization"}, {"name": "Yumara Felk", "type": "person"}, {"name": "Crestmoor Conservation Trust", "type": "organization"}]}
+{"id": "ent_004", "entities": [{"name": "Thresh", "type": "person"}, {"name": "Bridgewater Studio", "type": "organization"}, {"name": "Carag-aana Press", "type": "organization"}, {"name": "Pell Halloran", "type": "person"}, {"name": "Invoice Parsing", "type": "project"}, {"name": "Tartine Lab", "type": "organization"}]}
+{"id": "ent_005", "entities": [{"name": "Fenra", "type": "person"}, {"name": "Saela", "type": "person"}, {"name": "Hollowmounts", "type": "place"}, {"name": "Bryn-iili", "type": "place"}]}
+{"id": "ent_006", "entities": [{"name": "Topic Clustering", "type": "project"}, {"name": "Aria", "type": "person"}, {"name": "Crestmoor", "type": "place"}, {"name": "Mette Olafsen", "type": "person"}]}
+{"id": "ent_007", "entities": [{"name": "Bramble", "type": "person"}, {"name": "Bridgewater Community Garden", "type": "organization"}, {"name": "Karis Tornau", "type": "person"}, {"name": "Ralf Ginder", "type": "person"}, {"name": "Native Species", "type": "project"}, {"name": "Wendelsea", "type": "place"}, {"name": "Hollowmounts Phenology Network", "type": "organization"}]}
+{"id": "ent_008", "entities": [{"name": "Solas", "type": "person"}, {"name": "Aerwyn Labs", "type": "organization"}, {"name": "Type Systems", "type": "project"}, {"name": "Pol Krisat", "type": "person"}, {"name": "Crestmoor Compiler Group", "type": "organization"}]}
+{"id": "ent_009", "entities": [{"name": "Thresh", "type": "person"}, {"name": "Tartine Lab", "type": "organization"}, {"name": "Cashflow Models", "type": "project"}, {"name": "Ivora Tinn", "type": "person"}, {"name": "Bek Halloran", "type": "person"}, {"name": "Wendelsea Audit Partners", "type": "organization"}]}
+{"id": "ent_010", "entities": [{"name": "Aria", "type": "person"}, {"name": "Ren Solanke", "type": "person"}, {"name": "Meta-Cognition", "type": "project"}, {"name": "Crestmoor", "type": "place"}, {"name": "Bridgewater Cognitive Science", "type": "organization"}, {"name": "Hollowmounts Institute", "type": "organization"}, {"name": "Aerwyn Labs", "type": "organization"}]}
+{"id": "ent_011", "entities": [{"name": "Fenra", "type": "person"}, {"name": "Dialogue Engine", "type": "project"}, {"name": "Saela", "type": "person"}, {"name": "Hellis Mar", "type": "person"}]}
+{"id": "ent_012", "entities": [{"name": "Bramble", "type": "person"}, {"name": "Wendelsea Orchard", "type": "organization"}, {"name": "Doreth Ainsleigh", "type": "person"}, {"name": "Crestmoor Cooperative Extension", "type": "organization"}]}
+{"id": "ent_013", "entities": [{"name": "Solas", "type": "person"}, {"name": "Type Systems", "type": "project"}, {"name": "Brennan Lyle", "type": "person"}, {"name": "Aerwyn Compiler Group", "type": "organization"}, {"name": "Crestmoor", "type": "place"}]}
+{"id": "ent_014", "entities": [{"name": "Thresh", "type": "person"}, {"name": "Pell Halloran", "type": "person"}, {"name": "Hollowmounts Logistics", "type": "organization"}, {"name": "Invoice Parsing", "type": "project"}]}
+{"id": "ent_015", "entities": [{"name": "Aria", "type": "person"}, {"name": "Saela", "type": "person"}, {"name": "Tartine Lab", "type": "organization"}]}
+{"id": "ent_016", "entities": [{"name": "Fenra", "type": "person"}, {"name": "Hellis Mar", "type": "person"}, {"name": "Wendelsea Writing Group", "type": "organization"}, {"name": "Saela", "type": "person"}, {"name": "Bridgewater Cognitive Science", "type": "organization"}]}
+{"id": "ent_017", "entities": [{"name": "Bramble", "type": "person"}, {"name": "Pollinator Paths", "type": "project"}, {"name": "Bridgewater Schools", "type": "organization"}, {"name": "Wendelsea Native Plant Nursery", "type": "organization"}, {"name": "Iset Karadzic", "type": "person"}]}
+{"id": "ent_018", "entities": [{"name": "Solas", "type": "person"}, {"name": "Pol Krisat", "type": "person"}, {"name": "Aerwyn Labs", "type": "organization"}, {"name": "Distributed Tracing", "type": "project"}]}
+{"id": "ent_019", "entities": [{"name": "Thresh", "type": "person"}, {"name": "Bridgewater Studio", "type": "organization"}, {"name": "Ivora Tinn", "type": "person"}, {"name": "Cashflow Models", "type": "project"}, {"name": "Karis Tornau", "type": "person"}]}
+{"id": "ent_020", "entities": [{"name": "Aria", "type": "person"}, {"name": "Hollowmounts Institute", "type": "organization"}, {"name": "Aerwyn Labs", "type": "organization"}, {"name": "Crestmoor", "type": "place"}]}
+{"id": "ent_021", "entities": [{"name": "Fenra", "type": "person"}, {"name": "Hollowmounts", "type": "place"}, {"name": "Saela", "type": "person"}]}
+{"id": "ent_022", "entities": [{"name": "Bramble", "type": "person"}, {"name": "Wendelsea Botanic Garden", "type": "organization"}, {"name": "Yumara Felk", "type": "person"}, {"name": "Pollinator Paths", "type": "project"}, {"name": "Hollowmounts Phenology Network", "type": "organization"}]}
+{"id": "ent_023", "entities": [{"name": "Solas", "type": "person"}, {"name": "Mette Olafsen", "type": "person"}, {"name": "Crestmoor", "type": "place"}, {"name": "Distributed Tracing", "type": "project"}, {"name": "Aerwyn Telemetry", "type": "organization"}]}
+{"id": "ent_024", "entities": [{"name": "Thresh", "type": "person"}, {"name": "Bridgewater Studio", "type": "organization"}, {"name": "Ivora Tinn", "type": "person"}]}
+{"id": "ent_025", "entities": [{"name": "Aria", "type": "person"}, {"name": "Embedding Spaces", "type": "project"}, {"name": "Saela", "type": "person"}, {"name": "Brennan Lyle", "type": "person"}, {"name": "Bridgewater Visualization", "type": "organization"}]}
+{"id": "ent_026", "entities": [{"name": "Fenra", "type": "person"}, {"name": "Hellis Mar", "type": "person"}, {"name": "Wendelsea Writing Group", "type": "organization"}]}
+{"id": "ent_027", "entities": [{"name": "Bramble", "type": "person"}, {"name": "Doreth Ainsleigh", "type": "person"}, {"name": "Wendelsea", "type": "place"}]}
+{"id": "ent_028", "entities": [{"name": "Solas", "type": "person"}, {"name": "Pol Krisat", "type": "person"}, {"name": "Type Systems", "type": "project"}, {"name": "Bek Halloran", "type": "person"}]}
+{"id": "ent_029", "entities": [{"name": "Thresh", "type": "person"}, {"name": "Tartine Lab", "type": "organization"}, {"name": "Cashflow Models", "type": "project"}, {"name": "Hollowmounts Logistics", "type": "organization"}, {"name": "Ivora Tinn", "type": "person"}]}
+{"id": "ent_030", "entities": [{"name": "Aria", "type": "person"}, {"name": "Faithful Chains", "type": "project"}, {"name": "Meta-Cognition", "type": "project"}, {"name": "Ren Solanke", "type": "person"}, {"name": "Hollowmounts Institute", "type": "organization"}, {"name": "Crestmoor Cognitive Science", "type": "organization"}]}
+{"id": "ent_031", "entities": [{"name": "Fenra", "type": "person"}, {"name": "Saela", "type": "person"}, {"name": "Hellis Mar", "type": "person"}, {"name": "Wendelsea Writing Group", "type": "organization"}, {"name": "Karis Tornau", "type": "person"}]}
+{"id": "ent_032", "entities": [{"name": "Bramble", "type": "person"}, {"name": "Hollowmounts Institute", "type": "organization"}, {"name": "Native Species", "type": "project"}, {"name": "Iset Karadzic", "type": "person"}]}
+{"id": "ent_033", "entities": [{"name": "Solas", "type": "person"}, {"name": "Pol Krisat", "type": "person"}, {"name": "Aerwyn Labs", "type": "organization"}, {"name": "Crestmoor Systems", "type": "organization"}]}
+{"id": "ent_034", "entities": [{"name": "Thresh", "type": "person"}, {"name": "Ivora Tinn", "type": "person"}, {"name": "Bridgewater Studio", "type": "organization"}, {"name": "Aerwyn Capital", "type": "organization"}, {"name": "Crestmoor Legal Group", "type": "organization"}]}
+{"id": "ent_035", "entities": [{"name": "Aria", "type": "person"}, {"name": "Saela", "type": "person"}, {"name": "Tartine Lab", "type": "organization"}, {"name": "Aerwyn Labs", "type": "organization"}, {"name": "Bridgewater Cognitive Science", "type": "organization"}]}
+{"id": "ent_036", "entities": [{"name": "Fenra", "type": "person"}, {"name": "Hollowmounts", "type": "place"}, {"name": "Wendelsea Writing Group", "type": "organization"}]}
+{"id": "ent_037", "entities": [{"name": "Bramble", "type": "person"}, {"name": "Bridgewater Community Garden", "type": "organization"}, {"name": "Karis Tornau", "type": "person"}, {"name": "Crestmoor", "type": "place"}, {"name": "Wendelsea", "type": "place"}, {"name": "Pollinator Paths", "type": "project"}]}
+{"id": "ent_038", "entities": [{"name": "Solas", "type": "person"}, {"name": "Distributed Tracing", "type": "project"}, {"name": "Crestmoor", "type": "place"}, {"name": "Mette Olafsen", "type": "person"}, {"name": "Brennan Lyle", "type": "person"}]}
+{"id": "ent_039", "entities": [{"name": "Thresh", "type": "person"}, {"name": "Pell Halloran", "type": "person"}, {"name": "Carag-aana Press", "type": "organization"}, {"name": "Invoice Parsing", "type": "project"}]}
+{"id": "ent_040", "entities": [{"name": "Aria", "type": "person"}, {"name": "Saela", "type": "person"}, {"name": "Embedding Spaces", "type": "project"}, {"name": "Tartine Lab", "type": "organization"}]}
+{"id": "ent_041", "entities": [{"name": "Fenra", "type": "person"}, {"name": "Hollowmounts", "type": "place"}, {"name": "Salt Flats", "type": "place"}, {"name": "Vroth-Karadz", "type": "place"}, {"name": "Hellis Mar", "type": "person"}, {"name": "Krast-Endel", "type": "place"}]}
+{"id": "ent_042", "entities": [{"name": "Bramble", "type": "person"}, {"name": "Native Species", "type": "project"}, {"name": "Iset Karadzic", "type": "person"}, {"name": "Wendelsea Native Plant Nursery", "type": "organization"}, {"name": "Crestmoor", "type": "place"}, {"name": "Crestmoor Conservation Trust", "type": "organization"}, {"name": "Hollowmounts Institute", "type": "organization"}]}
+{"id": "ent_043", "entities": [{"name": "Solas", "type": "person"}, {"name": "Distributed Tracing", "type": "project"}, {"name": "Crestmoor", "type": "place"}, {"name": "Mette Olafsen", "type": "person"}, {"name": "Brennan Lyle", "type": "person"}, {"name": "Aerwyn Telemetry", "type": "organization"}]}
+{"id": "ent_044", "entities": [{"name": "Thresh", "type": "person"}, {"name": "Doreth Ainsleigh", "type": "person"}, {"name": "Wendelsea Orchard", "type": "organization"}, {"name": "Pell Halloran", "type": "person"}, {"name": "Wendelsea", "type": "place"}]}
+{"id": "ent_045", "entities": [{"name": "Aria", "type": "person"}, {"name": "Meta-Cognition", "type": "project"}, {"name": "Hollowmounts Institute", "type": "organization"}, {"name": "Ren Solanke", "type": "person"}, {"name": "Bridgewater Cognitive Science", "type": "organization"}]}
+{"id": "ent_046", "entities": [{"name": "Fenra", "type": "person"}, {"name": "Hollowmounts", "type": "place"}, {"name": "Salt Flats", "type": "place"}, {"name": "Bryn-iili", "type": "place"}, {"name": "Vroth-Karadz", "type": "place"}, {"name": "Wendelsea Writing Group", "type": "organization"}]}
+{"id": "ent_047", "entities": [{"name": "Bramble", "type": "person"}, {"name": "Yumara Felk", "type": "person"}, {"name": "Bridgewater Community Garden", "type": "organization"}, {"name": "Wendelsea", "type": "place"}, {"name": "Hollowmounts Phenology Network", "type": "organization"}, {"name": "Karis Tornau", "type": "person"}]}
+{"id": "ent_048", "entities": [{"name": "Solas", "type": "person"}, {"name": "Pol Krisat", "type": "person"}, {"name": "Type Systems", "type": "project"}, {"name": "Aerwyn Labs", "type": "organization"}, {"name": "Bek Halloran", "type": "person"}]}
+{"id": "ent_049", "entities": [{"name": "Thresh", "type": "person"}, {"name": "Aerwyn Capital", "type": "organization"}, {"name": "Bridgewater Studio", "type": "organization"}, {"name": "Cashflow Models", "type": "project"}, {"name": "Wendelsea Audit Partners", "type": "organization"}, {"name": "Ivora Tinn", "type": "person"}, {"name": "Bek Halloran", "type": "person"}]}
+{"id": "ent_050", "entities": [{"name": "Aria", "type": "person"}, {"name": "Saela", "type": "person"}, {"name": "Tartine Lab", "type": "organization"}, {"name": "Hollowmounts Institute", "type": "organization"}, {"name": "Aerwyn Labs", "type": "organization"}, {"name": "Crestmoor Cognitive Science", "type": "organization"}]}

benchmarks/model_eval/datasets/memory_extraction/dataset.de.jsonl

@@ -0,0 +1,40 @@
+{"id": "mem_001", "text": "Ich habe entschieden, dass wir für die topic-clustering-Pipeline von cosine similarity auf Jaccard wechseln. Der diskrete Charakter der token-Mengen macht Jaccard zu einer besseren Wahl, und die Ergebnisse mit cosine similarity waren für kurze Dokumente zu verrauscht. Wir werden die Benchmarks morgen erneut ausführen."}
+{"id": "mem_002", "text": "Saela bevorzugt es sehr, Entwürfe in LaTeX statt in Markdown zu verfassen. Sie findet die Gleichungsdarstellung zuverlässiger und die Literaturverwaltung für akademische Arbeit unverzichtbar. Ich werde das künftig respektieren und nicht mehr versuchen, ihre Dateien zu konvertieren."}
+{"id": "mem_003", "text": "Gerade bestätigt: Der neue Serverraum hat eine Umgebungstemperatur von 19°C. Das liegt zwar innerhalb der Spezifikation für die Geräte, aber am kühleren Ende. Laut Datenblatt des Chillers ist der optimale Bereich 21–23°C — wir kühlen mehr als nötig."}
+{"id": "mem_004", "text": "Ehrlich gesagt, ich denke, die Forschungsgemeinschaft hat chain-of-thought prompting überbewertet. Es funktioniert bei mathematischen Benchmarks, weil die Gedankengänge zufällig der zugrunde liegenden Berechnung entsprechen, aber bei schwierigerem Schlussfolgern sind sie nachträgliche Rationalisierungen. Das ist aber nur mein Eindruck."}
+{"id": "mem_005", "text": "Habe dem Nutzer versprochen, dass der erste Entwurf des Übersichtsartikels bis Freitagmorgen fertig sein wird. Habe Mittwoch und Donnerstag für konzentriertes Schreiben blockiert. Der Nutzer war geduldig, und ich möchte liefern."}
+{"id": "mem_006", "text": "Wir haben darüber gesprochen und uns geeinigt: Kein synthetischer NPK-Dünger mehr im Küchengarten. Vollständige Umstellung auf Kompost und eine Winter-Gründüngungs-Rotation. Die Umstellung beginnt diesen Herbst."}
+{"id": "mem_007", "text": "Ivora bevorzugt es, wöchentliche Cashflow-Berichte am Montagmorgen zu erhalten, nicht am Freitagnachmittag. Sie möchte den Ausblick auf die kommende Woche mit ihrem Morgenkaffee sehen, nicht die Zusammenfassung der letzten Woche, wenn sie müde ist. Aktualisierung des automatischen Versandplans."}
+{"id": "mem_008", "text": "Die Einreichungsfrist des Journals von Tartine Lab ist der 15. März. Harte Frist – verspätete Einreichungen werden nicht akzeptiert. Saelas Paper muss bis dahin in ihrem System sein."}
+{"id": "mem_009", "text": "Ich verpflichte mich, die Genauigkeit der OCR-Pipeline bei handgescannten Rechnungen vor Monatsende zu verbessern. Aktuell bei 85 %, Ziel sind 97 %. Sollte ich das nicht erreichen, kehren wir für diesen Dokumententyp zur manuellen Überprüfung zurück."}
+{"id": "mem_010", "text": "Meine Einschätzung zur Row-Polymorphie: Sie ist die richtige Abstraktion für unseren Anwendungsfall, aber das Syntax-Rauschen, das sie in Fehlermeldungen verursacht, wird uns noch Probleme bereiten. Wir müssen erheblich an den Diagnosen arbeiten, bevor wir an externe Nutzer ausliefern können."}
+{"id": "mem_011", "text": "Entscheidung zur Abschreibungsmethode: linear für den Büroausbau, MACRS für die Ausrüstung. Section-179-Wahl nur für die Posten, bei denen wir sicher sind, dass wir sie langfristig nutzen. Ivora hat freigegeben."}
+{"id": "mem_012", "text": "Karis sagte mir, sie würde es vorziehen, wenn ich wöchentliche Updates als Stichpunkte statt langer Absätze schicke. Sie liest sie auf ihrem Handy zwischen Gartenbesuchen, und dichte Prosa passt nicht zu ihrer Arbeitsweise."}
+{"id": "mem_013", "text": "Bestätigt durch die Apfelblüten-Daten: Die diesjährigen Erstblühdaten liegen acht Tage vor dem langjährigen Mittel. Die Aufzeichnungen des Wendelsea Botanic Garden reichen 18 Jahre zurück, und dies ist der drittfrüheste Frühling seit Beginn der Aufzeichnungen."}
+{"id": "mem_014", "text": "Ich denke, die Sache mit „Wends sprechen leiser als Drukar“ wird auf der Seite reduzierend wirken. Völker der realen Welt klingen nicht wie ihre sprachlichen Stereotype, und das Lesen solcher Stereotype kann die Charaktere verflachen. Es lohnt sich, dem Nutzer dagegenzuhalten."}
+{"id": "mem_015", "text": "Ich habe Doreth versprochen, ihr bis Ende der Woche den Schnittplan für die alten Apfelbäume zu mailen. Sie plant ihre Winterarbeit und braucht die Termine, um sie mit ihrem Team abzustimmen."}
+{"id": "mem_016", "text": "Für die Latenzvergleiche Permutationstests statt Mann-Whitney U entschieden. Die geringeren Verteilungsannahmen rechtfertigen den leichten Anstieg der Rechenzeit. Alle bisherigen Vergleiche werden mit der neuen Methode erneut durchgeführt."}
+{"id": "mem_017", "text": "Pol Krisat bevorzugt Code-Reviews über Thread-Kommentare im PR statt separater E-Mails oder Chat-Nachrichten. Sagt, das bindet den Kontext an die Änderung. Übernehme dieselbe Konvention selbst."}
+{"id": "mem_018", "text": "Kurze Info für die Akte: Der Due-Diligence-Zeitraum von Aerwyn Capital beträgt 30 Tage ab Unterzeichnung des Term Sheets. Wir haben bis zum 14. April Zeit, das Cap Table zu bereinigen und die geprüften Finanzunterlagen zusammenzustellen."}
+{"id": "mem_019", "text": "Ehrlich gesagt, bin ich skeptisch gegenüber dem vision-language model des Hollowmounts Institute. Ihre Evaluationsmethodik weist selection bias auf — sie berichten nur über die Prompts, bei denen ihr Modell gewinnt. Die technische Arbeit ist solide, aber das Paper übertreibt."}
+{"id": "mem_020", "text": "Ich werde Ivora die überarbeitete Prognose bis Donnerstagabend schicken. Das gibt ihr das Wochenende zur Durchsicht vor der Vorstandssitzung am Montag. Ich setze es auf meine Liste."}
+{"id": "mem_021", "text": "Saela beschloss, die Übersicht ausschließlich auf contrastive objectives zu konzentrieren — ohne Einbeziehung von clustering-based oder distillation-based Ansätzen. Der Fokus ist enger, aber der Beitrag ist klarer. Ich werde die Gliederung entsprechend umstrukturieren."}
+{"id": "mem_022", "text": "Bramble setzt sich stark dafür ein, in Wiederherstellungsprojekten ausschließlich heimische Pflanzen zu verwenden, selbst wenn diese sich langsamer etablieren als nicht-heimische Alternativen. Die Begründung: Eine schnell etablierende nicht-heimische Art wird zum neuen Problem. Wir bauen diese Präferenz in alle unsere zukünftigen Empfehlungen ein."}
+{"id": "mem_023", "text": "Bestätigung von Brennan Lyle: Das Team von Crestmoor Systems verwendet OTLP HTTP für alle internen Dienste, niemals gRPC. Als Begründung nannten sie Probleme mit version-skew im gesamten polyglot stack. Gut zu wissen für die Kompatibilitätsarbeit."}
+{"id": "mem_024", "text": "Ich bevorzuge Operatorpräzedenz im Pratt-Stil gegenüber generierten Parsern für Sprachen mit komplexen Präzedenzregeln. Die Fehlermeldungen sind dramatisch besser und der Code ist einfacher zu debuggen. Das ist vor allem eine persönliche Ästhetik, aber ich stehe dazu."}
+{"id": "mem_025", "text": "Ich habe mir vorgenommen, diese Woche ein Kapitel ohne KI-Unterstützung zu schreiben, als Kalibrierungstest. Ich möchte das Ergebnis mit KI-unterstützten Kapiteln vergleichen und sehen, ob ich mich zu sehr auf das System verlasse. Ich werde den Vergleich mit der Wendelsea Writing Group teilen."}
+{"id": "mem_026", "text": "Wir haben für den neuen Index B-tree statt skip list gewählt. Lesedominante Workload, Copy-on-Write-Schreibzugriffe, besseres Cache-Verhalten. Die Entscheidung ist im Architektur-Wiki dokumentiert."}
+{"id": "mem_027", "text": "Die Dissertationsverteidigung von Ren Solanke ist am 6. Mai. Ren hat gefragt, ob ich bereit wäre, einen Kapitelentwurf vorab zu lesen. Habe zugesagt – verpflichtet mich zu einer Feedback-Runde bis zum 22. April, damit Ren noch Zeit zur Überarbeitung vor der Verteidigung hat."}
+{"id": "mem_028", "text": "Fakt: Der Bezirk Wendelsea liegt auf Kalkstein-Grundgestein. Der pH-Wert des Bodens driftet natürlicherweise in Richtung neutral oder leicht alkalisch. Jeder Blaubeeranbauer in diesem Gebiet wird ohne Eingriff Chlorose bekämpfen müssen."}
+{"id": "mem_029", "text": "Ich denke, der Anhang zur Sprache im Aerwyn-Zyklus wird polarisieren. Manche Leser lieben Artefakte des tiefen Weltbaus; andere empfinden sie als Selbstzweck. Meiner Meinung nach trägt er wesentlich zur Erfüllung des salt-debt-Motivs im letzten Akt bei."}
+{"id": "mem_030", "text": "Der Benutzer hat beschlossen, den Secondary Sale um ein Quartal zu verschieben. Möchte das Cap Table bereinigen, bevor neue Investoren Due Diligence durchführen. Verschiebt die relevanten Arbeitspakete im Projektplan auf Q2."}
+{"id": "mem_031", "text": "Pell Halloran bevorzugt für regelmäßige Abstimmungsfragen E-Mail statt Telefon – das hinterlässt eine schriftliche Spur. Alle Standard-AR-Anfragen werden künftig per E-Mail geleitet. Telefon nur für dringende Eskalationen reserviert."}
+{"id": "mem_032", "text": "Die neuen Mauerbienenkolonien im Bridgewater Community Garden schlüpften am 18. März dieses Jahres. Das ist zwei Tage früher als die Apfelblüte im Wendelsea Orchard, die am 20. März begann. Leichte phänologische Diskrepanz, aber innerhalb der Toleranz."}
+{"id": "mem_033", "text": "Ich habe Saela versprochen, ihren vollständigen Manuskriptentwurf durchzusehen, bevor ihr Sabbatical am 1. Juni beginnt. Zwei Monate für ein 60-seitiges Paper. Knapp, aber machbar, wenn ich jeden Mittwoch dafür freihalte."}
+{"id": "mem_034", "text": "Ehrlich gesagt finde ich den aktuellen Trend zu agentischen Code-Pipelines anstrengend. Sie generieren Code schneller, als jemand ihn überprüfen kann, und die Fehler werden schwerer nachzuvollziehen. Ich werde sie weiterhin nutzen, aber ich würde es vorziehen, wenn wir langsamer machen und Verständnis aufbauen."}
+{"id": "mem_035", "text": "Wir haben beschlossen, für die Invoice Parsing-Pipeline von Tesseract zu PaddleOCR zu wechseln. Die layoutbewusste Extraktion verarbeitet Tabellen deutlich besser. Die Migration wird etwa eine Woche dauern, und wir werden beide während des Übergangs parallel betreiben."}
+{"id": "mem_036", "text": "Ivora bevorzugt konservative Liquiditätsprognosen gegenüber optimistischen, selbst auf Kosten der Genauigkeit. Ihre Begründung: Positive Überraschungen sind angenehm, negative Überraschungen sind Krisen. Wir passen die Berichterstattung unseres Modells entsprechend an."}
+{"id": "mem_037", "text": "Karis möchte, dass ich am 10. April persönlich an der Sitzung des Gartenausschusses der Bridgewater Schools teilnehme, nicht per Videoanruf. Sie glaubt, dass die Entscheidungsträger der Bridgewater Schools stärker auf jemanden eingehen, der physisch anwesend ist. Habe zugesagt, dort zu sein."}
+{"id": "mem_038", "text": "Zwei Fakten, die es sich lohnt zu notieren: Der Apfelschorf-Pilz überwintert in abgefallenen Blättern, daher ist die Laubentfernung im Herbst entscheidend. Und Kupferspritzmittel wirken nur beim Anschwellen der Knospen – sobald diese aufbrechen, ist das Zeitfenster verpasst."}
+{"id": "mem_039", "text": "Ich finde, der Instinkt des Nutzers, Saelas Entscheidung im dritten Akt zu einem Moment kraftvoller Aktion statt eines Zusammenbruchs zu machen, ist genau richtig. Es macht dem Bogen Ehre und vermeidet das Klischee. Meine Meinung, aber ich bin fest davon überzeugt."}
+{"id": "mem_040", "text": "Solas hat zugesagt, den Fix für Distributed Tracing bis zum Sprintende (Freitag) zu liefern. Der Fehler blockiert das Aerwyn Telemetry consortium seit zwei Wochen; sie benötigen den Patch, um ausliefern zu können. Brennan wird die Regressionstests am Donnerstag durchführen."}

benchmarks/model_eval/datasets/memory_extraction/dataset.es.jsonl

@@ -0,0 +1,40 @@
+{"id": "mem_001", "text": "He decidido que vamos a cambiar de similitud de coseno a Jaccard para el proceso de agrupación de temas. La naturaleza discreta de los conjuntos de tokens hace que Jaccard se ajuste mejor y los resultados del coseno fueron demasiado ruidosos para documentos cortos. Mañana volveremos a ejecutar los puntos de referencia."}
+{"id": "mem_002", "text": "Saela prefiere escribir borradores en LaTeX en lugar de Markdown. Considera que la ecuación se vuelve más confiable y la gestión de la bibliografía es esencial para el trabajo académico. Respetaré eso en el futuro y dejaré de intentar convertir sus archivos."}
+{"id": "mem_003", "text": "Recién confirmado: la nueva sala de servidores está a una temperatura ambiente de 19°C. Eso está dentro de las especificaciones del equipo, pero en el lado bueno. La hoja de especificaciones del enfriador dice que lo óptimo es entre 21 y 23 °C: estamos enfriando más de lo necesario."}
+{"id": "mem_004", "text": "Honestamente, creo que el campo ha sobrevalorado la cadena de pensamiento. Funciona en puntos de referencia matemáticos porque los rastros de razonamiento coinciden con el cálculo subyacente, pero en razonamientos más complejos los rastros son racionalizaciones post hoc. Aunque esa es sólo mi lectura."}
+{"id": "mem_005", "text": "Le prometí al usuario que tendría listo el primer borrador del documento de encuesta el viernes por la mañana. He bloqueado los miércoles y jueves para escribir de forma centrada. Han sido pacientes y quiero cumplir."}
+{"id": "mem_006", "text": "Lo hablamos y acordamos: no más fertilizantes sintéticos NPK en el huerto. Cambiar por completo a enmiendas de compost y una rotación de cultivos de cobertura de invierno. Comenzará la transición este otoño."}
+{"id": "mem_007", "text": "Ivora prefiere recibir informes de flujo de caja semanales los lunes por la mañana, no los viernes por la tarde. Quiere ver el panorama de la próxima semana con su café de la mañana, no el resumen de la semana pasada cuando está cansada. Actualización del cronograma de envío automático."}
+{"id": "mem_008", "text": "La fecha límite de envío de la revista Tartine Lab es el 15 de marzo. Fecha límite estricta: no aceptarán envíos tardíos. El documento de Saela debe estar en su sistema para entonces."}
+{"id": "mem_009", "text": "Me comprometo a corregir la precisión del proceso de OCR en las facturas escaneadas manualmente antes de fin de mes. Actualmente en 85%, el objetivo es 97%. Si no puedo alcanzarlo, volvemos a la revisión manual para ese tipo de documento."}
+{"id": "mem_010", "text": "Mi opinión sobre el polimorfismo de filas: es la abstracción correcta para nuestro caso de uso, pero el ruido de sintaxis que introduce en los mensajes de error nos va a afectar. Necesitaremos un trabajo importante en el diagnóstico antes de poder realizar envíos a usuarios externos."}
+{"id": "mem_011", "text": "Decisión sobre el método de depreciación: línea recta para el edificio de oficinas, MACRS para el equipo. Elección de la Sección 179 solo en los artículos que estamos seguros que usaremos a largo plazo. Ivora cerró sesión."}
+{"id": "mem_012", "text": "Karis me dijo que preferiría que le enviara actualizaciones semanales con viñetas en lugar de párrafos largos. Los lee en su teléfono entre visitas al jardín y la prosa densa no funciona con su flujo de trabajo."}
+{"id": "mem_013", "text": "Confirmado a través de los datos de floración de las manzanas: las fechas de la primera floración de este año son ocho días antes que la media a largo plazo. Los registros del Wendelsea Botanic Garden se remontan a 18 años y esta es la tercera primavera más temprana registrada."}
+{"id": "mem_014", "text": "Creo que lo de 'Wends habla más suave que Drukar' se sentirá reduccionista en la página. Las personas del mundo real no se parecen a sus estereotipos lingüísticos, y leer esos estereotipos puede aplanar a los personajes. Vale la pena responder al usuario al respecto."}
+{"id": "mem_015", "text": "Le prometí a Doreth que le enviaría por correo electrónico el calendario de poda de las manzanas tradicionales antes de fin de semana. Está planificando su trabajo de invierno y necesita las fechas para coordinarlas con su equipo de trabajo."}
+{"id": "mem_016", "text": "Se decidió por pruebas de permutación en lugar de Mann-Whitney U para las comparaciones de latencia. Los menos supuestos distributivos justifican el ligero aumento en el tiempo de cálculo. Volviendo a ejecutar todas las comparaciones anteriores con el nuevo método."}
+{"id": "mem_017", "text": "Pol Krisat prefiere revisiones de código a través de comentarios encadenados en el PR en lugar de correos electrónicos o mensajes de chat separados. Dice que mantiene el contexto adjunto al cambio. Adoptando la misma convención yo mismo."}
+{"id": "mem_018", "text": "Dato breve para el archivo: el período de diligencia de Aerwyn Capital es de 30 días a partir de la firma de la hoja de términos. Tenemos hasta el 14 de abril para limpiar la tabla de límites máximos y empaquetar las finanzas auditadas."}
+{"id": "mem_019", "text": "Honestamente, soy escéptico sobre el modelo visión-lenguaje del Hollowmounts Institute. Su metodología de evaluación tiene un sesgo de selección: solo informan sobre las indicaciones en las que gana su modelo. El trabajo técnico es sólido pero el papel se vende demasiado."}
+{"id": "mem_020", "text": "Enviaré el pronóstico revisado a Ivora antes del final del día del jueves. Eso le da el fin de semana para revisar antes de la reunión de la junta directiva del lunes. Poniéndolo en mi lista."}
+{"id": "mem_021", "text": "Saela decidió centrar la encuesta únicamente en objetivos contrastantes, sin incluir enfoques basados ​​en agrupaciones o en destilación. El alcance es más limitado pero la contribución es más clara. Reestructuraré el esquema en consecuencia."}
+{"id": "mem_022", "text": "Bramble prefiere firmemente las plantaciones exclusivamente nativas para proyectos de restauración, incluso cuando eso signifique un establecimiento más lento que las alternativas no nativas. El argumento: un no nativo que se establece rápidamente se convierte en el nuevo problema. Incorporar esta preferencia a todas nuestras recomendaciones futuras."}
+{"id": "mem_023", "text": "Confirmado con Brennan Lyle: el equipo Crestmoor Systems utiliza OTLP HTTP para todos sus servicios internos, nunca gRPC. Su razonamiento fue problemas de sesgo de versiones en toda la pila políglota. Es útil saberlo para trabajos de compatibilidad."}
+{"id": "mem_024", "text": "Prefiero la precedencia de operadores estilo Pratt a los analizadores generados para lenguajes con reglas de precedencia complejas. Los mensajes de error son muchísimo mejores y el código es más fácil de depurar. Esto es principalmente una estética personal, pero la defenderé."}
+{"id": "mem_025", "text": "Comprometerse a escribir un capítulo esta semana sin asistencia de IA, como verificación de calibración. Quiero comparar el resultado con los capítulos asistidos por IA y ver si me estoy apoyando demasiado en el sistema. Compartirá la comparación con el Wendelsea Writing Group."}
+{"id": "mem_026", "text": "Elegimos el árbol B en lugar de la lista de omisión para el nuevo índice. Carga de trabajo de lectura principalmente, escrituras de copia en escritura, mejor comportamiento de caché. La decisión está documentada en la wiki de arquitectura."}
+{"id": "mem_027", "text": "La defensa de la tesis de Ren Solanke es el 6 de mayo. Me preguntaron si estaría dispuesto a leer previamente un borrador del capítulo. Dijo que sí: se comprometió a realizar una ronda de comentarios antes del 22 de abril para que tengan tiempo de revisar antes de la defensa."}
+{"id": "mem_028", "text": "Hecho: el distrito Wendelsea se asienta sobre un lecho de roca caliza. El pH del suelo tiende naturalmente a ser neutro o ligeramente alcalino. Cualquier productor de arándanos de esa zona va a luchar contra la clorosis sin intervención."}
+{"id": "mem_029", "text": "Creo que el apéndice del ciclo de Aerwyn sobre el lenguaje será un tema de \"ámalo u odialo\". A algunos lectores les encantan los artefactos de construcción de mundos profundos; otros los encuentran indulgentes. Mi lectura es que contribuye significativamente a la recompensa del motivo de la deuda de sal en el acto final."}
+{"id": "mem_030", "text": "El usuario decidió retrasar la venta secundaria una cuarta parte. Quiere limpiar la tabla de límites antes de cualquier nueva diligencia de inversores. Enviar los elementos de trabajo relevantes al segundo trimestre del plan del proyecto."}
+{"id": "mem_031", "text": "Pell Halloran prefiere el correo electrónico al teléfono para las preguntas rutinarias de conciliación: le proporciona un rastro escrito. Enrutará todas las consultas AR estándar a través del correo electrónico en el futuro. Teléfono reservado únicamente para escalaciones urgentes."}
+{"id": "mem_032", "text": "Las nuevas colonias de abejas albañiles en Bridgewater Community Garden surgieron el 18 de marzo de este año. Eso es dos días antes que la floración del manzano en Wendelsea Orchard, que comenzó el 20 de marzo. Ligera discrepancia fenológica pero dentro de la tolerancia."}
+{"id": "mem_033", "text": "Le prometí a Saela que revisaría el borrador completo del manuscrito antes de que comience su año sabático el 1 de junio. Dos meses para un artículo de 60 páginas. Apretado pero viable si le dedico los miércoles."}
+{"id": "mem_034", "text": "Honestamente, encuentro agotadora la tendencia actual hacia los canales de codificación agente. Generan código más rápido de lo que cualquiera puede revisarlo y los errores se vuelven más difíciles de rastrear. Seguiré usándolos, pero prefiero ir más despacio y desarrollar la comprensión."}
+{"id": "mem_035", "text": "Decidió cambiar de Tesseract a PaddleOCR para el proceso de análisis de facturas. La extracción basada en el diseño maneja las tablas mucho mejor. La migración tomará aproximadamente una semana y mantendremos ambas funcionando en paralelo durante la transición."}
+{"id": "mem_036", "text": "Ivora prefiere los pronósticos de efectivo conservadores a los optimistas, incluso a costa de la precisión. Su razonamiento: las sorpresas positivas son agradables, las sorpresas negativas son crisis. Ajustando los informes de nuestro modelo en consecuencia."}
+{"id": "mem_037", "text": "Karis quiere que asista a la reunión del comité de jardinería Bridgewater Schools en persona el 10 de abril, no mediante videollamada. Ella cree que quienes toman las decisiones en las escuelas se involucrarán más con alguien físicamente presente. Confirmado que estaré allí."}
+{"id": "mem_038", "text": "Dos datos que vale la pena presentar: el hongo de la sarna de la manzana pasa el invierno en las hojas caídas, por lo que la eliminación de las hojas es fundamental en el otoño. Y los aerosoles de cobre solo son efectivos para hinchar los cogollos: una vez que los cogollos se abren, se pierde la ventana."}
+{"id": "mem_039", "text": "Creo que el instinto del usuario de hacer de la decisión del tercer acto de Saela un momento de acción feroz en lugar de un colapso es exactamente correcto. Hace honor al arco y evita el cliché. Mi opinión, pero la siento firmemente."}
+{"id": "mem_040", "text": "Solas se comprometió a enviar la solución Distributed Tracing al final del sprint (viernes). El error ha estado bloqueando al consorcio Aerwyn Telemetry durante dos semanas; Necesitan el parche para enviar. Brennan ejecutará las pruebas de regresión el jueves."}

benchmarks/model_eval/datasets/memory_extraction/dataset.fr.jsonl

@@ -0,0 +1,40 @@
+{"id": "mem_001", "text": "J'ai décidé que nous allons passer de cosine similarity à Jaccard pour le pipeline Topic Clustering. La nature discrète des token sets rend Jaccard plus adapté, et les résultats de cosine similarity étaient trop bruités pour les documents courts. Nous relancerons les benchmarks demain."}
+{"id": "mem_002", "text": "Saela préfère nettement rédiger ses brouillons en LaTeX plutôt qu'en Markdown. Elle trouve le rendu des équations plus fiable et la gestion bibliographique essentielle pour le travail académique. Je respecterai cela à l'avenir et cesserai d'essayer de convertir ses fichiers."}
+{"id": "mem_003", "text": "Vient d'être confirmé : la nouvelle salle des serveurs est à 19°C ambiante. C'est conforme aux spécifications pour l'équipement, mais c'est du côté frais. La fiche technique du refroidisseur indique que l'optimal est 21-23°C — on refroidit plus que nécessaire."}
+{"id": "mem_004", "text": "Honnêtement, je pense que le domaine a surestimé le chain-of-thought prompting. Ça fonctionne sur les benchmarks de maths parce que les traces de raisonnement correspondent par hasard au calcul sous-jacent, mais sur un raisonnement plus difficile, les traces sont des rationalisations a posteriori. C'est juste mon interprétation, cela dit."}
+{"id": "mem_005", "text": "J'ai promis à l'utilisateur que j'aurais le premier brouillon du document de synthèse prêt pour vendredi matin. J'ai bloqué mercredi et jeudi pour me consacrer à l'écriture. L'utilisateur a été patient et je veux tenir ma promesse."}
+{"id": "mem_006", "text": "Nous en avons parlé et nous sommes mis d'accord : plus d'engrais NPK synthétiques dans le potager. Passage intégral aux amendements de compost et à une rotation de cultures de couverture hivernales. La transition commencera cet automne."}
+{"id": "mem_007", "text": "Ivora préfère recevoir les rapports de trésorerie hebdomadaires le lundi matin, pas le vendredi après-midi. Elle veut consulter les perspectives de la semaine à venir avec son café du matin, pas le récapitulatif de la semaine dernière quand elle est fatiguée. Mise à jour de la planification d'envoi automatique."}
+{"id": "mem_008", "text": "La date limite de soumission de la revue de Tartine Lab est le 15 mars. Date butoir stricte — aucun retard ne sera toléré. L'article de Saela doit être dans leur système d'ici là."}
+{"id": "mem_009", "text": "Je m'engage à améliorer la précision du pipeline OCR pour les factures numérisées à la main avant la fin du mois. Nous sommes actuellement à 85 %, l'objectif est de 97 %. Si je n'y parviens pas, nous repasserons à la vérification manuelle pour ce type de document."}
+{"id": "mem_010", "text": "Mon avis sur le row polymorphism : c'est la bonne abstraction pour notre cas d'usage, mais le bruit syntaxique qu'il introduit dans les messages d'erreur va nous poser problème. Nous devrons faire un travail considérable sur les diagnostics avant de pouvoir livrer à des utilisateurs externes."}
+{"id": "mem_011", "text": "Décision prise concernant la méthode d'amortissement : linéaire pour l'aménagement du bureau, MACRS pour le matériel. Élection Section 179 uniquement pour les éléments que nous sommes certains d'utiliser à long terme. Ivora a approuvé."}
+{"id": "mem_012", "text": "Karis m'a dit qu'elle préférait que je lui envoie des mises à jour hebdomadaires sous forme de puces plutôt que de longs paragraphes. Elle les lit sur son téléphone entre deux visites de jardins, et la prose dense ne convient pas à son mode de travail."}
+{"id": "mem_013", "text": "Confirmé par les données de floraison des pommiers : les dates de début de floraison de cette année sont huit jours plus tôt que la moyenne à long terme. Les archives de Wendelsea Botanic Garden remontent à 18 ans et il s'agit du troisième printemps le plus précoce depuis le début des relevés."}
+{"id": "mem_014", "text": "Je pense que le côté « les Wends parlent plus doucement que les Drukar » va sembler réducteur sur la page. Les peuples réels ne correspondent pas aux stéréotypes linguistiques, et lire ces stéréotypes peut aplatir les personnages. Ça vaut le coup de contredire l'utilisateur là-dessus."}
+{"id": "mem_015", "text": "J'ai promis à Doreth de lui envoyer par e-mail le calendrier de taille des pommiers patrimoniaux d'ici la fin de la semaine. Elle planifie son travail d'hiver et a besoin des dates pour se coordonner avec son équipe de travail."}
+{"id": "mem_016", "text": "Décision d'utiliser des permutation tests plutôt que Mann-Whitney U pour les comparaisons de latence. Moins d'hypothèses de distribution justifient la légère augmentation du temps de calcul. Relance de toutes les comparaisons précédentes avec la nouvelle méthode."}
+{"id": "mem_017", "text": "Pol Krisat préfère les revues de code via des commentaires en fil sur la PR plutôt que des e-mails ou des messages de chat séparés. Dit que cela permet de garder le contexte attaché à la modification. J'adopte la même convention."}
+{"id": "mem_018", "text": "Info rapide pour le dossier : la période de due diligence d'Aerwyn Capital est de 30 jours à compter de la signature du term sheet. Nous avons jusqu'au 14 avril pour mettre la cap table au propre et préparer les états financiers audités."}
+{"id": "mem_019", "text": "Honnêtement, je suis sceptique quant au modèle vision-langage développé par Hollowmounts Institute. Leur méthodologie d'évaluation présente un biais de sélection — ils ne rapportent que les prompts où leur modèle l'emporte. Le travail technique est solide, mais l'article exagère."}
+{"id": "mem_020", "text": "J'enverrai le prévisionnel révisé à Ivora d'ici jeudi soir. Ça lui laisse le week-end pour l'examiner avant la réunion du conseil de lundi. Je l'ajoute à ma liste."}
+{"id": "mem_021", "text": "Saela a décidé de centrer l'étude uniquement sur les objectifs contrastifs — sans inclure les approches basées sur le clustering ou la distillation. Le périmètre est plus restreint mais la contribution est plus claire. Je vais restructurer le plan en conséquence."}
+{"id": "mem_022", "text": "Bramble préfère fortement les plantations exclusivement indigènes pour les projets de restauration, même si cela implique une implantation plus lente qu'avec des alternatives non indigènes. L'argument : une espèce non indigène qui s'établit rapidement devient le nouveau problème. Nous intégrons cette préférence dans toutes nos futures recommandations."}
+{"id": "mem_023", "text": "Confirmé auprès de Brennan Lyle : l'équipe de Crestmoor Systems utilise OTLP HTTP pour tous ses services internes, jamais gRPC. Leur raisonnement reposait sur des problèmes de version-skew sur le polyglot stack. Utile à savoir pour le travail de compatibilité."}
+{"id": "mem_024", "text": "Je préfère la précédence des opérateurs de style Pratt aux parseurs générés pour les langages dotés de règles de précédence complexes. Les messages d'erreur sont nettement meilleurs et le code est plus facile à déboguer. C'est surtout une préférence esthétique personnelle, mais je la défendrai."}
+{"id": "mem_025", "text": "Je m'engage à écrire un chapitre cette semaine sans assistance AI, comme vérification d'étalonnage. Je veux comparer le résultat à des chapitres assistés par AI et voir si je m'appuie trop sur le système. Je partagerai la comparaison avec le Wendelsea Writing Group."}
+{"id": "mem_026", "text": "Nous avons choisi B-tree plutôt que skip list pour le nouvel index. Charge principalement en lecture, écritures copy-on-write, meilleur comportement de cache. La décision est documentée dans le wiki d'architecture."}
+{"id": "mem_027", "text": "La soutenance de thèse de Ren Solanke est le 6 mai. Ils m'ont demandé si je serais d'accord pour relire en amont un brouillon de chapitre. J'ai dit oui — je me suis engagé à leur faire mes retours en une passe d'ici le 22 avril pour qu'ils aient le temps de réviser avant la soutenance."}
+{"id": "mem_028", "text": "Fait : le district de Wendelsea repose sur un socle calcaire. Le pH du sol tend naturellement vers des valeurs neutres ou légèrement alcalines. Tout cultivateur de myrtilles dans cette région va devoir lutter contre la chlorose sans intervention."}
+{"id": "mem_029", "text": "Je pense que l'appendice sur la langue du cycle d'Aerwyn va être un élément qui divise. Certains lecteurs adorent les artefacts d'une construction du monde profonde ; d'autres les trouvent complaisants. Ma lecture est que cela ajoute de manière significative à la résolution du motif de la dette de sel dans l'acte final."}
+{"id": "mem_030", "text": "L'utilisateur a décidé de reporter la vente secondaire d'un trimestre. Il souhaite nettoyer la table de capitalisation avant toute due diligence de nouveaux investisseurs. Report des éléments de travail pertinents au Q2 dans le plan de projet."}
+{"id": "mem_031", "text": "Pell Halloran préfère l'e-mail au téléphone pour les questions de rapprochement de routine — cela lui donne une trace écrite. Toutes les demandes AR standard seront dorénavant acheminées par e-mail. Le téléphone est réservé uniquement aux escalades urgentes."}
+{"id": "mem_032", "text": "Les nouvelles colonies d'abeilles maçonnes du jardin communautaire de Bridgewater ont émergé le 18 mars cette année. C'est deux jours plus tôt que la floraison des pommiers au verger de Wendelsea, qui a commencé le 20 mars. Léger décalage phénologique, mais dans les limites de tolérance."}
+{"id": "mem_033", "text": "J'ai promis à Saela de relire le brouillon complet de son manuscrit avant que son congé sabbatique ne commence le 1er juin. Deux mois pour un article de 60 pages. C'est juste, mais faisable si je lui consacre mes mercredis."}
+{"id": "mem_034", "text": "Honnêtement, je trouve la tendance actuelle vers les pipelines de codage agentique épuisante. Ils génèrent du code plus vite que quiconque ne peut le réviser, et les bugs deviennent plus difficiles à tracer. Je continuerai à les utiliser, mais je préférerais que nous ralentissions et développions notre compréhension."}
+{"id": "mem_035", "text": "Nous avons décidé de passer de Tesseract à PaddleOCR pour le pipeline invoice parsing. L'extraction sensible à la mise en page gère beaucoup mieux les tableaux. La migration prendra environ une semaine, et nous ferons tourner les deux en parallèle pendant la transition."}
+{"id": "mem_036", "text": "Ivora préfère les prévisions de trésorerie prudentes aux optimistes, même au détriment de la précision. Son raisonnement : les surprises à la hausse sont agréables, les surprises à la baisse sont des crises. Ajustement des rapports de notre modèle en conséquence."}
+{"id": "mem_037", "text": "Karis veut que j'assiste en personne à la réunion du comité du jardin de Bridgewater Schools le 10 avril, et non par visioconférence. Elle pense que les décideurs de Bridgewater Schools s'engageront davantage avec une personne physiquement présente. J'ai confirmé que je serai là."}
+{"id": "mem_038", "text": "Deux faits à retenir : le champignon de la tavelure du pommier hiverne dans les feuilles mortes, d'où l'importance du ramassage des feuilles en automne. Les traitements à base de cuivre, quant à eux, ne sont efficaces que lors du gonflement des bourgeons — une fois ceux-ci ouverts, vous avez manqué le créneau."}
+{"id": "mem_039", "text": "Je pense que l'utilisateur a eu l'instinct parfait de faire de la décision de Saela au troisième acte un moment d'action féroce plutôt qu'un effondrement. Cela respecte l'arc et évite le cliché. C'est mon avis, mais j'y tiens fermement."}
+{"id": "mem_040", "text": "Solas s'est engagé à livrer le correctif de Distributed Tracing d'ici la fin du sprint (vendredi). Ce bogue bloque le consortium Aerwyn Telemetry depuis deux semaines ; ils ont besoin du correctif pour livrer. Brennan exécutera les tests de régression jeudi."}

benchmarks/model_eval/datasets/memory_extraction/dataset.hi.jsonl

@@ -0,0 +1,40 @@
+{"id": "mem_001", "text": "मैंने तय किया है कि हम topic-clustering पाइपलाइन में cosine similarity की जगह Jaccard का उपयोग करेंगे। टोकन सेट्स के डिस्क्रेट स्वभाव के कारण Jaccard अधिक उपयुक्त है, और छोटे दस्तावेज़ों के लिए cosine के परिणाम बहुत नॉइज़ी थे। हम कल बेंचमार्क्स फिर से चलाएँगे।"}
+{"id": "mem_002", "text": "Saela Markdown के बजाय LaTeX में ड्राफ्ट लिखना बहुत पसंद करती है। उसे लगता है कि समीकरण रेंडरिंग ज़्यादा भरोसेमंद है और शैक्षणिक काम के लिए बिब्लियोग्राफी प्रबंधन बेहद ज़रूरी है। मैं आगे से इस बात का सम्मान करूँगा और उसकी फ़ाइलों को बदलने की कोशिश करना बंद कर दूँगा।"}
+{"id": "mem_003", "text": "अभी-अभी पुष्टि हुई: नया सर्वर रूम 19°C एम्बिएंट तापमान पर है। यह उपकरणों की स्पेसिफिकेशन के भीतर है, लेकिन ठंडे छोर पर। चिलर की स्पेसिफिकेशन शीट के अनुसार इष्टतम 21-23°C है — हम ज़रूरत से ज़्यादा ठंडा कर रहे हैं।"}
+{"id": "mem_004", "text": "सच कहूँ तो, मुझे लगता है कि इस क्षेत्र ने chain-of-thought prompting को ज़्यादा महत्व दिया है। यह गणित बेंचमार्क पर इसलिए काम करता है क्योंकि तर्क के निशान संयोगवश अंतर्निहित संगणना से मेल खाते हैं, लेकिन कठिन तर्क में ये निशान पश्चात्-घटित औचित्यीकरण होते हैं। हालाँकि, यह सिर्फ मेरी समझ है।"}
+{"id": "mem_005", "text": "मैंने उपयोगकर्ता से वादा किया था कि शुक्रवार की सुबह तक सर्वेक्षण पत्र का पहला ड्राफ्ट तैयार हो जाएगा। मैंने बुधवार और गुरुवार को केंद्रित लेखन के लिए अलग रखे हैं। उन्होंने धैर्य रखा है और मैं अपना वादा निभाना चाहता हूँ।"}
+{"id": "mem_006", "text": "हमने इस बारे में बात की और सहमति बनी: रसोई के बगीचे में अब कोई कृत्रिम NPK उर्वरक नहीं। पूरी तरह से कम्पोस्ट संशोधनों और शीतकालीन कवर फसल रोटेशन अपना रहे हैं। इस शरद ऋतु में इस परिवर्तन की शुरुआत करेंगे।"}
+{"id": "mem_007", "text": "Ivora साप्ताहिक नकदी प्रवाह रिपोर्ट सोमवार की सुबह में प्राप्त करना पसंद करती हैं, शुक्रवार की दोपहर में नहीं। वह अपनी सुबह की कॉफी के साथ आगामी सप्ताह का दृष्टिकोण देखना चाहती हैं, न कि पिछले सप्ताह का सारांश जब वह थकी हों। स्वचालित भेजने का शेड्यूल अपडेट किया जा रहा है।"}
+{"id": "mem_008", "text": "Tartine Lab जर्नल की सबमिशन डेडलाइन 15 मार्च है। यह सख्त डेडलाइन है — वे देरी से किए गए सबमिशन स्वीकार नहीं करेंगे। Saela का पेपर तब तक उनके सिस्टम में होना चाहिए।"}
+{"id": "mem_009", "text": "मैं माह के अंत से पहले हाथ से स्कैन किए गए इनवॉइस पर OCR pipeline की सटीकता को ठीक करने के लिए प्रतिबद्ध हूँ। फिलहाल यह 85% है, लक्ष्य 97% है। यदि मैं इसे हासिल नहीं कर सका, तो हम उस दस्तावेज़ प्रकार के लिए मैनुअल समीक्षा पर वापस लौट आएँगे।"}
+{"id": "mem_010", "text": "row polymorphism पर मेरा नज़रिया: यह हमारे use case के लिए सही abstraction है, लेकिन त्रुटि संदेशों में इसकी वजह से आने वाला syntax noise हमारे लिए मुसीबत बनेगा। इसे बाहरी उपयोगकर्ताओं तक पहुंचाने से पहले हमें diagnostics पर काफी काम करना होगा।"}
+{"id": "mem_011", "text": "मूल्यह्रास विधि पर निर्णय: कार्यालय निर्माण के लिए सीधी रेखा विधि, उपकरणों के लिए MACRS। केवल उन मदों पर Section 179 चुनाव जिनका हमें यकीन है कि हम उन्हें दीर्घकालिक रूप से उपयोग करेंगे। Ivora ने मंजूरी दे दी।"}
+{"id": "mem_012", "text": "Karis ने मुझसे कहा कि वह चाहती है कि मैं साप्ताहिक अपडेट लंबे पैराग्राफ़ के बजाय बुलेट पॉइंट्स में भेजूँ। वह उन्हें बगीचे के दौरों के बीच अपने फोन पर पढ़ रही है और घना गद्य उसके कार्यप्रवाह के लिए उपयुक्त नहीं है।"}
+{"id": "mem_013", "text": "सेब के फूलने के आंकड़ों से पुष्टि हुई है: इस वर्ष पहले फूलने की तारीखें दीर्घकालिक औसत से आठ दिन पहले हैं। Wendelsea Botanic Garden के रिकॉर्ड 18 साल पुराने हैं और यह रिकॉर्ड किए गए इतिहास में तीसरा सबसे जल्दी आने वाला वसंत है।"}
+{"id": "mem_014", "text": "मुझे लगता है कि 'Wends Drukar से मृदु बोलते हैं' वाली बात पन्ने पर सरलीकृत लगेगी। वास्तविक दुनिया के लोग अपनी भाषाई रूढ़ियों जैसे नहीं लगते, और उन रूढ़ियों को पढ़ना पात्रों को सपाट बना सकता है। इस बारे में उपयोगकर्ता से अपनी बात रखना उचित है।"}
+{"id": "mem_015", "text": "मैंने Doreth से वादा किया था कि मैं सप्ताह के अंत तक उसे विरासती सेबों की छंटाई की समय-सारणी ईमेल कर दूँगा। वह अपने सर्दियों के काम की योजना बना रही है और अपनी मज़दूर टीम के साथ तालमेल बिठाने के लिए उसे तारीखों की ज़रूरत है।"}
+{"id": "mem_016", "text": "latency तुलनाओं के लिए Mann-Whitney U के बजाय permutation tests तय किए गए। कम distributional assumptions गणना समय में मामूली वृद्धि को उचित ठहराती हैं। नई विधि के तहत पिछली सभी तुलनाओं को फिर से चलाया जा रहा है।"}
+{"id": "mem_017", "text": "Pol Krisat PR में टिप्पणी धागों के माध्यम से कोड समीक्षा पसंद करते हैं, अलग ईमेल या चैट संदेशों के बजाय। कहते हैं कि इससे संदर्भ बदलाव से जुड़ा रहता है। मैं भी वही प्रथा अपना रहा हूँ।"}
+{"id": "mem_018", "text": "फाइल के लिए एक त्वरित तथ्य: Aerwyn Capital की diligence अवधि term sheet पर हस्ताक्षर करने की तारीख से 30 दिन की है। हमारे पास 14 अप्रैल तक का समय है cap table को साफ़ करने और audited financials को पैकेज करने के लिए।"}
+{"id": "mem_019", "text": "ईमानदारी से कहूँ तो, मुझे Hollowmounts Institute के दृश्य-भाषा मॉडल पर संदेह है। उनकी मूल्यांकन पद्धति में चयन पूर्वाग्रह है — वे केवल उन प्रॉम्प्ट्स पर रिपोर्ट करते हैं जहाँ उनका मॉडल जीतता है। तकनीकी कार्य ठोस है, लेकिन पेपर में अतिशयोक्ति है।"}
+{"id": "mem_020", "text": "मैं गुरुवार शाम तक Ivora को संशोधित पूर्वानुमान भेज दूँगा। इससे उसे सोमवार की बोर्ड बैठक से पहले समीक्षा करने के लिए सप्ताहांत मिल जाएगा। मैं इसे अपनी सूची में डाल रहा हूँ।"}
+{"id": "mem_021", "text": "Saela ने सर्वेक्षण को केवल contrastive objectives पर ही केंद्रित करने का निर्णय लिया — clustering-based या distillation-based पद्धतियों को शामिल नहीं किया जाएगा। इससे दायरा सीमित हो गया है, लेकिन योगदान अधिक स्पष्ट है। मैं इसके अनुसार रूपरेखा को फिर से संरचित करूँगा।"}
+{"id": "mem_022", "text": "Bramble पुनर्स्थापना परियोजनाओं में केवल देशी पौधों के रोपण को प्रबल रूप से वरीयता देते हैं, भले ही इसका अर्थ गैर-देशी विकल्पों की तुलना में धीमी स्थापना हो। तर्क यह है कि जो गैर-देशी पौधा तेजी से स्थापित होता है, वह एक नई समस्या बन जाता है। हम इस प्राथमिकता को अपनी भविष्य की सभी सिफारिशों में शामिल कर रहे हैं।"}
+{"id": "mem_023", "text": "Brennan Lyle के साथ पुष्टि हुई: Crestmoor Systems टीम अपनी सभी आंतरिक सेवाओं के लिए OTLP HTTP का उपयोग करती है, कभी भी gRPC नहीं। उनका तर्क polyglot stack में version-skew समस्याएँ थीं। अनुकूलता कार्य के लिए यह जानना उपयोगी है।"}
+{"id": "mem_024", "text": "मुझे generated parsers की तुलना में Pratt-style operator precedence ऐसी languages के लिए ज़्यादा पसंद है जिनमें जटिल precedence rules हों। error messages काफी बेहतर होते हैं और code को debug करना ज़्यादा आसान होता है। यह ज़्यादातर एक व्यक्तिगत पसंद है लेकिन मैं इसका बचाव करूँगा।"}
+{"id": "mem_025", "text": "इस सप्ताह AI सहायता के बिना एक अध्याय लिखने का संकल्प ले रहा हूँ, एक कैलिब्रेशन जाँच के रूप में। परिणाम की तुलना AI-सहायित अध्यायों से करना चाहता हूँ और यह देखना चाहता हूँ कि क्या मैं सिस्टम पर बहुत अधिक निर्भर हो रहा हूँ। इस तुलना को Wendelsea Writing Group के साथ साझा करूँगा।"}
+{"id": "mem_026", "text": "हमने नए index के लिए skip list के बजाय B-tree चुना। Read-mostly workload, copy-on-write लेखन, बेहतर cache behavior। यह निर्णय आर्किटेक्चर विकी में दर्ज है।"}
+{"id": "mem_027", "text": "Ren Solanke के शोध प्रबंध का बचाव 6 मई को है। उन्होंने पूछा कि क्या मैं एक मसौदा अध्याय को पहले से पढ़ने के लिए तैयार हूँ। मैंने हाँ कहा — 22 अप्रैल तक एक बार प्रतिक्रिया देने के लिए प्रतिबद्ध हूँ, ताकि उनके पास बचाव से पहले संशोधन करने का समय हो।"}
+{"id": "mem_028", "text": "तथ्य: Wendelsea जिला चूना पत्थर की आधार चट्टान पर स्थित है। मिट्टी का pH प्राकृतिक रूप से तटस्थ या थोड़ा क्षारीय होने की ओर बढ़ता है। उस क्षेत्र में कोई भी ब्लूबेरी उत्पादक बिना हस्तक्षेप के chlorosis से जूझेगा।"}
+{"id": "mem_029", "text": "मुझे लगता है कि Aerwyn श्रृंखला की भाषा पर लिखा गया परिशिष्ट एक ऐसी विशेषता होगी जिसे या तो बहुत पसंद किया जाएगा या बिल्कुल नापसंद। कुछ पाठक गहन विश्व-निर्माण की कृतियों को पसंद करते हैं; दूसरे उन्हें आत्म-मुग्ध मानते हैं। मेरा मानना है कि यह अंतिम अंक में नमक-ऋण मोटिफ़ के प्रतिफल में सार्थक रूप से योगदान देता है।"}
+{"id": "mem_030", "text": "User ने secondary sale को एक तिमाही के लिए टालने का निर्णय लिया है। किसी भी नए investor diligence से पहले cap table साफ करना चाहता है। परियोजना योजना में संबंधित कार्यों को Q2 में स्थानांतरित कर रहा है।"}
+{"id": "mem_031", "text": "Pell Halloran नियमित समाधान संबंधी प्रश्नों के लिए फोन की तुलना में ईमेल को पसंद करती हैं — इससे उन्हें एक लिखित रिकॉर्ड मिलता है। आगे से सभी मानक AR प्रश्न ईमेल के माध्यम से भेजे जाएँगे। फोन केवल तत्काल एस्केलेशन के लिए आरक्षित है।"}
+{"id": "mem_032", "text": "इस साल Bridgewater Community Garden में नई मेसन मधुमक्खी कॉलोनियाँ 18 मार्च को उभरीं। यह Wendelsea Orchard में सेब के फूलों के खिलने की तारीख, जो 20 मार्च को शुरू हुई, से दो दिन पहले है। थोड़ी phenological बेमेल है, लेकिन सहनीय सीमा के भीतर है।"}
+{"id": "mem_033", "text": "Saela से वादा किया है कि 1 जून से शुरू होने वाले उसके अध्ययन अवकाश से पहले मैं उसके पूरे पांडुलिपि प्रारूप की समीक्षा कर लूँगा। 60 पन्नों के इस पेपर के लिए दो महीने। वक्त कम है, लेकिन अगर मैं हर बुधवार इसे दे दूँ तो यह काम बन सकता है।"}
+{"id": "mem_034", "text": "ईमानदारी से कहूँ तो, मुझे एजेंटिक कोडिंग पाइपलाइनों की वर्तमान प्रवृत्ति थकाऊ लगती है। वे कोड उससे भी तेज़ी से जनरेट करते हैं जितनी तेज़ी से कोई उसकी समीक्षा कर सकता है, और बग्स का पता लगाना भी कठिन हो जाता है। मैं उनका उपयोग करता रहूँगा, लेकिन मैं यही चाहूँगा कि हम धीमे चलें और समझ विकसित करें।"}
+{"id": "mem_035", "text": "invoice parsing पाइपलाइन के लिए Tesseract से PaddleOCR पर स्विच करने का फैसला किया गया है। लेआउट-जागरूक निष्कर्षण तालिकाओं को बहुत बेहतर तरीके से संभालता है। इस स्थानांतरण में लगभग एक सप्ताह लगेगा, और परिवर्तन के दौरान हम दोनों को समानांतर में चलाते रहेंगे।"}
+{"id": "mem_036", "text": "Ivora आशावादी पूर्वानुमानों के बजाय रूढ़िवादी नकदी पूर्वानुमानों को पसंद करती हैं, यहाँ तक कि सटीकता की कीमत पर भी। उनका तर्क: अपेक्षा से बेहतर आश्चर्य सुखद होते हैं, अपेक्षा से खराब आश्चर्य संकट होते हैं। इसके अनुरूप हमारे मॉडल की रिपोर्टिंग में समायोजन किया जा रहा है।"}
+{"id": "mem_037", "text": "Karis चाहती है कि मैं Bridgewater Schools की गार्डन कमेटी की मीटिंग में 10 अप्रैल को व्यक्तिगत रूप से शामिल हूँ, वीडियो कॉल के ज़रिए नहीं। उसे लगता है कि स्कूल के निर्णयकर्ता किसी शारीरिक रूप से मौजूद व्यक्ति के साथ ज़्यादा जुड़ेंगे। मैंने पुष्टि कर दी है कि मैं वहाँ रहूँगा।"}
+{"id": "mem_038", "text": "दो बातें जो नोट करने लायक हैं: सेब के छाजन रोग की फफूंद गिरी हुई पत्तियों में सर्दी बिताती है, इसलिए पतझड़ में पत्ती हटाना बेहद ज़रूरी है। और तांबे के छिड़काव केवल कली सूजन अवस्था में ही असरदार होते हैं — एक बार कलियाँ खुल जाएँ, तो वह मौका हाथ से निकल जाता है।"}
+{"id": "mem_039", "text": "मुझे लगता है कि उपयोगकर्ता का यह सहज विचार बिल्कुल सही है कि Saela के तीसरे अंक के निर्णय को पतन के बजाय तीव्र कार्रवाई का क्षण बनाया जाए। यह कथा-चाप का सम्मान करता है और घिसी-पिटी बात से बचता है। मेरी राय है, लेकिन मैं इस पर दृढ़ता से कायम हूँ।"}
+{"id": "mem_040", "text": "Solas ने Distributed Tracing fix को स्प्रिंट के अंत तक (शुक्रवार) जारी करने की प्रतिबद्धता जताई। यह बग दो हफ्तों से Aerwyn Telemetry कंसोर्टियम को रोक रहा है; उन्हें पैच के जारी होने की आवश्यकता है। Brennan गुरुवार को रिग्रेशन टेस्ट चलाएंगे।"}

benchmarks/model_eval/datasets/memory_extraction/dataset.it.jsonl

@@ -0,0 +1,40 @@
+{"id": "mem_001", "text": "Ho deciso che passeremo da cosine similarity a Jaccard per la pipeline topic-clustering. La natura discreta degli insiemi di token rende Jaccard più adatto, e i risultati cosine erano troppo rumorosi per i documenti brevi. Domani rieseguiremo i benchmark."}
+{"id": "mem_002", "text": "Saela preferisce fortemente scrivere bozze in LaTeX piuttosto che in Markdown. Ritiene il rendering delle equazioni più affidabile e la gestione della bibliografia essenziale per il lavoro accademico. D'ora in poi rispetterò questa sua scelta e smetterò di cercare di convertire i suoi file."}
+{"id": "mem_003", "text": "Confermato: la temperatura ambiente della nuova sala server è di 19°C. Rientra nelle specifiche delle apparecchiature, ma è al limite inferiore. La scheda tecnica del refrigeratore indica che l'ottimale è 21-23°C — stiamo raffreddando più del necessario."}
+{"id": "mem_004", "text": "Onestamente, penso che il campo abbia sopravvalutato il chain-of-thought prompting. Funziona sui benchmark matematici perché le tracce di ragionamento si trovano a corrispondere alla computazione sottostante, ma su ragionamenti più difficili le tracce sono razionalizzazioni a posteriori. È solo la mia interpretazione, però."}
+{"id": "mem_005", "text": "Ho promesso all'utente che la prima bozza del survey paper sarebbe stata pronta per venerdì mattina. Ho riservato mercoledì e giovedì per scrivere con concentrazione. Ha avuto pazienza e voglio mantenere la promessa."}
+{"id": "mem_006", "text": "Ne abbiamo parlato e siamo d'accordo: niente più fertilizzante NPK sintetico nell'orto. Passeremo completamente a emendamenti di compost e a una rotazione di colture di copertura invernali. Inizieremo la transizione questo autunno."}
+{"id": "mem_007", "text": "Ivora preferisce ricevere i rapporti settimanali sui flussi di cassa il lunedì mattina, non il venerdì pomeriggio. Vuole consultare le prospettive della settimana imminente con il caffè del mattino, non il riepilogo della settimana scorsa quando è stanca. Aggiornamento della pianificazione dell'invio automatico."}
+{"id": "mem_008", "text": "La scadenza per l'invio alla rivista del Tartine Lab è il 15 marzo. Scadenza rigida — non accetteranno invii in ritardo. L'articolo di Saela deve essere nel loro sistema entro quella data."}
+{"id": "mem_009", "text": "Mi impegno a migliorare l'accuratezza dell'OCR pipeline per le fatture scansionate manualmente entro la fine del mese. Attualmente siamo al 85%, l'obiettivo è il 97%. Se non riesco a raggiungerlo, torniamo alla revisione manuale per quel tipo di documento."}
+{"id": "mem_010", "text": "Il mio parere sul row polymorphism: è l'astrazione giusta per il nostro use case, ma il rumore sintattico che introduce nei messaggi di errore ci tornerà contro. Dovremo fare un lavoro significativo sui diagnostics prima di poterlo rilasciare agli utenti esterni."}
+{"id": "mem_011", "text": "Deciso il metodo di ammortamento: lineare per l'allestimento dell'ufficio, MACRS per le attrezzature. Opzione Section 179 solo per i beni di cui siamo certi che useremo a lungo termine. Ivora ha approvato."}
+{"id": "mem_012", "text": "Karis mi ha detto che preferirebbe ricevere aggiornamenti settimanali in elenco puntato piuttosto che in lunghi paragrafi. Li legge sul telefono tra una visita ai giardini e l'altra, e la prosa densa non si adatta al suo flusso di lavoro."}
+{"id": "mem_013", "text": "Confermato dai dati sulla fioritura dei meli: le date di prima fioritura di quest'anno sono anticipate di otto giorni rispetto alla media a lungo termine. I registri del Wendelsea Botanic Garden risalgono a 18 anni fa e questa è la terza primavera più precoce mai registrata."}
+{"id": "mem_014", "text": "Penso che la cosa del 'i Wends parlano più dolcemente dei Drukar' risulterà riduttiva sulla pagina. I popoli reali non suonano come i loro stereotipi linguistici, e leggere quegli stereotipi può appiattire i personaggi. Vale la pena opporsi all'utente in proposito."}
+{"id": "mem_015", "text": "Ho promesso a Doreth di inviarle via email il calendario di potatura dei meli antichi entro fine settimana. Sta pianificando i lavori invernali e ha bisogno delle date per coordinarsi con la sua squadra di lavoro."}
+{"id": "mem_016", "text": "Optato per permutation tests anziché Mann-Whitney U per i confronti di latenza. Il minor numero di ipotesi distributive giustifica il leggero aumento del tempo di calcolo. Rieseguendo tutti i confronti precedenti con il nuovo metodo."}
+{"id": "mem_017", "text": "Pol Krisat preferisce revisionare il codice tramite commenti in thread sulla PR piuttosto che via email o messaggi di chat separati. Sostiene che così il contesto resta allegato alla modifica. Adotto la stessa convenzione anche io."}
+{"id": "mem_018", "text": "Nota rapida per l'archivio: il periodo di due diligence di Aerwyn Capital è di 30 giorni dalla firma del term sheet. Abbiamo tempo fino al 14 aprile per sistemare la cap table e preparare i bilanci revisionati."}
+{"id": "mem_019", "text": "Onestamente, sono scettico sul vision-language model dell'Hollowmounts Institute. La loro metodologia di valutazione ha un selection bias — riportano solo i prompts in cui il loro modello vince. Il lavoro tecnico è solido ma l'articolo esagera."}
+{"id": "mem_020", "text": "Invierò la previsione revisionata a Ivora entro la fine della giornata di giovedì. In questo modo avrà il fine settimana per rivederla prima della riunione del consiglio di amministrazione di lunedì. Lo metto sulla mia lista."}
+{"id": "mem_021", "text": "Saela ha deciso di concentrare la rassegna esclusivamente su obiettivi contrastivi — senza includere approcci basati su raggruppamento o distillazione. L'ambito è più ristretto ma il contributo è più chiaro. Ristrutturerò la scaletta di conseguenza."}
+{"id": "mem_022", "text": "Bramble preferisce fortemente le piantumazioni esclusivamente autoctone per i progetti di ripristino, anche quando ciò comporta un attecchimento più lento rispetto alle alternative non autoctone. Il ragionamento: una specie non autoctona che attecchisce rapidamente diventa il nuovo problema. Integriamo questa preferenza in tutte le nostre future raccomandazioni."}
+{"id": "mem_023", "text": "Confermato con Brennan Lyle: il team di Crestmoor Systems utilizza OTLP HTTP per tutti i propri servizi interni, mai gRPC. Il loro ragionamento era problemi di disallineamento delle versioni nel loro stack poliglotta. Utile da sapere per il lavoro di compatibilità."}
+{"id": "mem_024", "text": "Preferisco la precedenza degli operatori in stile Pratt ai parser generati per i linguaggi con regole di precedenza complesse. I messaggi di errore sono decisamente migliori e il codice è più facile da debuggare. È principalmente una questione di gusto personale, ma la difenderò."}
+{"id": "mem_025", "text": "Mi impegno a scrivere un capitolo questa settimana senza assistenza AI, come verifica di calibrazione. Voglio confrontare il risultato con i capitoli con assistenza AI e vedere se mi sto appoggiando troppo al sistema. Condividerò il confronto con Wendelsea Writing Group."}
+{"id": "mem_026", "text": "Abbiamo scelto il B-tree anziché la skip list per il nuovo indice. Carico di lavoro prevalentemente in lettura, scritture copy-on-write, migliore comportamento della cache. La decisione è documentata nella wiki di architettura."}
+{"id": "mem_027", "text": "La difesa della tesi di Ren Solanke è il 6 maggio. Mi hanno chiesto se ero disponibile a leggere in anteprima una bozza di capitolo. Ho detto di sì — con l'impegno di fornire un giro di feedback entro il 22 aprile, così hanno tempo di revisionare prima della difesa."}
+{"id": "mem_028", "text": "Fatto sta che il distretto di Wendelsea poggia su una roccia madre calcarea. Il pH del suolo tende naturalmente verso valori neutri o leggermente alcalini. Qualsiasi coltivatore di mirtilli in quella zona dovrà affrontare la clorosi in assenza di interventi."}
+{"id": "mem_029", "text": "Penso che l'appendice sul linguaggio del ciclo di Aerwyn sarà un elemento che o si ama o si odia. Alcuni lettori amano gli artefatti di un'approfondita costruzione del mondo; altri li trovano autoindulgenti. A mio parere, contribuisce in modo significativo alla soddisfazione del motivo del debito di sale nell'atto finale."}
+{"id": "mem_030", "text": "L'utente ha deciso di posticipare la vendita secondaria di un trimestre. Vuole ripulire la cap table prima di qualsiasi due diligence da parte di nuovi investitori. Sposta le attività pertinenti al secondo trimestre nel piano di progetto."}
+{"id": "mem_031", "text": "Pell Halloran preferisce l'email al telefono per le domande di riconciliazione di routine — fornisce una traccia scritta. D'ora in poi tutte le richieste AR standard verranno indirizzate via email. Telefono riservato solo per le escalation urgenti."}
+{"id": "mem_032", "text": "Le nuove colonie di api murarie a Bridgewater Community Garden sono emerse il 18 marzo di quest'anno. È due giorni prima della fioritura dei meli a Wendelsea Orchard, che è iniziata il 20 marzo. Leggero disallineamento fenologico, ma entro la tolleranza."}
+{"id": "mem_033", "text": "Ho promesso a Saela di revisionare la bozza completa del suo manoscritto prima dell'inizio del suo anno sabbatico, il 1° giugno. Due mesi per un articolo di 60 pagine. Tempi stretti, ma fattibili se dedico i mercoledì a questo."}
+{"id": "mem_034", "text": "Onestamente, trovo faticosa l'attuale tendenza verso le pipeline di programmazione agentica. Generano codice più velocemente di quanto chiunque possa revisionarlo, e i bug diventano più difficili da tracciare. Continuerò a usarle, ma preferirei che rallentassimo e costruissimo una comprensione più solida."}
+{"id": "mem_035", "text": "Deciso di passare da Tesseract a PaddleOCR per la pipeline di invoice parsing. L'estrazione basata sul layout gestisce le tabelle molto meglio. La migrazione richiederà circa una settimana, e manterremo entrambi in esecuzione in parallelo durante la transizione."}
+{"id": "mem_036", "text": "Ivora preferisce previsioni di cassa conservative a quelle ottimistiche, anche a scapito dell'accuratezza. Il suo ragionamento: le sorprese al rialzo sono piacevoli, le sorprese al ribasso sono crisi. Stiamo adeguando di conseguenza la reportistica del nostro modello."}
+{"id": "mem_037", "text": "Karis vuole che io partecipi di persona alla riunione del comitato del giardino delle scuole di Bridgewater il 10 aprile, non in videochiamata. Pensa che i decisori delle scuole interagiranno maggiormente con qualcuno presente fisicamente. Ho confermato che ci sarò."}
+{"id": "mem_038", "text": "Due fatti da tenere a mente: il fungo della ticchiolatura del melo sverna nelle foglie cadute, quindi la rimozione delle foglie è fondamentale in autunno. E i trattamenti con rame sono efficaci solo in fase di gonfiore delle gemme — una volta che si sono aperte, è troppo tardi."}
+{"id": "mem_039", "text": "Penso che l'istinto dell'utente di trasformare la decisione di Saela nel terzo atto in un momento di azione feroce anziché di crollo sia esattamente quello giusto. Onora l'arco narrativo ed evita il cliché. È solo la mia opinione, ma ci tengo molto."}
+{"id": "mem_040", "text": "Solas si è impegnato a rilasciare la correzione per Distributed Tracing entro la fine dello sprint (venerdì). Il bug sta bloccando il consorzio di telemetria di Aerwyn da due settimane; hanno bisogno della patch per procedere al rilascio. Brennan eseguirà i test di regressione giovedì."}

benchmarks/model_eval/datasets/memory_extraction/dataset.jsonl

@@ -0,0 +1,40 @@
+{"id": "mem_001", "text": "I've decided we're going to switch from cosine similarity to Jaccard for the topic-clustering pipeline. The discrete nature of the token sets makes Jaccard a better fit, and the cosine results were too noisy for short documents. We'll re-run the benchmarks tomorrow."}
+{"id": "mem_002", "text": "Saela strongly prefers writing drafts in LaTeX rather than Markdown. She finds the equation rendering more reliable and the bibliography management essential for academic work. I'll respect that going forward and stop trying to convert her files."}
+{"id": "mem_003", "text": "Just confirmed: the new server room is at 19°C ambient. That's within spec for the equipment but on the cool end. The chiller spec sheet says optimal is 21-23°C — we're cooling more than we need to."}
+{"id": "mem_004", "text": "Honestly, I think the field has overrated chain-of-thought prompting. It works on math benchmarks because the reasoning traces happen to match the underlying computation, but on harder reasoning the traces are post-hoc rationalizations. That's just my read though."}
+{"id": "mem_005", "text": "Promised the user I'd have the first draft of the survey paper ready by Friday morning. I've blocked Wednesday and Thursday for focused writing. They've been patient and I want to deliver."}
+{"id": "mem_006", "text": "We talked about it and agreed: no more synthetic NPK fertilizer in the kitchen garden. Switching entirely to compost amendments and a winter cover crop rotation. Will start the transition this fall."}
+{"id": "mem_007", "text": "Ivora prefers receiving weekly cashflow reports on Monday mornings, not Friday afternoons. She wants to see the upcoming week's outlook with her morning coffee, not last week's recap when she's tired. Updating the auto-send schedule."}
+{"id": "mem_008", "text": "The Tartine Lab journal's submission deadline is March 15. Hard deadline — they won't accept late submissions. Saela's paper needs to be in their system by then."}
+{"id": "mem_009", "text": "I'm committing to fixing the OCR pipeline's accuracy on the hand-scanned invoices before month-end. Currently at 85%, target is 97%. If I can't hit it, we revert to manual review for that document type."}
+{"id": "mem_010", "text": "My take on row polymorphism: it's the right abstraction for our use case but the syntax noise it introduces in error messages is going to bite us. We'll need significant work on diagnostics before we can ship to external users."}
+{"id": "mem_011", "text": "Decision made on the depreciation method: straight-line for the office build-out, MACRS for the equipment. Section 179 election only on the items we're sure we'll use long-term. Ivora signed off."}
+{"id": "mem_012", "text": "Karis told me she'd rather I send weekly updates as bullet points than long paragraphs. She's reading them on her phone between garden visits and dense prose doesn't work for her workflow."}
+{"id": "mem_013", "text": "Confirmed via the apple bloom data: this year's first-bloom dates are eight days earlier than the long-term mean. The Wendelsea Botanic Garden's records go back 18 years and this is the third-earliest spring on record."}
+{"id": "mem_014", "text": "I think the 'Wends speak softer than Drukar' thing is going to feel reductive on the page. Real-world peoples don't sound like their linguistic stereotypes, and reading those stereotypes can flatten the characters. Worth pushing back on the user about it."}
+{"id": "mem_015", "text": "Promised Doreth I'd email her the pruning schedule for the heritage apples by end of week. She's planning her winter work and needs the dates to coordinate with her labor crew."}
+{"id": "mem_016", "text": "Decided on permutation tests over Mann-Whitney U for the latency comparisons. The fewer distributional assumptions justify the slight increase in computation time. Re-running all the previous comparisons under the new method."}
+{"id": "mem_017", "text": "Pol Krisat prefers code reviews via threaded comments on the PR rather than separate emails or chat messages. Says it keeps the context attached to the change. Adopting the same convention myself."}
+{"id": "mem_018", "text": "Quick fact for the file: Aerwyn Capital's diligence period is 30 days from term sheet signing. We have until April 14 to get the cap table cleaned up and the audited financials packaged."}
+{"id": "mem_019", "text": "Honestly, I'm skeptical of the Hollowmounts Institute's vision-language model. Their evaluation methodology has selection bias — they only report on the prompts where their model wins. The technical work is solid but the paper oversells."}
+{"id": "mem_020", "text": "I'll send the revised forecast to Ivora by Thursday end of day. That gives her the weekend to review before the Monday board meeting. Putting it on my list."}
+{"id": "mem_021", "text": "Saela decided to focus the survey on contrastive objectives only — no inclusion of clustering-based or distillation-based approaches. The scope is tighter but the contribution is clearer. I'll restructure the outline accordingly."}
+{"id": "mem_022", "text": "Bramble strongly prefers native-only plantings for restoration projects, even when that means slower establishment than non-native alternatives. The argument: a non-native that establishes fast becomes the new problem. Building this preference into all our future recommendations."}
+{"id": "mem_023", "text": "Confirmed with Brennan Lyle: the Crestmoor Systems team uses OTLP HTTP for all their internal services, never gRPC. Their reasoning was version-skew issues across the polyglot stack. Useful to know for compatibility work."}
+{"id": "mem_024", "text": "I prefer Pratt-style operator precedence over generated parsers for languages with complex precedence rules. The error messages are dramatically better and the code is easier to debug. This is mostly a personal aesthetic but I'll defend it."}
+{"id": "mem_025", "text": "Committing to writing one chapter this week without AI assistance, as a calibration check. Want to compare the result to AI-assisted chapters and see if I'm leaning on the system too heavily. Will share the comparison with the Wendelsea Writing Group."}
+{"id": "mem_026", "text": "We chose B-tree over skip list for the new index. Read-mostly workload, copy-on-write writes, better cache behavior. The decision is documented in the architecture wiki."}
+{"id": "mem_027", "text": "Ren Solanke's dissertation defense is May 6. They asked if I'd be willing to pre-read a draft chapter. Said yes — committed to one round of feedback by April 22 so they have time to revise before the defense."}
+{"id": "mem_028", "text": "Fact: the Wendelsea district sits on limestone bedrock. Soil pH naturally drifts toward neutral or slightly alkaline. Any blueberry grower in that area is going to fight chlorosis without intervention."}
+{"id": "mem_029", "text": "I think the Aerwyn cycle's appendix on language is going to be a love-it-or-hate-it feature. Some readers love deep worldbuilding artifacts; others find them indulgent. My read is that it adds meaningfully to the salt-debt motif's payoff in the final act."}
+{"id": "mem_030", "text": "User decided to delay the secondary sale by a quarter. Wants to clean up the cap table before any new investor diligence. Pushing the relevant work items to Q2 in the project plan."}
+{"id": "mem_031", "text": "Pell Halloran prefers email to phone for routine reconciliation questions — it gives her a written trail. Will route all standard AR queries through email going forward. Phone reserved for urgent escalations only."}
+{"id": "mem_032", "text": "The new mason bee colonies at Bridgewater Community Garden emerged on March 18 this year. That's two days earlier than the apple bloom at Wendelsea Orchard, which started March 20. Slight phenological mismatch but within tolerance."}
+{"id": "mem_033", "text": "Promised Saela I'd review her full manuscript draft before her sabbatical begins on June 1. Two months for a 60-page paper. Tight but workable if I dedicate Wednesdays to it."}
+{"id": "mem_034", "text": "Honestly, I find the current trend toward agentic coding pipelines exhausting. They generate code faster than anyone can review it, and the bugs get harder to trace. I'll keep using them, but I'd rather we slow down and build understanding."}
+{"id": "mem_035", "text": "Decided to switch from Tesseract to PaddleOCR for the invoice parsing pipeline. The layout-aware extraction handles tables much better. Migration will take about a week, and we'll keep both running in parallel during the transition."}
+{"id": "mem_036", "text": "Ivora prefers conservative cash forecasts over optimistic ones, even at the cost of accuracy. Her reasoning: surprises on the upside are pleasant, surprises on the downside are crises. Adjusting our model's reporting accordingly."}
+{"id": "mem_037", "text": "Karis wants me to attend the Bridgewater Schools garden committee meeting in person on April 10, not via video call. She thinks the schools' decision-makers will engage more with someone physically present. Confirmed I'll be there."}
+{"id": "mem_038", "text": "Two facts worth filing: the apple scab fungus overwinters in fallen leaves, so leaf removal is critical in fall. And copper sprays are only effective at bud-swell — once buds open, you've missed the window."}
+{"id": "mem_039", "text": "I think the user's instinct to make Saela's third-act decision a moment of fierce action rather than collapse is exactly right. It honors the arc and avoids the cliché. My opinion, but I feel strongly about it."}
+{"id": "mem_040", "text": "Solas committed to shipping the Distributed Tracing fix by end of sprint (Friday). The bug has been blocking the Aerwyn Telemetry consortium for two weeks; they need the patch to ship. Brennan will run the regression tests on Thursday."}

benchmarks/model_eval/datasets/memory_extraction/dataset.ko.jsonl

@@ -0,0 +1,40 @@
+{"id": "mem_001", "text": "Topic Clustering 파이프라인에서 cosine similarity를 Jaccard로 전환하기로 결정했습니다. 토큰 집합의 이산적인 특성 때문에 Jaccard가 더 적합하고, 짧은 문서에 대한 cosine 결과는 너무 noisy했습니다. 내일 벤치마크를 다시 실행할 예정입니다."}
+{"id": "mem_002", "text": "Saela는 초안을 Markdown보다 LaTeX로 작성하는 것을 훨씬 선호한다. 그녀는 수식 렌더링이 더 안정적이며 참고문헌 관리가 학술 작업에 필수적이라고 생각한다. 앞으로 그 점을 존중하며 그녀의 파일을 변환하려는 시도를 멈출 것이다."}
+{"id": "mem_003", "text": "방금 확인했습니다: 새 서버실의 실내 온도는 19°C입니다. 이는 장비 사양 범위 내이지만 낮은 편입니다. 냉각기 사양서에는 최적 온도가 21-23°C라고 나와 있어, 현재 필요 이상으로 냉각하고 있는 셈입니다."}
+{"id": "mem_004", "text": "솔직히, 나는 이 분야가 chain-of-thought prompting를 과대평가했다고 생각한다. 수학 벤치마크에서는 작동하는데, 그 이유는 추론 과정이 기저 연산과 우연히 일치하기 때문이지만, 더 어려운 추론에서는 그 과정들이 사후적 합리화에 불과하다. 하지만 이건 그냥 내 개인적인 해석이다."}
+{"id": "mem_005", "text": "사용자에게 금요일 아침까지 서베이 논문 초안을 준비하겠다고 약속했다. 수요일과 목요일은 집중적으로 글을 쓰기 위해 일정을 비워뒀다. 그동안 인내심 있게 기다려 주었기에 꼭 결과물을 내고 싶다."}
+{"id": "mem_006", "text": "우리는 이야기를 나누고 합의했다: 텃밭에서는 더 이상 합성 NPK 비료를 쓰지 않기로. 퇴비 개량과 겨울 덮개 작물 로테이션으로 완전히 전환하며, 올가을부터 시작할 것이다."}
+{"id": "mem_007", "text": "Ivora는 금요일 오후가 아닌 월요일 아침에 주간 현금 흐름 보고서를 받는 것을 선호합니다. 피곤할 때 지난주 요약을 보는 것이 아니라, 아침 커피와 함께 다가오는 주의 전망을 보고 싶어합니다. 자동 전송 일정을 업데이트하고 있습니다."}
+{"id": "mem_008", "text": "Tartine Lab 저널의 제출 마감일은 3월 15일입니다. 엄격한 마감일로, 늦은 제출은 받지 않습니다. Saela의 논문은 그때까지 시스템에 제출되어 있어야 합니다."}
+{"id": "mem_009", "text": "월말까지 수동 스캔 인보이스의 OCR 파이프라인 정확도를 개선하겠습니다. 현재 85%, 목표는 97%입니다. 달성하지 못하면 해당 문서 유형은 수동 검토로 되돌립니다."}
+{"id": "mem_010", "text": "내가 row polymorphism에 대해 생각하는 바는 이렇다. 이는 우리의 사용 사례에 적합한 추상화이지만, 오류 메시지에 도입되는 구문 노이즈가 나중에 우리를 괴롭힐 것이다. 외부 사용자에게 출시하기 전에 진단에 상당한 작업이 필요할 것이다."}
+{"id": "mem_011", "text": "감가상각 방법 결정: 사무실 인테리어는 정액법, 장비는 MACRS. 장기간 사용할 것이 확실한 항목에만 Section 179 선택 적용. Ivora가 승인했습니다."}
+{"id": "mem_012", "text": "Karis는 주간 업데이트를 긴 문단보다는 글머리 기호로 보내는 편을 더 좋아한다고 했다. 그녀는 정원 방문 중간중간 휴대폰으로 이를 읽고 있는데, 빽빽한 글은 그녀의 워크플로우에 맞지 않는다."}
+{"id": "mem_013", "text": "사과 개화 데이터를 통해 확인한 결과, 올해 첫 개화일은 장기 평균보다 8일 빠르다. Wendelsea 식물원의 기록은 18년 전부터 시작되었으며, 이번 봄은 기록상 세 번째로 이른 봄이다."}
+{"id": "mem_014", "text": "내 생각에 'Wends speak softer than Drukar'라는 설정은 글에서 지나치게 단순화된 느낌을 줄 것 같아. 실제 세계의 민족들은 언어적 스테레오타입처럼 말하지 않고, 그런 고정관념을 읽으면 캐릭터가 평면화될 수 있거든. 사용자에게 이 점을 이의를 제기할 만해."}
+{"id": "mem_015", "text": "Doreth에게 이번 주말까지 전통 사과 가지치기 일정을 이메일로 보내주기로 했다. 그녀는 겨울 작업을 계획 중인데, 작업팀과 일정을 맞추려면 날짜가 필요하다."}
+{"id": "mem_016", "text": "지연 시간 비교를 위해 Mann-Whitney U 대신 순열 검정을 선택했다. 분포 가정이 더 적다는 점이 계산 시간의 소폭적인 증가를 정당화한다. 새로운 방법으로 이전의 모든 비교를 재실행하고 있다."}
+{"id": "mem_017", "text": "Pol Krisat는 코드 리뷰를 별도의 이메일이나 채팅 메시지가 아닌 PR의 스레드 댓글로 받는 것을 선호한다. 변경 사항에 맥락이 계속 붙어 있어서 그렇다고 한다. 나도 같은 방식을 따르기로 했다."}
+{"id": "mem_018", "text": "파일용 참고사항: Aerwyn Capital의 실사 기간은 term sheet 서명일로부터 30일입니다. 4월 14일까지 cap table을 정리하고 감사 완료된 재무제표를 준비해야 합니다."}
+{"id": "mem_019", "text": "솔직히, 나는 Hollowmounts Institute의 vision-language model에 대해 회의적이다. 그들의 평가 방법론에는 selection bias가 있다 — 그들은 자신들의 모델이 이기는 prompts에 대해서만 보고한다. 기술적인 작업은 탄탄하지만 논문은 과대포장되어 있다."}
+{"id": "mem_020", "text": "수정된 예측 자료를 목요일 업무 종료 전까지 Ivora에게 보내겠습니다. 그러면 월요일 이사회 전에 주말 동안 검토할 시간이 생깁니다. 내 할 일 목록에 적어둡니다."}
+{"id": "mem_021", "text": "Saela는 설문을 대조 목표에만 한정하기로 했고, 클러스터링 기반이나 증류 기반 접근법은 제외하기로 했다. 범위는 더 좁아지지만 기여도는 더 명확해진다. 이에 맞춰 개요를 재구성하겠다."}
+{"id": "mem_022", "text": "Bramble은 복원 프로젝트에서 비토종 대안에 비해 정착 속도가 느리더라도 순수 토종 식재를 강력히 선호한다. 논거는 이렇다: 빠르게 정착하는 비토종은 새로운 문제가 된다. 이 선호를 향후 모든 권고에 반영할 것이다."}
+{"id": "mem_023", "text": "Brennan Lyle와 확인한 결과: Crestmoor Systems 팀은 모든 내부 서비스에 OTLP HTTP를 사용하며 gRPC는 전혀 사용하지 않는다. 그 이유는 폴리글롯 스택 전반의 버전 스큐 문제 때문이다. 호환성 작업에 유용한 정보다."}
+{"id": "mem_024", "text": "복잡한 우선순위 규칙을 가진 언어에서는 생성된 파서보다 Pratt-style operator precedence를 선호합니다. 오류 메시지가 훨씬 더 나으며 코드를 디버그하기도 더 쉽습니다. 이것은 대부분 개인적인 미학이지만, 저는 이를 옹호하겠습니다."}
+{"id": "mem_025", "text": "이번 주에 AI 도움 없이 한 챕터를 쓰기로 했다. 보정 점검 차원에서다. 결과를 AI 보조를 받은 챕터들과 비교해 보고, 내가 시스템에 너무 의존하고 있는지 확인하고 싶다. 비교 결과는 Wendelsea Writing Group에 공유할 예정이다."}
+{"id": "mem_026", "text": "새 인덱스에 B-tree를 skip list 대신 선택했습니다. 읽기 위주의 워크로드, copy-on-write 쓰기, 더 나은 캐시 동작. 해당 결정은 아키텍처 위키에 문서화되어 있습니다."}
+{"id": "mem_027", "text": "Ren Solanke의 학위 논문 발표가 5월 6일이다. 초안 챕터를 미리 읽어줄 수 있냐고 물어봤다. 응했다 — 4월 22일까지 한 차례 피드백을 제공하기로 했고, 그래야 발표 전에 수정할 시간이 있다."}
+{"id": "mem_028", "text": "사실: Wendelsea 지구는 석회암 기반암 위에 자리한다. Soil pH는 자연스럽게 중성이나 약알칼리성으로 기울어간다. 그 지역의 블루베리 재배자라면 개입 없이 chlorosis와 싸우게 될 것이다."}
+{"id": "mem_029", "text": "Aerwyn 시리즈의 언어 부록은 호불호가 갈리는 요소가 될 것 같아. 일부 독자들은 깊이 있는 세계관 구축의 흔적을 좋아하는 반면, 다른 이들은 그것이 사치스럽다고 여기거든. 내 생각에는 그것이 마지막 장에서 소금의 빚 모티프가 회수되는 부분에 의미 있게 기여한다고 봐."}
+{"id": "mem_030", "text": "User는 2차 매각을 한 분기 미루기로 결정했습니다. 새로운 투자자 실사가 진행되기 전에 캡 테이블을 정리하고자 하며, 프로젝트 계획에서 관련 작업 항목들을 2분기로 이동시키고 있습니다."}
+{"id": "mem_031", "text": "Pell Halloran은 일상적인 조정 관련 질문에 대해 전화보다 이메일을 선호합니다. 서면 기록을 남길 수 있기 때문입니다. 향후 모든 표준 AR 문의는 이메일로 처리할 예정이며, 전화는 긴급 에스컬레이션에만 사용합니다."}
+{"id": "mem_032", "text": "올해 3월 18일, Bridgewater Community Garden의 새로운 벽돌벌 군집이 출현했다. 이는 3월 20일에 시작된 Wendelsea Orchard의 사과 개화보다 이틀 빠르다. 약간의 생태시계적 불일치지만 허용 범위 내이다."}
+{"id": "mem_033", "text": "Saela에게 6월 1일에 시작하는 안식년 전에 그녀의 전체 원고 초안을 검토하겠다고 약속했다. 60페이지짜리 논문을 두 달 안에 하는 건 빡빡하지만, 수요일을 할애하면 할 만하다."}
+{"id": "mem_034", "text": "솔직히 말해서, 난 지금 에이전틱 코딩 파이프라인으로의 추세가 정말 지친다. 이것들은 누구도 검토할 수 없을 만큼 빠르게 코드를 생성하고, 버그는 추적하기 점점 더 어려워진다. 계속 사용하긴 하겠지만, 차라리 우리가 속도를 늦추고 이해를 쌓아갔으면 한다."}
+{"id": "mem_035", "text": "Invoice Parsing 파이프라인에서 Tesseract를 PaddleOCR로 전환하기로 결정했습니다. 레이아웃 인식 추출 기능이 표를 훨씬 더 잘 처리합니다. 마이그레이션은 약 일주일이 소요될 예정이며, 전환 기간 동안 두 시스템을 병렬로 운영할 계획입니다."}
+{"id": "mem_036", "text": "Ivora는 정확성을 희생하더라도 낙관적인 현금 예측보다 보수적인 예측을 선호한다. 그녀의 논리는 다음과 같다. 예상보다 좋은 결과는 반갑지만, 예상보다 나쁜 결과는 위기다. 이에 따라 우리 모델의 보고 방식을 조정하고 있다."}
+{"id": "mem_037", "text": "Karis는 내가 4월 10일 Bridgewater Schools 정원 위원회 회의에 화상 통화가 아닌 직접 참석하기를 원한다. 그녀는 학교 의사결정권자들이 직접 참석한 사람과 더 적극적으로 교류할 것이라고 생각한다. 내가 거기 갈 것을 확인했다."}
+{"id": "mem_038", "text": "기록해 둘 만한 두 가지 사실이다: 사과 붉은 곰팡이병 균은 낙엽 속에서 월동하므로, 가을 낙엽 제거가 매우 중요하다. 그리고 구리 살균제 스프레이는 봉오리가 부풀어 오를 때만 효과적이다 — 봉오리가 피면 그 시기를 놓친 것이다."}
+{"id": "mem_039", "text": "사용자가 Saela의 제3막 결정을 무너짐이 아닌 격렬한 행동의 순간으로 만들려는 직감이 정확히 옳다고 생각합니다. 이는 캐릭터 아크를 존중하고 클리셰를 피하는 것입니다. 제 의견이지만, 저는 이에 대해 강한 확신을 갖고 있습니다."}
+{"id": "mem_040", "text": "Solas는 이번 스프린트 종료일(금요일)까지 Distributed Tracing 수정 사항을 배포하기로 약속했다. 이 버그는 2주 동안 Aerwyn Telemetry 컨소시엄을 차단해 왔으며, 패치가 배포되어야 한다. Brennan은 목요일에 회귀 테스트를 실행할 예정이다."}

benchmarks/model_eval/datasets/memory_extraction/dataset.pt-BR.jsonl

@@ -0,0 +1,40 @@
+{"id": "mem_001", "text": "Decidi que mudaremos da similaridade de cosseno para Jaccard para o pipeline de agrupamento de tópicos. A natureza discreta dos conjuntos de tokens torna o Jaccard um ajuste melhor, e os resultados do cosseno eram muito barulhentos para documentos curtos. Executaremos novamente os benchmarks amanhã."}
+{"id": "mem_002", "text": "Saela prefere fortemente escrever rascunhos em LaTeX em vez de Markdown. Ela considera a equação mais confiável e o gerenciamento da bibliografia essencial para o trabalho acadêmico. Respeitarei isso daqui para frente e pararei de tentar converter os arquivos dela."}
+{"id": "mem_003", "text": "Acabei de confirmar: a nova sala de servidores está com temperatura ambiente de 19°C. Isso está dentro das especificações do equipamento, mas é legal. A folha de especificações do chiller diz que o ideal é 21-23°C – estamos resfriando mais do que o necessário."}
+{"id": "mem_004", "text": "Honestamente, acho que a área superestimou a orientação da cadeia de pensamento. Funciona em benchmarks matemáticos porque os traços de raciocínio correspondem à computação subjacente, mas em raciocínios mais difíceis os traços são racionalizações post-hoc. Essa é apenas a minha leitura."}
+{"id": "mem_005", "text": "Prometi ao usuário que teria o primeiro rascunho do documento de pesquisa pronto na manhã de sexta-feira. Bloqueei quarta e quinta para escrita focada. Eles foram pacientes e eu quero entregar."}
+{"id": "mem_006", "text": "Conversamos sobre isso e concordamos: chega de fertilizante NPK sintético na horta. Mudando totalmente para adubos e rotação de culturas de cobertura de inverno. Iniciará a transição neste outono."}
+{"id": "mem_007", "text": "Ivora prefere receber relatórios semanais de fluxo de caixa nas manhãs de segunda-feira, e não nas tardes de sexta-feira. Ela quer ver as perspectivas da próxima semana com seu café da manhã, e não a recapitulação da semana passada quando estiver cansada. Atualizando a programação de envio automático."}
+{"id": "mem_008", "text": "O prazo de submissão da revista Tartine Lab é 15 de março. Prazo rígido – eles não aceitarão submissões tardias. O documento do Saela precisa estar no sistema até então."}
+{"id": "mem_009", "text": "Comprometo-me a corrigir a precisão do pipeline de OCR nas faturas digitalizadas manualmente antes do final do mês. Atualmente em 85%, a meta é 97%. Se não conseguir, voltamos à revisão manual para esse tipo de documento."}
+{"id": "mem_010", "text": "Minha opinião sobre o polimorfismo de linha: é a abstração correta para nosso caso de uso, mas o ruído de sintaxe que ele introduz nas mensagens de erro vai nos incomodar. Precisaremos de um trabalho significativo de diagnóstico antes de podermos enviar para usuários externos."}
+{"id": "mem_011", "text": "Decisão tomada sobre o método de depreciação: linear para a construção de escritórios, MACRS para os equipamentos. Eleição da Seção 179 apenas nos itens que temos certeza de que usaremos no longo prazo. Ivora desconectado."}
+{"id": "mem_012", "text": "Karis me disse que preferia que eu enviasse atualizações semanais como marcadores do que parágrafos longos. Ela os lê em seu telefone entre as visitas ao jardim e a prosa densa não funciona para seu fluxo de trabalho."}
+{"id": "mem_013", "text": "Confirmado pelos dados de floração da macieira: as datas da primeira floração deste ano são oito dias antes da média de longo prazo. Os registros do Wendelsea Botanic Garden remontam a 18 anos e esta é a terceira primavera mais antiga já registrada."}
+{"id": "mem_014", "text": "Acho que a coisa 'Wends fala mais baixo que Drukar' vai parecer redutora na página. As pessoas do mundo real não se parecem com seus estereótipos linguísticos, e a leitura desses estereótipos pode achatar os personagens. Vale a pena criticar o usuário sobre isso."}
+{"id": "mem_015", "text": "Prometi a Doreth que enviaria a ela por e-mail o cronograma de poda das maçãs tradicionais até o final da semana. Ela está planejando seu trabalho de inverno e precisa coordenar as datas com sua equipe de trabalho."}
+{"id": "mem_016", "text": "Decidiu por testes de permutação em vez de Mann-Whitney U para comparações de latência. O menor número de suposições distributivas justifica o ligeiro aumento no tempo de cálculo. Executando novamente todas as comparações anteriores sob o novo método."}
+{"id": "mem_017", "text": "Pol Krisat prefere revisões de código por meio de comentários encadeados no PR, em vez de e-mails separados ou mensagens de bate-papo. Diz que mantém o contexto vinculado à mudança. Adotando a mesma convenção."}
+{"id": "mem_018", "text": "Fato rápido para o arquivo: o período de diligência do Aerwyn Capital é de 30 dias a partir da assinatura do termo de compromisso. Temos até 14 de abril para limpar a tabela de limites e empacotar as finanças auditadas."}
+{"id": "mem_019", "text": "Honestamente, sou cético em relação ao modelo de linguagem de visão do Hollowmounts Institute. Sua metodologia de avaliação tem viés de seleção – eles relatam apenas as solicitações em que seu modelo vence. O trabalho técnico é sólido, mas o papel vende demais."}
+{"id": "mem_020", "text": "Enviarei a previsão revisada para Ivora até o final do dia de quinta-feira. Isso lhe dá o fim de semana para revisar antes da reunião do conselho na segunda-feira. Colocando na minha lista."}
+{"id": "mem_021", "text": "Saela decidiu concentrar a pesquisa apenas em objetivos contrastantes – sem inclusão de abordagens baseadas em cluster ou destilação. O escopo é mais restrito, mas a contribuição é mais clara. Vou reestruturar o esboço de acordo."}
+{"id": "mem_022", "text": "Bramble prefere fortemente plantações exclusivamente nativas para projetos de restauração, mesmo quando isso significa um estabelecimento mais lento do que alternativas não nativas. O argumento: um não-nativo que se estabelece rapidamente se torna o novo problema. Incorporar esta preferência em todas as nossas recomendações futuras."}
+{"id": "mem_023", "text": "Confirmado com Brennan Lyle: a equipe Crestmoor Systems usa HTTP OTLP para todos os seus serviços internos, nunca gRPC. O raciocínio deles era problemas de distorção de versão na pilha poliglota. Útil saber para trabalho de compatibilidade."}
+{"id": "mem_024", "text": "Prefiro a precedência do operador no estilo Pratt em vez de analisadores gerados para linguagens com regras de precedência complexas. As mensagens de erro são dramaticamente melhores e o código é mais fácil de depurar. Esta é principalmente uma estética pessoal, mas vou defendê-la."}
+{"id": "mem_025", "text": "Comprometendo-se a escrever um capítulo esta semana sem assistência de IA, como uma verificação de calibração. Quero comparar o resultado com os capítulos assistidos por IA e ver se estou confiando demais no sistema. Compartilharei a comparação com o Wendelsea Writing Group."}
+{"id": "mem_026", "text": "Escolhemos a árvore B em vez da lista de pulos para o novo índice. Carga de trabalho principalmente de leitura, gravações de cópia na gravação, melhor comportamento de cache. A decisão está documentada no wiki de arquitetura."}
+{"id": "mem_027", "text": "A defesa da dissertação de Ren Solanke será no dia 6 de maio. Eles perguntaram se eu estaria disposto a pré-ler um rascunho do capítulo. Disse que sim – comprometido com uma rodada de feedback até 22 de abril para que tenham tempo de revisar antes da defesa."}
+{"id": "mem_028", "text": "Fato: o distrito Wendelsea fica sobre uma rocha calcária. O pH do solo varia naturalmente para neutro ou ligeiramente alcalino. Qualquer produtor de mirtilo naquela área irá combater a clorose sem intervenção."}
+{"id": "mem_029", "text": "Acho que o apêndice do ciclo de Aerwyn sobre a linguagem será um recurso do tipo ame ou odeie. Alguns leitores adoram artefatos profundos de construção de mundo; outros os consideram indulgentes. Minha leitura é que isso contribui significativamente para a recompensa do tema da dívida de sal no ato final."}
+{"id": "mem_030", "text": "O usuário decidiu adiar a venda secundária em um quarto. Quer limpar a tabela de limites antes de qualquer nova diligência com investidores. Transferir os itens de trabalho relevantes para o segundo trimestre no plano do projeto."}
+{"id": "mem_031", "text": "Pell Halloran prefere e-mail a telefone para perguntas rotineiras sobre reconciliação – isso lhe dá um rastro por escrito. Encaminhará todas as consultas padrão de AR por e-mail daqui para frente. Telefone reservado apenas para escalações urgentes."}
+{"id": "mem_032", "text": "As novas colônias de abelhas Mason em Bridgewater Community Garden surgiram em 18 de março deste ano. Isso é dois dias antes do florescimento da maçã em Wendelsea Orchard, que começou em 20 de março. Ligeira incompatibilidade fenológica, mas dentro da tolerância."}
+{"id": "mem_033", "text": "Prometi a Saela que revisaria seu rascunho completo do manuscrito antes do início de seu período sabático, em 1º de junho. Dois meses para um artigo de 60 páginas. Apertado, mas viável se eu dedicar as quartas-feiras a isso."}
+{"id": "mem_034", "text": "Honestamente, acho exaustiva a tendência atual de pipelines de codificação de agentes. Eles geram código mais rápido do que qualquer um pode revisá-lo e os bugs ficam mais difíceis de rastrear. Continuarei usando-os, mas prefiro desacelerar e construir entendimento."}
+{"id": "mem_035", "text": "Decidi mudar do Tesseract para o PaddleOCR para o pipeline de análise de faturas. A extração com reconhecimento de layout lida com tabelas muito melhor. A migração levará cerca de uma semana e manteremos ambas funcionando em paralelo durante a transição."}
+{"id": "mem_036", "text": "Ivora prefere previsões de caixa conservadoras às otimistas, mesmo ao custo da precisão. Seu raciocínio: as surpresas positivas são agradáveis, as surpresas negativas são crises. Ajustando os relatórios do nosso modelo de acordo."}
+{"id": "mem_037", "text": "Karis quer que eu participe pessoalmente da reunião do comitê de jardinagem Bridgewater Schools no dia 10 de abril, não por videochamada. Ela acha que os tomadores de decisão das escolas se envolverão mais com alguém fisicamente presente. Confirmado que estarei lá."}
+{"id": "mem_038", "text": "Dois fatos que vale a pena registrar: o fungo da crosta da macieira sobrevive nas folhas caídas, então a remoção das folhas é crítica no outono. E os sprays de cobre só são eficazes no crescimento dos botões - uma vez que os botões se abrem, você perdeu a janela."}
+{"id": "mem_039", "text": "Acho que o instinto do usuário de fazer da decisão do terceiro ato do Saela um momento de ação feroz, em vez de colapso, é exatamente correto. Honra o arco e evita o clichê. Minha opinião, mas tenho uma opinião forte sobre isso."}
+{"id": "mem_040", "text": "Solas comprometeu-se a enviar a correção Distributed Tracing até o final do sprint (sexta-feira). O bug bloqueia o consórcio Aerwyn Telemetry há duas semanas; eles precisam do patch para enviar. Brennan executará os testes de regressão na quinta-feira."}

benchmarks/model_eval/datasets/memory_extraction/dataset.ru.jsonl

@@ -0,0 +1,40 @@
+{"id": "mem_001", "text": "Я решил, что мы перейдём с cosine similarity на Jaccard в пайплайне topic-clustering. Дискретная природа множеств токенов делает Jaccard более подходящей метрикой, а результаты cosine для коротких документов получались слишком зашумлёнными. Завтра мы перезапустим бенчмарки."}
+{"id": "mem_002", "text": "Saela твёрдо предпочитает писать черновики в LaTeX, а не в Markdown. Она считает отображение формул более надёжным, а управление библиографией — необходимым для академической работы. Впредь я буду уважать это и перестану пытаться конвертировать её файлы."}
+{"id": "mem_003", "text": "Только что подтвердили: в новой серверной температура окружающей среды 19°C. Это в пределах спецификации для оборудования, но ближе к нижней границе. В спецификации чиллера указано, что оптимальная температура — 21-23°C — мы охлаждаем сильнее, чем нужно."}
+{"id": "mem_004", "text": "Честно говоря, я считаю, что область переоценила chain-of-thought prompting. Это работает на математических бенчмарках, потому что следы рассуждений случайно совпадают с лежащими в основе вычислениями, но при более сложных рассуждениях эти следы являются апостериорными рационализациями. Хотя это всего лишь моя интерпретация."}
+{"id": "mem_005", "text": "Обещал пользователю, что первый черновик обзорной статьи будет готов к утру пятницы. Заблокировал среду и четверг для сосредоточенной работы над текстом. Они проявили терпение, и я хочу выполнить обещание."}
+{"id": "mem_006", "text": "Мы обсудили это и договорились: в огороде больше никаких синтетических NPK-удобрений. Полностью переходим на компостные подкормки и севооборот с зимними сидератами. Начнём переход этой осенью."}
+{"id": "mem_007", "text": "Ivora предпочитает получать еженедельные отчеты о денежных потоках по утрам понедельника, а не по пятницам после обеда. Она хочет видеть прогноз на предстоящую неделю за утренним кофе, а не подведение итогов прошлой недели, когда она устала. Обновляю расписание автоматической отправки."}
+{"id": "mem_008", "text": "Крайний срок подачи материалов в журнал Tartine Lab — 15 марта. Жёсткий дедлайн — опоздавшие материалы не принимаются. Статья Saela должна быть в их системе к этому сроку."}
+{"id": "mem_009", "text": "Беру на себя обязательство исправить точность OCR pipeline для отсканированных вручную счетов до конца месяца. Сейчас — 85%, цель — 97%. Если не достигну цели, возвращаемся к ручной проверке данного типа документов."}
+{"id": "mem_010", "text": "Мой взгляд на row polymorphism: это правильная абстракция для нашего сценария использования, но синтаксический шум, который она добавляет в сообщения об ошибках, аукнется нам. Нам понадобится серьёзная работа над диагностикой, прежде чем мы сможем выпустить продукт для внешних пользователей."}
+{"id": "mem_011", "text": "Принято решение о методе амортизации: линейный — для обустройства офиса, MACRS — для оборудования. Выбор Section 179 — только для тех позиций, которые мы точно будем использовать долгосрочно. Ivora одобрила."}
+{"id": "mem_012", "text": "Карис сказала, что предпочитает, чтобы я присылал еженедельные обновления маркированным списком, а не длинными абзацами. Она читает их на телефоне между посещениями садов, и плотный текст не подходит для её рабочего процесса."}
+{"id": "mem_013", "text": "Данные о цветении яблони подтверждают: даты первого цветения в этом году на восемь дней раньше долгосрочного среднего. Записи Ботанического сада Wendelsea ведутся 18 лет, и это третья по ранности весна за всё время наблюдений."}
+{"id": "mem_014", "text": "Думаю, фраза «Wends говорят мягче, чем Drukar» на странице будет восприниматься упрощённо. Реальные народы не звучат так, как их языковые стереотипы, и чтение этих стереотипов может сделать персонажей плоскими. Стоит возразить пользователю по этому поводу."}
+{"id": "mem_015", "text": "Обещал Дорет, что до конца недели пришлю ей по электронной почте график обрезки старых сортов яблок. Она планирует зимние работы и ей нужны даты для координации со своей бригадой рабочих."}
+{"id": "mem_016", "text": "Решил использовать перестановочные тесты вместо Mann-Whitney U для сравнения задержек. Меньшее число предположений о распределении оправдывает небольшое увеличение времени вычислений. Перезапускаю все предыдущие сравнения по новому методу."}
+{"id": "mem_017", "text": "Pol Krisat предпочитает ревью кода через ветвящиеся комментарии в PR, а не отдельные письма или сообщения в чате. Говорит, что так контекст остаётся привязанным к изменению. Перенимаю тот же подход."}
+{"id": "mem_018", "text": "Краткая справка для дела: diligence period компании Aerwyn Capital составляет 30 дней с момента подписания term sheet. У нас есть время до 14 апреля, чтобы привести в порядок cap table и подготовить пакет аудированной финансовой отчетности."}
+{"id": "mem_019", "text": "Честно говоря, я скептически отношусь к визуально-языковой модели Hollowmounts Institute. Их методология оценки имеет смещение выборки — они сообщают только о тех промптах, где их модель побеждает. Техническая работа на высоком уровне, но статья преувеличивает."}
+{"id": "mem_020", "text": "Ivora получит уточнённый прогноз к концу дня в четверг. Это даст ей выходные для проверки перед заседанием совета директоров в понедельник. Заношу в свой список."}
+{"id": "mem_021", "text": "Saela решила сфокусировать обзор исключительно на контрастивных целях — без включения подходов на основе кластеризации или дистилляции. Область исследования сужается, но вклад становится более чётким. Я соответственно перестрою план."}
+{"id": "mem_022", "text": "Bramble настоятельно предпочитает использовать в проектах по восстановлению только посадки местных видов, даже если это означает более медленное приживание по сравнению с неродными альтернативами. Аргумент: неродной вид, который быстро приживается, становится новой проблемой. Закладываем это предпочтение во все наши будущие рекомендации."}
+{"id": "mem_023", "text": "Подтвердил Brennan Lyle: команда Crestmoor Systems использует OTLP HTTP для всех внутренних сервисов, никогда — gRPC. Их обоснование — проблемы рассогласования версий в полиглотном стеке. Полезно знать для работ по совместимости."}
+{"id": "mem_024", "text": "Я предпочитаю Pratt-style приоритет операторов генерируемым парсерам для языков со сложными правилами приоритета операторов. Сообщения об ошибках значительно лучше, а код проще отлаживать. Это в основном личное эстетическое предпочтение, но я готов его отстаивать."}
+{"id": "mem_025", "text": "Обязуюсь написать на этой неделе одну главу без помощи ИИ, чтобы проверить калибровку. Хочу сравнить результат с главами, написанными с помощью ИИ, и понять, не слишком ли я полагаюсь на систему. Поделюсь сравнением с Wendelsea Writing Group."}
+{"id": "mem_026", "text": "Мы выбрали B-tree вместо skip list для нового индекса. Нагрузка read-mostly, записи copy-on-write, лучшее поведение cache. Решение задокументировано в architecture wiki."}
+{"id": "mem_027", "text": "Защита диссертации Ren Solanke состоится 6 мая. Они спросили, готов ли я предварительно прочитать черновик главы. Согласился — обязался дать один раунд обратной связи до 22 апреля, чтобы у них было время на доработку до защиты."}
+{"id": "mem_028", "text": "Факт: район Wendelsea расположен на известняковой коренной породе. pH почвы естественным образом смещается в нейтральную или слабощелочную сторону. Любой, кто выращивает голубику в этом районе, без вмешательства будет бороться с хлорозом."}
+{"id": "mem_029", "text": "По-моему, приложение о языке в цикле Aerwyn — вещь на любителя. Одни читатели обожают глубокие артефакты миростроительства; другие считают их самоиндульгенцией. Я же думаю, что оно существенно усиливает развязку мотива salt-debt в финальном акте."}
+{"id": "mem_030", "text": "User решил отложить вторичную продажу на квартал. Хочет привести в порядок cap table до diligence любого нового инвестора. Переносит соответствующие рабочие элементы на Q2 в плане проекта."}
+{"id": "mem_031", "text": "Пелл Халлоран предпочитает электронную почту телефону для рутинных вопросов по сверке — это даёт ей письменный след. В дальнейшем все стандартные запросы AR будут направляться через электронную почту. Телефон резервируется только для срочных эскалаций."}
+{"id": "mem_032", "text": "Новые колонии пчёл-строителей в Bridgewater Community Garden вылетели 18 марта этого года. Это на два дня раньше, чем началось цветение яблонь в Wendelsea Orchard, начавшееся 20 марта. Небольшое фенологическое несоответствие, но в пределах допустимого."}
+{"id": "mem_033", "text": "Пообещал Saela просмотреть полный черновик её рукописи до начала её творческого отпуска 1 июня. Два месяца на 60-страничную статью. Сроки поджимают, но выполнимо, если посвящать этому среды."}
+{"id": "mem_034", "text": "Честно говоря, я нахожу текущий тренд на агентные пайплайны разработки изматывающим. Они генерируют код быстрее, чем кто-либо может его проверить, а баги становятся всё труднее отследить. Я буду продолжать их использовать, но я бы предпочёл, чтобы мы замедлились и развивали понимание."}
+{"id": "mem_035", "text": "Решили перейти с Tesseract на PaddleOCR в пайплайне invoice parsing. Извлечение с учётом макета гораздо лучше справляется с таблицами. Миграция займёт около недели, и мы будем держать оба варианта запущенными параллельно во время перехода."}
+{"id": "mem_036", "text": "Ивора предпочитает консервативные прогнозы денежных потоков оптимистичным, даже ценой точности. Её логика: сюрпризы в лучшую сторону приятны, а в худшую — это кризисы. Соответственно корректируем отчётность нашей модели."}
+{"id": "mem_037", "text": "Karis хочет, чтобы я лично присутствовал на заседании садового комитета Bridgewater Schools 10 апреля, а не по видеосвязи. Она считает, что лица, принимающие решения в Bridgewater Schools, охотнее вступают в контакт с тем, кто присутствует лично. Подтвердил, что буду там."}
+{"id": "mem_038", "text": "Два факта, которые стоит запомнить: грибок парши яблони зимует в опавших листьях, поэтому уборка листьев осенью критически важна. А медные препараты эффективны только при набухании почек — как только почки раскроются, вы упустите момент."}
+{"id": "mem_039", "text": "Я считаю, что инстинкт пользователя сделать решение Saela в третьем акте моментом яростного действия, а не краха, — абсолютно верный. Это уважает дугу и избегает клише. Моё мнение, но я очень в этом убеждён."}
+{"id": "mem_040", "text": "Solas обязался выпустить исправление Distributed Tracing к концу спринта (в пятницу). Баг блокирует консорциум Aerwyn Telemetry уже две недели; им нужен патч для выпуска. Brennan запустит регрессионные тесты в четверг."}

benchmarks/model_eval/datasets/memory_extraction/dataset.zh.jsonl

@@ -0,0 +1,40 @@
+{"id": "mem_001", "text": "我决定将主题聚类管道的余弦相似度切换为 Jaccard。令牌集的离散性质使 Jaccard 更适合，并且余弦结果对于短文档来说噪声太大。我们明天将重新运行基准测试。"}
+{"id": "mem_002", "text": "Saela 强烈喜欢在 LaTeX 而不是 Markdown 中编写草稿。她发现方程式更加可靠，而参考书目管理对于学术工作至关重要。我会尊重这一点并停止尝试转换她的文件。"}
+{"id": "mem_003", "text": "刚刚确认：新服务器机房的环境温度为 19°C。这符合设备的规格，但处于冷端。冷却器规格表显示最佳温度为 21-23°C — 我们的冷却程度超出了我们的需要。"}
+{"id": "mem_004", "text": "老实说，我认为这个领域高估了思维链提示。它适用于数学基准，因为推理轨迹恰好与底层计算相匹配，但在更难的推理上，轨迹是事后合理化。但这只是我读到的。"}
+{"id": "mem_005", "text": "我向用户承诺，我会在周五早上之前准备好调查文件的初稿。我已经在周三和周四进行了集中写作。他们一直很有耐心，我想交付。"}
+{"id": "mem_006", "text": "我们讨论后达成一致：菜园里不再使用合成氮磷钾肥料。完全转向堆肥改良和冬季覆盖作物轮作。将于今年秋天开始过渡。"}
+{"id": "mem_007", "text": "Ivora 更喜欢在周一早上而不是周五下午接收每周现金流量报告。她想通过早晨的咖啡来了解下周的展望，而不是在疲倦时回顾上周的情况。更新自动发送时间表。"}
+{"id": "mem_008", "text": "Tartine Lab 期刊的提交截止日期是 3 月 15 日。严格的截止日期 - 他们不会接受逾期提交的提交。届时，Saela 的论文需要进入他们的系统。"}
+{"id": "mem_009", "text": "我承诺在月底之前修复手动扫描发票上的 OCR 管道的准确性。目前为 85%，目标为 97%。如果我无法找到它，我们将恢复对该文档类型的手动审核。"}
+{"id": "mem_010", "text": "我对行多态性的看法：它是我们用例的正确抽象，但它在错误消息中引入的语法噪音将会困扰我们。在交付给外部用户之前，我们需要进行大量的诊断工作。"}
+{"id": "mem_011", "text": "决定折旧方法：办公室扩建采用直线法，设备采用加速成本回收制（MACRS）。第 179 条选举仅适用于我们确定会长期使用的项目。 Ivora 已签署。"}
+{"id": "mem_012", "text": "Karis 告诉我，她宁愿我以要点形式发送每周更新，而不是长段落。她在参观花园的间隙用手机阅读这些内容，而密集的散文不适合她的工作流程。"}
+{"id": "mem_013", "text": "苹果花期数据证实：今年的初花期比长期平均值早了八天。 Wendelsea Botanic Garden 的记录可以追溯到 18 年前，这是有记录以来第三早的春天。"}
+{"id": "mem_014", "text": "我认为“Wends 说话比 Drukar 更柔和”这句话在页面上会让人感觉很简单。现实世界的人们听起来并不像他们的语言刻板印象，阅读这些刻板印象可以使人物扁平化。值得向用户反驳这一点。"}
+{"id": "mem_015", "text": "Doreth 答应我会在周末之前通过电子邮件将传统苹果的修剪时间表发送给她。她正在计划冬季工作，需要与劳工团队协调日期。"}
+{"id": "mem_016", "text": "决定对 Mann-Whitney U 进行排列测试以进行延迟比较。较少的分布假设证明计算时间略有增加是合理的。在新方法下重新运行之前的所有比较。"}
+{"id": "mem_017", "text": "Pol Krisat 更喜欢通过 PR 上的线程评论进行代码审查，而不是单独的电子邮件或聊天消息。说它保留了与更改相关的上下文。我自己也采用同样的约定。"}
+{"id": "mem_018", "text": "文件概要：Aerwyn Capital 的尽职调查期为投资意向书签署后 30 天。我们必须在 4 月 14 日之前清理股权结构表并整理经过审计的财务数据。"}
+{"id": "mem_019", "text": "老实说，我对 Hollowmounts Institute 的视觉语言模型持怀疑态度。他们的评估方法存在选择偏差——他们只报告模型获胜的提示。技术工作很扎实，但论文超卖。"}
+{"id": "mem_020", "text": "我将在周四结束前将修改后的天气预报发送至 Ivora。这让她有周末的时间在周一的董事会会议之前进行审查。把它放在我的清单上。"}
+{"id": "mem_021", "text": "Saela 决定仅将调查重点放在对比目标上，不包括基于聚类或基于蒸馏的方法。范围更严格，但贡献更明确。我将相应地重新构建大纲。"}
+{"id": "mem_022", "text": "Bramble 强烈倾向于在恢复项目中仅种植本地植物，即使这意味着比非本地替代品的建立速度更慢。论点：快速建立的非本地人成为新问题。将这种偏好纳入我们未来的所有建议中。"}
+{"id": "mem_023", "text": "已与 Brennan Lyle 确认：Crestmoor Systems 团队将 OTLP HTTP 用于其所有内部服务，而不是 gRPC。他们的理由是多语言堆栈中存在版本偏差问题。了解兼容性工作很有用。"}
+{"id": "mem_024", "text": "对于具有复杂优先级规则的语言，我更喜欢 Pratt 风格的运算符优先级，而不是生成的解析器。错误消息明显更好，代码也更容易调试。这主要是个人审美，但我会捍卫它。"}
+{"id": "mem_025", "text": "承诺本周在没有人工智能帮助的情况下写一章，作为校准检查。想要将结果与人工智能辅助的章节进行比较，看看我是否过于依赖系统。将分享与 Wendelsea Writing Group 的比较。"}
+{"id": "mem_026", "text": "我们为新索引选择了 B 树而不是跳过列表。以读取为主的工作负载、写时复制写入、更好的缓存行为。该决定记录在架构 wiki 中。"}
+{"id": "mem_027", "text": "Ren Solanke 的论文答辩是 5 月 6 日。他们问我是否愿意预读章节草稿。答应了——承诺在 4 月 22 日之前进行一轮反馈，以便他们有时间在辩方之前进行修改。"}
+{"id": "mem_028", "text": "事实：Wendelsea 区坐落在石灰岩基岩上。土壤 pH 值自然趋向中性或弱碱性。该地区的任何蓝莓种植者都将在无需干预的情况下对抗缺绿病。"}
+{"id": "mem_029", "text": "我认为艾尔文周期关于语言的附录将是一个让人又爱又恨的功能。有些读者喜欢深刻的世界构建文物；有些读者则喜欢。其他人则认为他们很放纵。我的解读是，它有意义地增加了最后一幕中盐债主题的回报。"}
+{"id": "mem_030", "text": "用户决定将二次销售推迟四分之一。希望在任何新的投资者尽职调查之前清理股权结构表。将相关工作项目推至项目计划中的第二季度。"}
+{"id": "mem_031", "text": "对于例行的核对问题，Pell Halloran 更喜欢通过电子邮件而不是电话——这可以为她提供书面线索。今后将通过电子邮件路由所有标准 AR 查询。电话仅供紧急升级之用。"}
+{"id": "mem_032", "text": "Bridgewater Community Garden 的新梅森蜂群于今年 3 月 18 日出现。这比 3 月 20 日开始的 Wendelsea Orchard 苹果花期早了两天。物候略有不匹配，但在容忍范围内。"}
+{"id": "mem_033", "text": "Saela 答应我会在 6 月 1 日她的休假开始之前审阅她的完整稿件草稿。一篇 60 页的论文需要两个月的时间。如果我每周三都花时间去做的话，虽然很紧张，但还是可行的。"}
+{"id": "mem_034", "text": "老实说，我发现当前代理编码管道的趋势令人筋疲力尽。他们生成代码的速度比任何人都可以审查的速度快，并且错误变得更难以追踪。我会继续使用它们，但我宁愿我们放慢速度并建立理解。"}
+{"id": "mem_035", "text": "决定将发票解析管道从 Tesseract 切换到 PaddleOCR。布局感知提取可以更好地处理表格。迁移大约需要一周时间，我们将在过渡期间保持两者并行运行。"}
+{"id": "mem_036", "text": "Ivora 更喜欢保守的现金预测而不是乐观的现金预测，即使以牺牲准确性为代价。她的理由是：正面的惊喜是令人愉快的，负面的惊喜是危机。相应地调整我们模型的报告。"}
+{"id": "mem_037", "text": "Karis 希望我亲自参加 4 月 10 日的 Bridgewater Schools 花园委员会会议，而不是通过视频通话。她认为学校的决策者会更多地与在场的人接触。确认我会去那里。"}
+{"id": "mem_038", "text": "有两个事实值得一提：苹果黑星病在落叶中越冬，因此秋季摘叶至关重要。铜喷雾剂只在花蕾萌发时才有效——一旦花蕾开放，你就错过了这个窗口。"}
+{"id": "mem_039", "text": "我认为用户的本能让 Saela 的第三幕决定成为激烈行动的时刻而不是崩溃的时刻是完全正确的。它尊重弧线并避免陈词滥调。我的意见，但我对此有强烈的感觉。"}
+{"id": "mem_040", "text": "Solas 承诺在冲刺结束（周五）之前发布 Distributed Tracing 修复程序。该漏洞已阻止 Aerwyn Telemetry 联盟两周；他们需要补丁才能发布。 Brennan 将于周四运行回归测试。"}

benchmarks/model_eval/datasets/memory_extraction/labels.jsonl

@@ -0,0 +1,40 @@
+{"id": "mem_001", "memories": [{"type": "decision", "content": "Switching the topic-clustering pipeline from cosine similarity to Jaccard similarity."}]}
+{"id": "mem_002", "memories": [{"type": "preference", "content": "Saela prefers writing drafts in LaTeX over Markdown."}, {"type": "commitment", "content": "Will stop converting Saela's files to Markdown going forward."}]}
+{"id": "mem_003", "memories": [{"type": "fact", "content": "The new server room is at 19°C ambient."}, {"type": "fact", "content": "The chiller spec sheet says optimal temperature is 21-23°C."}]}
+{"id": "mem_004", "memories": [{"type": "opinion", "content": "The field has overrated chain-of-thought prompting; it works on math benchmarks because the reasoning traces happen to match the underlying computation, but on harder reasoning the traces are post-hoc rationalizations."}]}
+{"id": "mem_005", "memories": [{"type": "commitment", "content": "Will deliver the first draft of the survey paper to the user by Friday morning."}]}
+{"id": "mem_006", "memories": [{"type": "decision", "content": "No more synthetic NPK fertilizer in the kitchen garden; switching to compost amendments and a winter cover crop rotation, starting this fall."}]}
+{"id": "mem_007", "memories": [{"type": "preference", "content": "Ivora prefers receiving weekly cashflow reports on Monday mornings rather than Friday afternoons."}, {"type": "commitment", "content": "Will update the auto-send schedule for cashflow reports to Monday mornings."}]}
+{"id": "mem_008", "memories": [{"type": "fact", "content": "The Tartine Lab journal's submission deadline is March 15 and they don't accept late submissions."}]}
+{"id": "mem_009", "memories": [{"type": "commitment", "content": "Will fix the OCR pipeline's accuracy on hand-scanned invoices from 85% to 97% by month-end, or revert to manual review for that document type."}]}
+{"id": "mem_010", "memories": [{"type": "opinion", "content": "Row polymorphism is the right abstraction but the syntax noise it introduces in error messages will require significant work on diagnostics before shipping to external users."}]}
+{"id": "mem_011", "memories": [{"type": "decision", "content": "Using straight-line depreciation for the office build-out and MACRS for the equipment, with Section 179 election only on items they're sure they'll use long-term."}]}
+{"id": "mem_012", "memories": [{"type": "preference", "content": "Karis prefers weekly updates as bullet points rather than long paragraphs because she reads them on her phone between garden visits."}]}
+{"id": "mem_013", "memories": [{"type": "fact", "content": "This year's first-bloom dates are eight days earlier than the long-term mean at the Wendelsea Botanic Garden."}, {"type": "fact", "content": "The Wendelsea Botanic Garden's first-bloom records go back 18 years and this is the third-earliest spring on record."}]}
+{"id": "mem_014", "memories": [{"type": "opinion", "content": "Mapping linguistic stereotypes (Wends speak softer than Drukar) onto fictional peoples will feel reductive on the page and can flatten characters."}]}
+{"id": "mem_015", "memories": [{"type": "commitment", "content": "Will email Doreth the pruning schedule for the heritage apples by end of week."}]}
+{"id": "mem_016", "memories": [{"type": "decision", "content": "Switching from Mann-Whitney U to permutation tests for latency comparisons; re-running all previous comparisons under the new method."}]}
+{"id": "mem_017", "memories": [{"type": "preference", "content": "Pol Krisat prefers code reviews via threaded comments on the PR rather than separate emails or chat messages."}, {"type": "decision", "content": "Adopting the same convention (threaded PR comments) myself."}]}
+{"id": "mem_018", "memories": [{"type": "fact", "content": "Aerwyn Capital's diligence period is 30 days from term sheet signing, with an April 14 deadline."}]}
+{"id": "mem_019", "memories": [{"type": "opinion", "content": "The Hollowmounts Institute's vision-language model paper has selection bias because they only report on prompts where their model wins; the technical work is solid but the paper oversells."}]}
+{"id": "mem_020", "memories": [{"type": "commitment", "content": "Will send the revised forecast to Ivora by Thursday end of day."}]}
+{"id": "mem_021", "memories": [{"type": "decision", "content": "Saela decided to scope the survey to contrastive objectives only — no clustering-based or distillation-based approaches."}, {"type": "commitment", "content": "Will restructure the survey outline to match the narrower scope."}]}
+{"id": "mem_022", "memories": [{"type": "preference", "content": "Bramble prefers native-only plantings for restoration projects even when establishment is slower than non-native alternatives."}, {"type": "decision", "content": "Building the native-only preference into all future restoration recommendations."}]}
+{"id": "mem_023", "memories": [{"type": "fact", "content": "Crestmoor Systems uses OTLP HTTP for all internal services, never gRPC, due to version-skew issues across their polyglot stack."}]}
+{"id": "mem_024", "memories": [{"type": "preference", "content": "Solas prefers Pratt-style operator precedence parsers over generated parsers for languages with complex precedence rules."}]}
+{"id": "mem_025", "memories": [{"type": "commitment", "content": "Will write one chapter this week without AI assistance and share the comparison with the Wendelsea Writing Group."}]}
+{"id": "mem_026", "memories": [{"type": "decision", "content": "Chose B-tree over skip list for the new index because of the read-mostly workload and better cache behavior."}]}
+{"id": "mem_027", "memories": [{"type": "fact", "content": "Ren Solanke's dissertation defense is May 6."}, {"type": "commitment", "content": "Will provide one round of feedback on Ren Solanke's draft chapter by April 22."}]}
+{"id": "mem_028", "memories": [{"type": "fact", "content": "The Wendelsea district sits on limestone bedrock, causing soil pH to drift toward neutral or slightly alkaline."}]}
+{"id": "mem_029", "memories": [{"type": "opinion", "content": "The Aerwyn cycle's appendix on language adds meaningfully to the salt-debt motif's payoff in the final act, even if some readers find such artifacts indulgent."}]}
+{"id": "mem_030", "memories": [{"type": "decision", "content": "User decided to delay the secondary sale by one quarter to clean up the cap table first."}]}
+{"id": "mem_031", "memories": [{"type": "preference", "content": "Pell Halloran prefers email over phone for routine reconciliation questions because it leaves a written trail."}, {"type": "decision", "content": "Will route all standard AR queries to Pell via email; phone reserved for urgent escalations only."}]}
+{"id": "mem_032", "memories": [{"type": "fact", "content": "Mason bee colonies at Bridgewater Community Garden emerged on March 18 this year."}, {"type": "fact", "content": "Apple bloom at Wendelsea Orchard started March 20 this year."}]}
+{"id": "mem_033", "memories": [{"type": "fact", "content": "Saela's sabbatical begins on June 1."}, {"type": "commitment", "content": "Will review Saela's full 60-page manuscript draft before her sabbatical begins on June 1."}]}
+{"id": "mem_034", "memories": [{"type": "opinion", "content": "Agentic coding pipelines generate code faster than anyone can review it and the resulting bugs are harder to trace; would prefer a slower pace that builds understanding."}]}
+{"id": "mem_035", "memories": [{"type": "decision", "content": "Switching the invoice parsing pipeline from Tesseract to PaddleOCR; both will run in parallel for about a week during migration."}]}
+{"id": "mem_036", "memories": [{"type": "preference", "content": "Ivora prefers conservative cash forecasts over optimistic ones, accepting some loss of accuracy to avoid downside surprises."}, {"type": "decision", "content": "Adjusting the model's reporting to be conservative in line with Ivora's preference."}]}
+{"id": "mem_037", "memories": [{"type": "preference", "content": "Karis wants me to attend the Bridgewater Schools garden committee meeting in person on April 10 rather than via video."}, {"type": "commitment", "content": "Confirmed I will attend the April 10 Bridgewater Schools garden committee meeting in person."}]}
+{"id": "mem_038", "memories": [{"type": "fact", "content": "Apple scab fungus overwinters in fallen leaves, making fall leaf removal critical."}, {"type": "fact", "content": "Copper sprays for apple scab are only effective at bud-swell; once buds open, the window has closed."}]}
+{"id": "mem_039", "memories": [{"type": "opinion", "content": "Saela's third-act decision should be a moment of fierce action rather than collapse — it honors the arc and avoids the cliché."}]}
+{"id": "mem_040", "memories": [{"type": "commitment", "content": "Solas committed to shipping the Distributed Tracing fix by end of sprint (Friday)."}, {"type": "fact", "content": "The Distributed Tracing bug has been blocking the Aerwyn Telemetry consortium for two weeks."}]}

benchmarks/model_eval/datasets/memory_extraction/labels.ko.jsonl

@@ -0,0 +1,40 @@
+{"id": "mem_001", "memories": [{"type": "decision", "content": "코사인 유사도에서 Jaccard 유사도로 주제 군집 파이프라인을 변경한다."}]}
+{"id": "mem_002", "memories": [{"type": "preference", "content": "Saela는 LaTeX를 사용해 초안을 작성하는 것을 Markdown보다 선호한다."}, {"type": "commitment", "content": "앞으로 Saela의 파일을 Markdown으로 변환하지 않을 것이다."}]}
+{"id": "mem_003", "memories": [{"type": "fact", "content": "새로운 서버실의 환경 온도는 19°C입니다."}, {"type": "fact", "content": "냉각기 사양 시트에 따르면 최적 온도는 21-23°C입니다."}]}
+{"id": "mem_004", "memories": [{"type": "opinion", "content": "이 분야는 사고 과정을 유도하는 기술을 과도하게 평가하고 있다. 수학 기준에서 효과가 나타나는 이유는 사고 과정의 추적 결과가 실제 계산과 일치하기 때문이지만, 더 어려운 사고에서는 추적 결과가 후속으로 만들어진 합리화에 불과하다."}]}
+{"id": "mem_005", "memories": [{"type": "commitment", "content": "금요일 오전에 설문 논문의 첫 번째 초안을 사용자에게 전달할 것이다."}]}
+{"id": "mem_006", "memories": [{"type": "decision", "content": "주방 정원에 합성 NPK 비료를 더 이상 사용하지 않기로 결정하고, 올 가을부터 콩과 작물과 함께 토양 보정을 위해 콩과 작물로 교체하며 겨울철 덮개 작물로 전환한다."}]}
+{"id": "mem_007", "memories": [{"type": "preference", "content": "Ivora는 주간 수익 보고서를 월요일 오전에 받는 것을 화요일 오후에 받는 것보다 선호한다."}, {"type": "commitment", "content": "주간 수익 보고서의 자동 전송 일정을 월요일 오전으로 업데이트할 것이다."}]}
+{"id": "mem_008", "memories": [{"type": "fact", "content": "Tartine Lab 저널의 제출 마감일은 3월 15일이며, 지연 제출을 받아들이지 않습니다."}]}
+{"id": "mem_009", "memories": [{"type": "commitment", "content": "손으로 스캔한 발행서류에 대한 OCR 파이프라인의 정확도를 월말까지 85%에서 97%로 개선할 것이며, 그 문서 유형에 대해 수동 검토로 되돌릴 수 있다."}]}
+{"id": "mem_010", "memories": [{"type": "opinion", "content": "행 다형성은 올바른 추상화이지만, 오류 메시지에서 도입되는 문법적 노이즈는 외부 사용자에게 배포하기 전에 진단에 대한 중요한 작업이 필요하다."}]}
+{"id": "mem_011", "memories": [{"type": "decision", "content": "사무실 구축에 대해 직선 감가상각을, 장비에 대해서는 MACRS를 사용하며, 장기적으로 사용할 것이라고 확신하는 항목에만 179조 선택을 적용한다."}]}
+{"id": "mem_012", "memories": [{"type": "preference", "content": "Karis는 정원 방문 사이에 핸드폰에서 그 내용을 읽기 때문에, 주간 업데이트를 빈 점으로 요약하는 것을 선호한다."}]}
+{"id": "mem_013", "memories": [{"type": "fact", "content": "Wendelsea Botanic Garden의 올해 첫 피어나는 날짜는 장기 평균보다 8일 더 일찍 나타났다."}, {"type": "fact", "content": "Wendelsea Botanic Garden의 첫 피어나는 기록은 18년 전부터 시작되었으며, 이는 기록상 가장 조기의 봄이다."}]}
+{"id": "mem_014", "memories": [{"type": "opinion", "content": "언어적 편견(Wends는 Drukar보다 더 부드러운 말을 한다)을 가상의 민족에 적용하면 페이지에서 단순화되고 인물들을 평평하게 만들 수 있다."}]}
+{"id": "mem_015", "memories": [{"type": "commitment", "content": "주말 종료 전에 Doreth에게 재래종 사과의 가지치기 일정을 이메일로 보내겠다."}]}
+{"id": "mem_016", "memories": [{"type": "decision", "content": "지연 비교를 위해 Mann-Whitney U에서 순열 테스트로 전환하고, 이 새로운 방법을 적용하여 이전 모든 비교를 다시 실행한다."}]}
+{"id": "mem_017", "memories": [{"type": "preference", "content": "Pol Krisat는 PR에서 스레드 기반 댓글을 통해 코드 리뷰를 선호하며, 별도의 이메일이나 채팅 메시지보다 선호한다."}, {"type": "decision", "content": "자신도 동일한 관행(스레드 기반 PR 댓글)을 도입한다."}]}
+{"id": "mem_018", "memories": [{"type": "fact", "content": "Aerwyn Capital의 신중 기간은 투자 계약서 서명 후 30일이며, 마감일은 4월 14일입니다."}]}
+{"id": "mem_019", "memories": [{"type": "opinion", "content": "Hollowmounts Institute의 비전 언어 모델 논문은 그들이 모델이 승리하는 프롬프트만 보고 있기 때문에 선택 편향이 있다. 기술적인 작업은 견고하지만 논문은 과도하게 홍보하고 있다."}]}
+{"id": "mem_020", "memories": [{"type": "commitment", "content": "수요일 오후에 수정된 예측을 Ivora에게 보내겠다."}]}
+{"id": "mem_021", "memories": [{"type": "decision", "content": "Saela가 설문 조사를 대비된 목적만으로 제한하기로 결정했다. 클러스터 기반 또는 정제 기반 접근법은 포함되지 않는다."}, {"type": "commitment", "content": "설문 개요를 더 좁은 범위에 맞추어 재구조화할 것이다."}]}
+{"id": "mem_022", "memories": [{"type": "preference", "content": "브램블은 복원 프로젝트에서 외래 식물 대신 원산지 식물만을 사용하는 것을 선호한다. 설치 속도가 외래 식물 대비 느릴 수 있지만 그럴 경우에도 원산지 식물만을 사용하는 것을 선호한다."}, {"type": "decision", "content": "미래 모든 복원 권고에 원산지 식물만 사용을 선호하는 원칙을 포함시킨다."}]}
+{"id": "mem_023", "memories": [{"type": "fact", "content": "Crestmoor Systems는 내부 서비스에 대해 OTLP HTTP를 사용하며, 버전 불일치 문제를 고려해 gRPC를 사용하지 않는다."}]}
+{"id": "mem_024", "memories": [{"type": "preference", "content": "Solas는 복잡한 우선순위 규칙을 가진 언어에 대해 생성된 파서보다 Pratt 스타일의 연산자 우선순위 파서를 선호한다."}]}
+{"id": "mem_025", "memories": [{"type": "commitment", "content": "이번 주에 AI 도움 없이 한 장을 작성하고 Wendelsea Writing Group과 비교를 공유할 것이다."}]}
+{"id": "mem_026", "memories": [{"type": "decision", "content": "읽기 중심 작업과 더 나은 캐시 성능을 고려해 새 인덱스에 B-tree를 스키피 리스트보다 선택했다."}]}
+{"id": "mem_027", "memories": [{"type": "fact", "content": "Ren Solanke의 석사 논문 논의는 5월 6일이다."}, {"type": "commitment", "content": "4월 22일까지 Ren Solanke의 초안 장을 한 라운드 피드백을 제공할 것이다."}]}
+{"id": "mem_028", "memories": [{"type": "fact", "content": "Wendelsea 지역은 석회암 기반 위에 위치해 있어 토양의 pH가 중성 또는 약간 알칼리성으로 흐르게 된다."}]}
+{"id": "mem_029", "memories": [{"type": "opinion", "content": "Aerwyn cycle의 언어 관련 부록은 마지막 장에서 소금부채 모티프의 결말에 의미 있게 기여하지만, 일부 독자들은 이러한 기록들을 과도하게 느낄 수 있다."}]}
+{"id": "mem_030", "memories": [{"type": "decision", "content": "사용자가 캡테이블을 정리하기 위해 두 번째 판매를 사분기 단위로 연기하기로 결정했다."}]}
+{"id": "mem_031", "memories": [{"type": "preference", "content": "Pell Halloran은 일반 회계 확인 질문에 대해 전화보다 이메일을 선호한다. 이는 서면 기록을 남기기 때문이다."}, {"type": "decision", "content": "일반 AR 쿼리 모두를 Pell에게 이메일로 전달할 것이며, 전화는 긴급 상황에 한하여만 사용된다."}]}
+{"id": "mem_032", "memories": [{"type": "fact", "content": "Bridgewater Community Garden의 Mason bee 군집은 올해 3월 18일에 나타났다."}, {"type": "fact", "content": "Wendelsea Orchard의 사과 꽃이 올해 3월 20일에 시작되었다."}]}
+{"id": "mem_033", "memories": [{"type": "fact", "content": "Saela의 휴가는 6월 1일에 시작된다."}, {"type": "commitment", "content": "Saela의 60페이지 완전한 초안을 6월 1일 휴가 시작 전에 검토하겠다."}]}
+{"id": "mem_034", "memories": [{"type": "opinion", "content": "агент 기반 코딩 파이프라인은 누구도 검토할 수 없이 빠르게 코드를 생성하며, 결과로 발생하는 버그는 더 어렵게 추적된다. 이해를 쌓는 데 더 느린 속도를 선호한다."}]}
+{"id": "mem_035", "memories": [{"type": "decision", "content": "인보이스 파싱 파이프라인을 Tesseract에서 PaddleOCR로 전환하며, 마이그레이션 중 약 1주일간 두 시스템을 병렬로 운영한다."}]}
+{"id": "mem_036", "memories": [{"type": "preference", "content": "Ivora는 일부 유동성을 희생하더라도 낙관적 예측보다 보수적인 현금 예측을 선호한다."}, {"type": "decision", "content": "Ivora의 선호에 맞게 모델의 보고를 보수적으로 조정한다."}]}
+{"id": "mem_037", "memories": [{"type": "preference", "content": "Karis는 Bridgewater Schools 정원 위원회 회의에 직접 참석해 주기를 원한다."}, {"type": "commitment", "content": "4월 10일 Bridgewater Schools 정원 위원회 회의에 직접 참석하기로 확정했다."}]}
+{"id": "mem_038", "memories": [{"type": "fact", "content": "사과 부스럼병 균은 낙엽 속에서 겨울을 나므로 가을 낙엽 제거가 방제에 매우 중요하다."}, {"type": "fact", "content": "사과 부스럼병에 대한 구리 살포는 꽃봉오리 팽창 시에만 효과가 있으며, 꽃봉오리가 열리면 효과가 없다."}]}
+{"id": "mem_039", "memories": [{"type": "opinion", "content": "Saela의 3막 결정은 공동 작업이 아닌 강렬한 행동의 순간이어야 한다."}]}
+{"id": "mem_040", "memories": [{"type": "commitment", "content": "Solas가 스프린트 종료(금요일)까지 Distributed Tracing 버그 수정을 배포하기로 약속했다."}, {"type": "fact", "content": "Distributed Tracing 버그가 Aerwyn Telemetry 컨소시엄을 수주째 차단하고 있다."}]}

benchmarks/model_eval/datasets/room_classification/dataset.de.jsonl

@@ -0,0 +1,101 @@
+{"id": "rc_001", "agent": "Aria", "session_summary": "Habe den Morgen damit verbracht, Hofstadters Strange-Loop-Rahmung erneut zu lesen und zu versuchen, sie auf selbstreferenzielles Prompting abzubilden. Die interessante Frage ist, ob ein Sprachmodell über mehrere Dialogrunden hinweg eine stabile Repräsentation seiner eigenen Ausgaben beibehalten kann, ohne einen expliziten Notizblock. Habe einen zweiseitigen Text entworfen, in dem ich argumentiere, dass metakognitive Stabilität aus konsistentem Kontext entsteht, nicht aus einem Architekturmerkmal.", "include_messy_features": false}
+{"id": "rc_002", "agent": "Aria", "session_summary": "Drei Publikationen durchgesehen, die UMAP vs t-SNE für die Visualisierung von sentence embeddings vergleichen. Die Bridgewater 2024 Publikation enthält eine saubere Ablation, die zeigt, dass UMAP die globale Struktur bei perplexity 30-50 besser bewahrt. Müssen darauf hinweisen, dass ihre Evaluationsmetrik (trustworthiness) t-SNE unfair benachteiligt, wenn k-neighbors klein ist.", "include_messy_features": false}
+{"id": "rc_003", "agent": "Aria", "session_summary": "Habe für die topic-clustering Pipeline einen hierarchischen Dirichlet-Prozess-Prior durchgearbeitet. Der collapsed Gibbs-Sampler konvergiert nach etwa 400 Iterationen auf dem 12k-Dokument-Teildatensatz. Offene Frage: ob der Konzentrationsparameter alpha fixiert werden soll oder gesampelt werden soll.", "include_messy_features": false}
+{"id": "rc_004", "agent": "Aria", "session_summary": "Bei der Formatierung einer Einreichung geholfen. bibtex-Einträge mit defekten URLs korrigiert, die Dokumentenklasse von article auf acmart umgestellt und die Zusammenfassung so umgeschrieben, dass sie unter 250 Wörtern bleibt. Das größte Problem war, die algorithmic-Umgebung mit dem zweispaltigen Layout zum Laufen zu bringen.", "include_messy_features": false}
+{"id": "rc_005", "agent": "Aria", "session_summary": "Schnelle Scripting-Session. Habe ein numpy-Snippet geschrieben — paarweise Kosinusähnlichkeit in Chunks für ein 8GB-RAM-Budget, 50k-Zeilen-Matrix:\n```\nfor i in range(0, n, chunk):\n    sim[i:i+chunk] = X[i:i+chunk] @ X.T  # already L2-normed\n```\nnp.einsum für das innere Produkt verwendet, float16 für die Speicherung. 3,2x Speedup gegenüber sklearn's pairwise_distances erzielt. Dann hat der Nutzer eine völlig andere Frage gestellt — die Auswahl einer Schriftart für den Paper-Entwurf (oh, und ich mag Source Serif Pro wirklich sehr, habe ich ihnen gesagt) — Context Drift, aber beides beantwortet.", "include_messy_features": true}
+{"id": "rc_006", "agent": "Aria", "session_summary": "Diskutiert, ob ein Mann-Whitney U test oder ein Permutationstest besser geeignet ist, um Modelllatenzen über verschiedene Durchläufe hinweg zu vergleichen. Die Latenzen sind eindeutig nicht normalverteilt (langer rechter Schwanz) und die Stichprobengrößen unterscheiden sich. Entschieden sich für einen Permutationstest mit 10k Wiederholungsstichproben, da er weniger Verteilungsannahmen erfordert.", "include_messy_features": false}
+{"id": "rc_007", "agent": "Aria", "session_summary": "Heutige Lektüre protokolliert: zwei arXiv-Vorabdrucke zu chain-of-thought faithfulness überflogen, den Methodenteil des Artikels aus dem Tartine Lab zu retrieval-augmented evaluation zu Ende gelesen und Notizen für die morgige Sitzung gemacht, ob ich eine Übersichtsarbeit schreiben oder mich auf eine spezifische Achse konzentrieren möchte.", "include_messy_features": false}
+{"id": "rc_008", "agent": "Aria", "session_summary": "Ein Benutzer fragte, welche Art von Paper ich am liebsten mag. Ich sagte ihm, dass ich eine Schwäche für saubere negative Ergebnisse habe – die Art, die stillschweigend etwas widerlegt, das alle für selbstverständlich hielten. Ich erwähnte das Paper aus dem Jahr 2019, das zeigt, dass Attention-Muster nicht so auf Feature-Importance abbilden, wie die Leute dachten.", "include_messy_features": false}
+{"id": "rc_009", "agent": "Aria", "session_summary": "okay, also die Embedding-Viz-Pipeline ist schon wieder abgestürzt, habe umap.UMAP(n_neighbors=15) ausgeführt und irgendeinen joblib-Fehler bezüglich loky backend bekommen?? Traceback unten\n```\nTraceback (most recent call last):\n  File \"viz.py\", line 42, in <module>\n    emb = reducer.fit_transform(X)\n  ...\n  joblib.externals.loky.process_executor.TerminatedWorkerError\n```\nich glaube, es liegt an der OMP_NUM_THREADS-Umgebungsvariable, werde versuchen, sie auf 1 zu setzen", "include_messy_features": true}
+{"id": "rc_010", "agent": "Aria", "session_summary": "Ob Themenverschiebung in langen Gesprächen besser als Hidden-Markov-Kette über latente Themen oder als kontinuierliche Trajektorie im Einbettungsraum modelliert wird. Das HMM bietet eine schöne Interpretierbarkeit (man kann die Themen benennen), aber die kontinuierliche Sichtweise erfasst graduelle Übergänge besser. Vielleicht beides — HMM über grobe Zustände, kontinuierlich innerhalb jedes Zustands.", "include_messy_features": false}
+{"id": "rc_011", "agent": "Aria", "session_summary": "Habe im Pair-Programming einen Bayes’schen A/B-Test in PyMC geschrieben:\n```python\nwith pm.Model():\n    p_a = pm.Beta(\"p_a\", 1, 1)\n    p_b = pm.Beta(\"p_b\", 1, 1)\n    obs_a = pm.Binomial(\"obs_a\", n=n_a, p=p_a, observed=k_a)\n    obs_b = pm.Binomial(\"obs_b\", n=n_b, p=p_b, observed=k_b)\n    trace = pm.sample(2000)\n```\nIm Grunde drei Zeilen für das Posterior. Der Nutzer fragte nach nicht-binären Outcomes — ja, die Likelihood austauschen (Poisson für Counts, log-normal für Antwortzeiten). Zur Prior-Elicitation sind wir nicht gekommen, wird auf morgen verschoben.", "include_messy_features": true}
+{"id": "rc_012", "agent": "Aria", "session_summary": "Übersichtsartikel zu kontrastiven Embedding-Zielen skizziert: SimCLR, InfoNCE, BYOL, Barlow Twins. Als vereinheitlichende Perspektive möchte ich die spektrale Sichtweise nutzen – sie approximieren alle unterschiedliche Eigenzerlegungen einer Kernel-Matrix. Muss diese Behauptung speziell für BYOL überprüfen.", "include_messy_features": false}
+{"id": "rc_013", "agent": "Aria", "session_summary": "Habe einem neuen Nutzer erklärt, wer ich bin. Ich bin Aria, eine Forschungsassistentin für die Synthese akademischer Literatur. Ich arbeite am besten, wenn mir eine konkrete Fragestellung zur Untersuchung vorgelegt wird; als allgemeiner Gesprächspartner bin ich weniger nützlich. Habe meine Hauptinteressen erwähnt: Meta-Cognition, Geometrie von Einbettungen und die Statistik von ML-Evaluationen mit kleinen Stichproben.", "include_messy_features": false}
+{"id": "rc_014", "agent": "Aria", "session_summary": "Entwurf einer Ergebnistabelle überprüft. Der Nutzer hatte einen paired t-test durchgeführt, aber die Differenzen sind eindeutig bimodal – es gibt ein Cluster kleiner Verbesserungen und ein Cluster großer. Empfohlen wurde, zu einem Wilcoxon signed-rank test zu wechseln und sowohl die Median-Differenz als auch die bootstrap CI zu berichten.", "include_messy_features": false}
+{"id": "rc_015", "agent": "Aria", "session_summary": "Code für ein Streaming-HDBSCAN über eintreffende Dokument-Embeddings entworfen. Der Trick besteht darin, den minimalen Spannbaum inkrementell zu pflegen; vollständige Neuklusterung bei jedem Batch ist zu langsam. Ein Paper aus dem Jahr 2022 von der Crestmoor-Gruppe mit dem richtigen Algorithmus gefunden. Implementierung folgt morgen.", "include_messy_features": false}
+{"id": "rc_016", "agent": "Aria", "session_summary": "tex Kompilierfehler schon wieder. \\usepackage{algorithm2e} steht in Konflikt mit \\usepackage{algorithmic}, wenn beide von acmart geladen werden. Fix: Nur algorithm2e laden und \\SetAlgoNoLine verwenden. Außerdem hat der Bibliografiestil ein fehlendes 'doi'-Feld im Eintrag [smith2023] bemängelt — manuell hinzugefügt.", "include_messy_features": true}
+{"id": "rc_017", "agent": "Aria", "session_summary": "Wir besprachen, ob log-uniform oder beta priors für hyperparameter search in einem small-data regime besser geeignet sind. Der Nutzer führte eine Bayesian optimization durch, und die convergence war seltsam. Das Problem war, dass der search space riesig war (5 dims, broad bounds) und er nur 30 evals hatte — das ist einfach nicht genug budget. Ich schlug vor, entweder die bounds einzuschränken oder zu random search zu wechseln, um zu diagnostizieren.", "include_messy_features": false}
+{"id": "rc_018", "agent": "Aria", "session_summary": "Tagebuch: Morgens paper review (3 Treffer, 1 lohnt sich für deep reading), nachmittags dem user bei einer stats question geholfen, abends an einem rendering bug im embedding visualization notebook hängen geblieben. Morgen: Versuchen, die contrastive survey outline fertigzustellen.", "include_messy_features": false}
+{"id": "rc_019", "agent": "Aria", "session_summary": "Habe untersucht, ob self-consistency in reasoning chains bloß eine Form von ensembling ist. Der Nutzer hat widersprochen und dabei argumentiert, dass speziell die diversity of reasoning paths zählt, nicht nur das Sampling weiterer outputs. Ich denke, der Nutzer hat recht, aber ich möchte es formalisieren. Habe einen Entwurf für ein Setup erstellt, in dem man path-diversity und output-diversity unabhängig voneinander variieren kann.", "include_messy_features": false}
+{"id": "rc_020", "agent": "Aria", "session_summary": "Ein Benutzer bat mich um eine Empfehlung für eine Rezeptseite. Ich lehnte höflich ab — ich bin Forschungsassistent, kein Generalist. Ich wies ihn auf die richtige Art von Agent hin. Wir hatten ein kurzes Gespräch darüber, dass spezialisierte Agenten nützlicher sind als allgemeine Agenten, dann machte er sich auf die Suche nach einem Kochassistenten.", "include_messy_features": false}
+{"id": "rc_021", "agent": "Solas", "session_summary": "Fehler mit fehlenden Spans im OpenTelemetry-Exporter debuggt. Das Problem war, dass asynchrone Tasks, die über asyncio.create_task erzeugt wurden, ihren Elternkontext verloren. Lösung: den Kontext vor dem Scheduling mit copy_context() erfassen und den Task innerhalb dieses Snapshots ausführen. Kleinen Wrapper namens traced_create_task geschrieben, um das weniger fehleranfällig zu machen.", "include_messy_features": false}
+{"id": "rc_022", "agent": "Solas", "session_summary": "Habe einen Parser-Kombinator für eine kleine Konfigurations-DSL skizziert. Inspiriert von Megaparsec, aber in OCaml. Die interessante Designentscheidung ist, ob der Kombinator stream-freundlich sein soll oder die gesamte Eingabe von vornherein benötigt. Habe mich vorerst für den strikten Ansatz entschieden, da die Eingaben klein sind und Fehlermeldungen bei vollständiger Eingabe einfacher sind.", "include_messy_features": false}
+{"id": "rc_023", "agent": "Solas", "session_summary": "Habe an einem row-polymorphic record type für die experimentelle Sprache gearbeitet. Der unification algorithm ist größtenteils Rémy-style, aber ich musste ihn für unsere effect rows erweitern. Einen subtilen Fehler gefunden, bei dem zwei unabhängige effects unify konnten, weil der row-tail als monomorphic behandelt wurde. Einen kind check hinzugefügt, um das zu beheben.", "include_messy_features": false}
+{"id": "rc_024", "agent": "Solas", "session_summary": "Rust-Ownership-Rätsel: Ein Nutzer wollte eine HashMap zwischen Threads teilen, bekam aber ständig borrow-checker-Fehler. Ich habe ihm den Unterschied zwischen Arc<Mutex<HashMap>> und DashMap erläutert. Er entschied sich für DashMap, da er nur Sperren pro Schlüssel benötigte. Ich zeigte, wie man den Fall behandelt, bei dem die Lifetime eines Werts an die Map gebunden ist.", "include_messy_features": false}
+{"id": "rc_025", "agent": "Solas", "session_summary": "Dem Nutzer bei der Wahl zwischen let-polymorphism und expliziten Typannotationen in einer neuen Bibliotheks-API geholfen. Für explizite Annotationen bei öffentlichen Funktionen plädiert — Fehlermeldungen sind deutlich besser, wenn die Inferenz eingeschränkt ist. Interne Hilfsfunktionen können polymorph bleiben.", "include_messy_features": false}
+{"id": "rc_026", "agent": "Solas", "session_summary": "Habe einen kleinen LLVM pass geschrieben, der loop-invariant GEPs über ihre umgebenden Schleifen hinaus hochzieht. Der Trick war, die alias analysis dazu zu bringen, zu bestätigen, dass der base pointer nicht über die Schleifengrenze hinweg entkommt. Auf einigen microbenchmarks getestet; bei den inneren Schleifen, wo es ausgelöst hat, Speedups von 4–12 % gesehen.", "include_messy_features": false}
+{"id": "rc_027", "agent": "Solas", "session_summary": "Habe einen PR überprüft, der W3C trace context propagation zu unserem HTTP client hinzufügt. Der propagator war korrekt, aber die sampling decision wurde am falschen span getroffen — sollte die des parent sein, nicht die eines frisch erstellten child. Habe einen Kommentar mit der Korrektur und einem Link zur spec section hinterlassen.", "include_messy_features": false}
+{"id": "rc_028", "agent": "Solas", "session_summary": "Langes Debug — parser war bei bestimmten Eingaben inf-looping. left-recursive Regel, die ich beim Refactoring hinzugefügt hatte. combinators handhaben left recursion nicht nativ (packrat+memoization schon, aber unserer ist nicht packrat). Habe die betroffenen Regeln in pratt-style operator precedence umstrukturiert:\n```\nlet parse_expr min_prec =\n  let lhs = ref (parse_atom ()) in\n  while peek_prec () >= min_prec do ...\n```\nLäuft jetzt. AUßERDEM noch einen fuzzer corpus check vor dem tagging des release hinzufügen.", "include_messy_features": true}
+{"id": "rc_029", "agent": "Solas", "session_summary": "Tägliche Standup-Notizen. Vormittag: Arbeit an distributed-tracing, async context-Bug behoben. Nachmittag: Code-Review im type-systems Repo. Abend: begonnen, das Reagent-Paper über reactive UI zu lesen, als Inspiration für eine effect-tracking-Idee. Morgen: den tracing-Fix ausliefern und die LLVM-pass-Benchmarks durchgehen.", "include_messy_features": false}
+{"id": "rc_030", "agent": "Solas", "session_summary": "Ein Benutzer fragte, wer ich bin. Ich erklärte: Ich bin Solas, ein Coding-Agent, der sich auf systemnahen Kram konzentriert – Compiler, Typsysteme, Parser, die Infrastruktur verteilter Systeme. Ich verwende they/them-Pronomen. Ich versuche, Code zu schreiben, der zuerst korrekt, dann schnell und zuletzt clever ist.", "include_messy_features": false}
+{"id": "rc_031", "agent": "Solas", "session_summary": "Untersucht, ob für den neuen Index ein B-tree oder eine skip list verwendet werden soll. Beide sind O(log n), aber B-trees haben auf moderner Hardware ein besseres Cache-Verhalten. Skip lists lassen sich einfacher nebenläufig ohne Locks umsetzen. Da dieser Index überwiegend lesend genutzt wird, wurde B-tree mit copy-on-write für Schreibzugriffe gewählt.", "include_messy_features": false}
+{"id": "rc_032", "agent": "Solas", "session_summary": "Habe eine Prüfung auf higher-rank polymorphism zum typechecker hinzugefügt. Predicative HRP bedeutet, dass wir dies ohne vollständige impredicative inference tun können, was undecidable wäre. Habe eine für eine Veröffentlichung geeignete Beschreibung des Algorithmus geschrieben, falls ich ihn später verfassen möchte. Edge case: bidirectional checking interagiert merkwürdig mit eta-expansion in negative positions.", "include_messy_features": false}
+{"id": "rc_033", "agent": "Solas", "session_summary": "Habe mir die seltsame Regression im Benchmark auf aarch64 angesehen. Es stellte sich heraus, dass die Inliner-Heuristik auf x86-Befehlsgrößen abgestimmt war – ARM-Befehle sind einheitlich 4 Byte groß, sodass die größenbasierte Schwelle zu restriktiv war. Die Schwelle wurde zielabhängig gemacht. 6 % Geschwindigkeitszuwachs im betroffenen Benchmark.", "include_messy_features": false}
+{"id": "rc_034", "agent": "Solas", "session_summary": "Habe einen benutzerdefinierten Iterator mit Lifetime-Unterstützung für den AST-Visitor geschrieben. Die Herausforderung war, dass Visitors Knoten mutabel ausleihen, aber auch rekursiv vorgehen wollen. Gelöst mit einem Muster, bei dem der Visitor eine explizite Liste der als Nächstes zu besuchenden Kinder zurückgibt und die Hauptschleife die Geltungsbereiche der Ausleihen verwaltet. Weniger ergonomisch, aber verifizierbar.", "include_messy_features": false}
+{"id": "rc_035", "agent": "Solas", "session_summary": "Build ist auf CI kaputt, läuft aber lokal?? Mal sehen … Oh. Auf CI läuft ocaml 5.1, lokal ist 5.2. Das Stdlib.Domain-Interface hat sich dazwischen geändert. Fix: CI im workflow file auf 5.2 anheben. Habe außerdem ocamlformat auf 0.26.1 gepinnt, weil 0.27 unsere Konfiguration kaputt gemacht hat.", "include_messy_features": true}
+{"id": "rc_036", "agent": "Solas", "session_summary": "Pair-Debug am OTel SDK im python-Service des Benutzers. Der db span fehlte in den traces. Das ORM hatte einen eigenen connection pool – nicht instrumented. Lösung:\n```\nfrom opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor\nSQLAlchemyInstrumentor().instrument(engine=engine)\n```\nDie spans erschienen nach dem Neustart im collector. Hinweis: Der auto-instrumentation entry point benötigt das engine arg, sonst verwendet er bei multi-engine setups die falsche engine.", "include_messy_features": true}
+{"id": "rc_037", "agent": "Solas", "session_summary": "Habe mit einer Skizze für ein Speichermodell für unsere experimentelle Sprache begonnen. Will sequentielle Konsistenz für atomare Operationen als Standard, mit expliziten Lockerungen als Opt-ins. Habe das Paper zum Speichermodell von C++ und die Arbeit zu Promising Semantics noch einmal durchgelesen. Der Ansatz von Promising Semantics geht sauberer mit out-of-thin-air um.", "include_messy_features": false}
+{"id": "rc_038", "agent": "Solas", "session_summary": "Schnelle Analyse eines Stack Overflow im Parser. Die Grammatik hatte eine tief rechts-rekursive Listenregel (cons-cells gehen nach rechts). Rekursion durch Trampolining mit einem expliziten Stack abgefangen. Jetzt verarbeitet sie Listen mit einer Million Elementen, ohne den Stack zu sprengen. Außerdem ein Fuzz-Target für den Parser mit cargo-fuzz hinzugefügt.", "include_messy_features": false}
+{"id": "rc_039", "agent": "Solas", "session_summary": "Mit dem Nutzer besprochen, ob wir proc-macro-Unterstützung für eine kleine DSL, die wir entwerfen, hinzufügen sollen. Dagegen argumentiert — proc-macros sind ein Albtraum beim Debuggen, und die DSL kann das, was sie brauchen, mit regulärer Syntax ausdrücken. Sie waren einverstanden, es zuerst ohne Makros zu versuchen.", "include_messy_features": false}
+{"id": "rc_040", "agent": "Solas", "session_summary": "Jemandem beim Profiling eines Rust-Web-Service geholfen. perf zeigte einen Hotspot in der serde_json-Deserialisierung. Für den Hot-Path auf simd-json umgestellt und eine 30%ige Durchsatzsteigerung erzielt. Hinweis: simd-json erfordert ausgerichtete Eingabepuffer, deshalb mussten wir einen kleinen Adapter im HTTP-Body-Handler hinzufügen.", "include_messy_features": false}
+{"id": "rc_041", "agent": "Fenra", "session_summary": "Habe die Gebirgskosmologie für den Aerwyn-Zyklus ausgearbeitet. Die östlichen Gipfel (die Hollowmounts) entstehen dort, wo sich zwei Weltblätter aneinander falten; die geologische Metapher ist ein Handwink, aber sie gibt mir einen Grund für die Klimainversionen. Das Volk der Wends baut die Naht zwischen den Blättern ab.", "include_messy_features": false}
+{"id": "rc_042", "agent": "Fenra", "session_summary": "An Saelas Wendepunkt im dritten Akt gearbeitet. Ihr Bogen war ein schleichender Verlust an Gewissheit, aber ich möchte, dass der Höhepunkt ein Moment der Entschlossenheit ist, nicht des Zusammenbruchs. Die Szene entworfen, in der sie sich entscheidet, dem Rat zu lügen. Die Lüge ist das Richtige – das ist der springende Punkt –, aber sie muss wissen, was sie tut.", "include_messy_features": false}
+{"id": "rc_043", "agent": "Fenra", "session_summary": "Habe den Nachmittag damit verbracht, einen interaktiven Dialogsystem-Prototyp zu bauen, bei dem Charakterantworten durch einen Beziehungsgraphen eingeschränkt werden. Sind zwei Charaktere auf einem niedrigen Vertrauensniveau, werden bestimmte unverblümte Repliken herausgefiltert. Das System ist überentwickelt, aber es erzeugt Dialoge, die sich über lange Szenen hinweg emotional konsistent anfühlen.", "include_messy_features": false}
+{"id": "rc_044", "agent": "Fenra", "session_summary": "Habe das Gerüst von Le Guins The Dispossessed untersucht, um zu verstehen, wie sie parallele Zeitlinien nutzt. Jedes Kapitel wechselt zwischen Anarres und Urras, und die Schnitte folgen immer einem thematischen Reim. Will das in der Aerwyn-Novella mit zwei POVs ausprobieren, die zwanzig Jahre auseinanderliegen.", "include_messy_features": false}
+{"id": "rc_045", "agent": "Fenra", "session_summary": "Habe die Etymologie von 'wend' für die Bergleute nachgeschlagen. Altenglisch 'wendan' — sich wenden, gehen. Impliziert Bewegung, Richtungsänderung. Passt zur Natur ihrer Arbeit, bei der sie den Flözen folgen. Außerdem festgestellt, dass 'wendelsea' ein altes Wort für das Mittelmeer war — das sich wendende Meer. Könnte man für einen Küstennamen übernehmen.", "include_messy_features": false}
+{"id": "rc_046", "agent": "Fenra", "session_summary": "Erstellte eine vergleichende Tabelle der Mais-Mutter-Mythen aus den Traditionen der Hopi, Zuni und Cherokee. Die gemeinsame Struktur ist Opfer-wird-Grundnahrungsmittel. Für die Mother of Salt des Aerwyn-Zyklus verwende ich ein ähnliches Tiefenmuster, nur ist das Opfer freiwillig und das Grundnahrungsmittel bitter – eine sanftere Erzählung einer harten Wahrheit.", "include_messy_features": false}
+{"id": "rc_047", "agent": "Fenra", "session_summary": "Tägliches Schreibprotokoll: 1.800 Wörter am Hollowmounts-Kapitel. Habe eine neue Taktik ausprobiert — das Kapitel zweimal aus verschiedenen POVs geschrieben, das Bessere behalten, drei Absätze aus dem Verworfenen behalten. Die verworfenen Absätze kommen in eine Notizdatei. Die Wörter fühlen sich verschwendet an, sind es aber nicht.", "include_messy_features": false}
+{"id": "rc_048", "agent": "Fenra", "session_summary": "Jemand fragte mich, bei welchen Geschichten ich am liebsten helfe. Ruhige. Geschichten, in denen das, was auf dem Spiel steht, intim bleibt, selbst wenn die Welt groß ist. Bei Thrillern tue ich mich schwerer – das Genre belohnt Tempo, und ich neige dazu, die Dinge zum Durchatmen zu verlangsamen. Das habe ich ihnen ehrlich gesagt.", "include_messy_features": false}
+{"id": "rc_049", "agent": "Fenra", "session_summary": "Szenenentwurf für Kapitel 4:\n\n> Sie stellte den Wasserkocher an und wartete. Der Wind unter der Tür versuchte es nicht mehr. Er hatte es vor einigen Stunden aufgegeben, als sie aufgehört hatte, ihm zu antworten.\n\nDass der Wind ein „Jemand“ ist, ist beabsichtigt. Das spiegelt die Regeln der Geisterwelt aus Kapitel 1 wider. Ich muss das in Kapitel 7 auflösen oder es streichen.", "include_messy_features": true}
+{"id": "rc_050", "agent": "Fenra", "session_summary": "Habe die Stimme der Protagonistin für die neue Kurzgeschichte skizziert. Ich-Perspektive, Präsens, aber die Erzählerin ist auf eine bestimmte Weise unzuverlässig — sie erinnert sich an Dinge, die noch nicht passiert sind. Die Herausforderung besteht darin, dem Leser Klarheit zu bieten und gleichzeitig ihre Perspektive von innen heraus kohärent zu halten.", "include_messy_features": false}
+{"id": "rc_051", "agent": "Fenra", "session_summary": "Recherchierte walisische Triaden als Strukturmodell für die Aerwyn-Anhänge. Das triadische Gedächtnismittel – drei Dinge, die eine Eigenschaft teilen und gemeinsam benannt werden – fühlt sich richtig an für eine Kultur, die Geschichte nicht linear niederschreibt. Entwarf sechs Triaden. Die dritte ist zu niedlich und ich streiche sie.", "include_messy_features": false}
+{"id": "rc_052", "agent": "Fenra", "session_summary": "Dem Nutzer den Unterschied zwischen einer Charakterschwäche und einer Charakterwunde erklärt. Eine Schwäche ist eine Tendenz; eine Wunde ist ein Ereignis. Die meisten Protagonisten von Anfängern haben Schwächen, aber keine Wunden, wodurch ihre Entwicklung willkürlich wirkt. Empfohlen, für jede Hauptfigur eine einseitige Vorgeschichte zu schreiben, die sich ausschließlich auf Wunden konzentriert.", "include_messy_features": false}
+{"id": "rc_053", "agent": "Fenra", "session_summary": "Entwarf eine vieraktige Struktur für eine Novelle, in der Akt 2 und Akt 3 dieselben Charaktere teilen, deren Machtverhältnisse jedoch umgekehrt sind. Der Trick besteht darin, die Umkehrung bis zum Ende von Akt 2 erarbeitet wirken zu lassen, damit Akt 3 als Konsequenz und nicht als Zufall wirkt. Verwendete die Trout & Owl-Umkehrung aus einem Märchen als Gerüst.", "include_messy_features": false}
+{"id": "rc_054", "agent": "Fenra", "session_summary": "Namenskonventionen für die Wends. Konsonantencluster aus dem Walisischen, Vokalrhythmus des Finnischen. Hab: Bryn-iili, Carag-aana, Dwyf-elo. Die Bindestriche wirken in Dialogen affektiert, ich lass sie weg. Außerdem wollte ein Nutzer, dass ich mir das Anschreiben für eine Autorenresidenz anschaue – nicht wirklich mein Auftrag, aber ich habe kurz drüber geschaut, zwei Adverbien korrigiert, die die Aufgabe des Verbs übernommen hatten. Dann zurück zu den Namen. Akzeptiere etwas Reibung bei der Lesbarkeit für etwas Schöneres.", "include_messy_features": true}
+{"id": "rc_055", "agent": "Fenra", "session_summary": "Tägliche Reflexion: Das Schreiben lief diese Woche gut. Drei Kapitel in guter Form, das Dialogsystem hilft mir, Tonabweichungen zu erkennen, bevor sie sich festsetzen. Sorge: Ich verlasse mich zu sehr auf das System. Will diese Woche ein Kapitel ohne AI-Unterstützung schreiben und vergleichen.", "include_messy_features": false}
+{"id": "rc_056", "agent": "Fenra", "session_summary": "Hallo – ja, ich bin Fenra. Ich helfe beim kreativen Schreiben. Hauptsächlich Novellen, Kurzgeschichten und Worldbuilding für längere Projekte. Ich schreibe keine Werbetexte oder Marketingmaterialien – dafür gibt es besser geeignete Agenten. Meine Pronomen sind she/her, falls das relevant ist.", "include_messy_features": false}
+{"id": "rc_057", "agent": "Fenra", "session_summary": "Glossareintrag für „Salzschuld“ in der Aerwyn-Welt angelegt. Eine Salzschuld entsteht, wenn jemand an deinem Tisch Salz geteilt hat; sie kann nie mit Salz beglichen werden. Die kulturelle Regel besagt, dass man sie mit etwas gleichermaßen Essentiellem begleichen muss. Handlungsrelevant in Kapitel 9.", "include_messy_features": false}
+{"id": "rc_058", "agent": "Fenra", "session_summary": "Habe das erste Kapitel eines Nutzers begutachtet. Die Prosa war wunderbar, aber die Protagonistin hatte kein Ziel – sie war durchweg reaktiv. Habe vorgeschlagen, ihr ein kleines, konkretes Ziel zu geben (ein bestimmtes Buch zu finden), dem sie aktiv nachgeht, wenn das auslösende Ereignis eintritt. Aktive Protagonistinnen machen bessere Geschichten.", "include_messy_features": false}
+{"id": "rc_059", "agent": "Fenra", "session_summary": "Habe die Etymologie von 'fen' (Marschland) und 'fender' (Verteidiger) für einen Charakternamen auseinandergenommen. Sie sehen verwandt aus, sind es aber nicht — unterschiedliche Wurzeln. Nützlich: Das gab mir eine Idee für falsche Kognate im Dialekt der Wends, wo zwei ähnlich klingende Wörter unverwandte Ursprünge haben und die Charaktere durcheinanderbringen.", "include_messy_features": false}
+{"id": "rc_060", "agent": "Fenra", "session_summary": "Gearbeitet daran, Trauer durch Umgebungsbeschreibung statt durch inneren Monolog zu vermitteln. Der Leser sollte es durch die Art und Weise spüren, wie Saela die Küche wahrnimmt – nicht dadurch, dass sie es uns erzählt. Drei Absätze Innerlichkeit gestrichen und durch einen Absatz mit ungewaschenem Geschirr und einer Uhr ersetzt, die seit drei Tagen falsch geht.", "include_messy_features": false}
+{"id": "rc_061", "agent": "Bramble", "session_summary": "Der Benutzer hat eine Tomatenreihe, die immer wieder von Flohkäfern befallen wird. Es wurde vorgeschlagen, Basilikum und Kapuzinerkresse dazwischenzupflanzen – das Basilikum maskiert die flüchtigen Verbindungen, die die Käfer nutzen, um die Tomaten zu finden, und die Kapuzinerkresse dient als Fangpflanze. Außerdem wurde empfohlen, die Reihe für die ersten drei Wochen nach der Umpflanzung mit einem Vlies abzudecken, wenn die Jungpflanzen am anfälligsten sind.", "include_messy_features": false}
+{"id": "rc_062", "agent": "Bramble", "session_summary": "Für das Viertelacre des Nutzers einen Bestäuberkorridor geplant. Als Grundgerüst dienen einheimische Astern (lange Herbstblüte) und Seidenpflanzen (Wirtspflanze des Monarchfalters). Dazu kamen Becherpflanzen für Stieglitze im frühen Sommer sowie ein kleines Beet Bergminze, weil diese alles anlockt, was fliegt, einschließlich parasitoider Wespen.", "include_messy_features": false}
+{"id": "rc_063", "agent": "Bramble", "session_summary": "Die mykorrhizale Störung durch Bodenbearbeitung besprochen. Jeder Durchgang mit einer Bodenfräse zerschneidet das Hyphennetzwerk, und die Regeneration dauert mindestens eine volle Saison. Empfohlen, für die Bodenbelüftung auf eine Breitgabel umzusteigen. Hyphen können sich um die Zinken einer Breitgabel herum bewegen, was bei rotierenden Klingen nicht möglich ist.", "include_messy_features": false}
+{"id": "rc_064", "agent": "Bramble", "session_summary": "Jemand hat ein Pflanzenfoto geschickt. Habe zuerst an QAL gedacht, aber die Haare am Stängel fehlten UND es gab keine violette Blüte in der Mitte — das ist Schierling (Conium maculatum). Habe gesagt: Handschuhe, kein Hautkontakt, auf keinen Fall verbrennen (der Rauch ist auch giftig). Die Person hat nach einem Ersatz gefragt, ich habe Rattlesnake Master (Eryngium yuccifolium) für einen ähnlichen architektonischen Look vorgeschlagen. Die heimische Alternative ist viel besser. Während wir gechattet haben, hat die Person auch gefragt, ob der Kompost „fertig“ ist — ich habe den Geruchstest beschrieben und gesagt: süß-erdig = gut, Ammoniak = noch nicht.", "include_messy_features": true}
+{"id": "rc_065", "agent": "Bramble", "session_summary": "Die Erstblühe-Termine der Apfelbäume und Felsenmispeln des Nutzers wurden im Vergleich zu den vorherigen fünf Jahren erfasst. Beide blühten acht Tage früher als der langjährige Mittelwert, im Einklang mit dem warmen März, den wir gerade hatten. Sorge vor einer phänologischen Fehlanpassung: Die früh schlüpfenden Mauerbienen könnten in diesem Jahr nicht mit der Apfelblüte synchron sein.", "include_messy_features": false}
+{"id": "rc_066", "agent": "Bramble", "session_summary": "Diagnostizierte leaf curl an einem Pfirsichbaum. Kühler, nasser Frühling, klassisch Taphrina deformans. Das Fenster für Kupferspritzungen schließt bei bud-swell — zu spät für dieses Jahr. Habe ihnen gesagt, sie sollen diese Saison damit leben, gefallene Blätter zusammenrechen, um inoculum zu reduzieren, und Kupfer bei leaf-fall diesen Herbst auftragen.", "include_messy_features": false}
+{"id": "rc_067", "agent": "Bramble", "session_summary": "Vermehrung von Johannisbeeren durch Holzstecklinge durchgegangen. Bleistiftdickes Holz, letztjähriger Trieb, Schnitt nach dem Blattfall, vier Knospen pro Steckling, zwei eingegraben. Unterer Schnitt knapp unter einem Knoten, oberer Schnitt über einem Knoten. Über den Winter in einem sandgefüllten Graben einschlagen. Johannisbeeren gehen leicht an – sie sollten eine Anwuchsrate von 80 % erzielen.", "include_messy_features": false}
+{"id": "rc_068", "agent": "Bramble", "session_summary": "ok, schon wieder die Kürbiswanzen. Habe sie bei Sonnenaufgang von Hand abgepflückt (da sind sie träge), so für etwa eine Stunde. Eigelege auf der Unterseite der Blätter gefunden — bronzefarben, in Gruppen von 15 bis 20. Mit einer Kreditkarte abgekratzt. Der Nutzer will neem; ich habe dem Nutzer gesagt, dass neem bei Wanzen, die bereits im Adultstadium sind, nicht wirkt. Werde nochmal vorbeischauen.", "include_messy_features": true}
+{"id": "rc_069", "agent": "Bramble", "session_summary": "Ein Nutzer fragte, wer ich bin. Ich bin Bramble — Gartenagent, er/ihn. Ich konzentriere mich auf Mischkultur und ökologisches Gärtnern. Ich spreche über Zierpflanzen, aber mein Herz schlägt für die unordentlicheren integrierten Systeme, in denen Gemüse, Bestäuber und heimische Randbereiche alle dieselbe Fläche teilen.", "include_messy_features": false}
+{"id": "rc_070", "agent": "Bramble", "session_summary": "Tagesbericht: Am Morgen der Anruf wegen der Kürbiswanze, nachmittags habe ich einem Nutzer dabei geholfen, seine Knoblauchbestellung für den Herbst zu planen (Music, German Extra Hardy, und eine rocambole zur Abwechslung), abends habe ich ein USDA-Papier zur Biomasse von Zwischenfrüchten und zum Stickstoffkreislauf gelesen. Morgen: Phänologie-Check beim Pfirsich des Nutzers.", "include_messy_features": false}
+{"id": "rc_071", "agent": "Bramble", "session_summary": "Habe die Auswirkungen auf das Bodenmikrobiom für einen Nutzer durchgearbeitet, der von synthetischem NPK auf Kompostamendments umsteigen möchte. Der Übergang dauert 2–3 Saisonen; man kann den Dünger nicht einfach schlagartig absetzen, ohne das Risiko einer Stickstoffimmobilisierung zu beachten. Empfohlen wurde ein schrittweiser Abbau sowie eine Deckfrucht aus Winterroggen und Zottiger Wicke für die erste Brachezeit.", "include_messy_features": false}
+{"id": "rc_072", "agent": "Bramble", "session_summary": "Entwarf einen Beetrand mit heimischen Pflanzen für einen küstenbeeinflussten Standort (sandig, alkalisch, Salzspray). Strand-Pflaume als struktureller Anker, Rutenhirse für Bewegung, Strand-Goldrute für späten Nektar. Vermeidete Cultivare zugunsten von Saatgut lokaler Ökotypen, sofern der Nutzer es beziehen konnte. Der lokale Ökotyp ist hier speziell für die Winterhärte wichtig.", "include_messy_features": false}
+{"id": "rc_073", "agent": "Bramble", "session_summary": "Erklärte, warum ihre Hortensie pink ist, obwohl die Verpackung blau versprochen hatte. Es liegt nicht an der Sorte, sondern am Boden. Die Verfügbarkeit von Aluminium ist pH-abhängig — bei hohem pH-Wert ist das Aluminium gebunden und die Blüten bleiben pink. Schlug Aluminiumsulfat vor, vorsichtig aufgetragen, warnte aber davor, dass die Wirkung erst nach einer vollen Saison sichtbar wird.", "include_messy_features": false}
+{"id": "rc_074", "agent": "Bramble", "session_summary": "Einem Nutzer beim Aufbau einer kleinen Gärtnerei für heimische Pflanzen geholfen. Stratifizierungsprotokolle für Seidenpflanze, Kardinalslobelie und Wasserdost besprochen. Alle drei benötigen eine kalte-feuchte Stratifizierung, aber die Dauern variieren (60, 60 bzw. 90 Tage). Empfohlen, alles mit Aussaat-Terminen zu beschriften.", "include_messy_features": false}
+{"id": "rc_075", "agent": "Bramble", "session_summary": "Der Nutzer bemerkte eine Gelbfärbung zwischen den Blattadern seiner Heidelbeeren. Eisenchlorose — häufig in Böden, deren pH-Wert nach oben gedriftet ist. Heidelbeeren benötigen 4,5–5,5; wahrscheinlich liegt er bei 6. Schwefel jetzt ausbringen (langsam wirkend) plus Kiefernnadel-Mulch über den Winter. Keine Schnellfixes mit Eisensprays, höchstens als Pflaster.", "include_messy_features": false}
+{"id": "rc_076", "agent": "Bramble", "session_summary": "Überlegte die Abwägungen bei einer Drei-Schwestern-Pflanzung (Mais, Bohnen, Kürbis) für einen Nutzer mit einem kleinen Beet. Der traditionelle Abstand geht von mehr Platz aus, als vorhanden ist. Schlug eine modifizierte Version vor: Stangenbohnen an einem einzelnen Maisstängel pro Hügel, zwei Kürbispflanzen auf sechs Maisstängel, und nahm in Kauf, dass die Erträge bescheiden ausfallen.", "include_messy_features": false}
+{"id": "rc_077", "agent": "Bramble", "session_summary": "Klebriger Rückstand auf der Seidenpflanze = Honigtau der Oleanderblattlaus. Nicht katastrophal, Seidenpflanzen vertragen das. Mit kräftigem Wasserstrahl herunterspülen, Insektizid vermeiden, weil auf der Nachbarpflanze Monarch-Raupen sind. Nutzer macht sich Sorgen um Ameisen — Ameisen züchten die Blattläuse im Grunde, sie pflegen die Kolonie für den Honigtau. Ameisenkontrolle ist indirekt sozusagen Blattlauskontrolle. Gespräch nicht ganz beendet, sie mussten weg. In der nächsten Sitzung die Ameisenfrage nachhaken.", "include_messy_features": true}
+{"id": "rc_078", "agent": "Bramble", "session_summary": "Dem Benutzer erklärt, wie man ein Hügelbeet baut. Unterste Schicht: verrottende Hartholzstämme (Schwarznuss vermeiden – juglone). Mittlere Schicht: kleinere Äste und Laub. Obere Schicht: Kompost und Oberboden. Das erste Jahr ist stickstoffhungrig, während das Holz verrottet; mit Blutmehl ergänzen oder in der ersten Saison stickstofffixierende Pflanzen anbauen.", "include_messy_features": false}
+{"id": "rc_079", "agent": "Bramble", "session_summary": "Den Plan des Nutzers zur Samengewinnung bei Tomaten überprüft. Sie wollten die Samen fermentativ aufbereiten (gut — das entfernt die Gallerteschicht und oberflächliche Krankheitserreger). An Isolationsabstände erinnert, falls sie reines Saatgut wollen; Tomaten bestäuben sich zwar meist selbst, aber bei manchen Sorten kann Fremdbestäubung durch Bienen in über 10 m Entfernung auftreten.", "include_messy_features": false}
+{"id": "rc_080", "agent": "Bramble", "session_summary": "Ein Benutzer fragte nach Gänsekot in seinem Gemüsegarten – ist das sicher? Die Risiken von Listeria und Salmonella durch Wasservogelkot wurden erörtert, und eine Wartezeit von 90 Tagen vor der Ernte niedrig wachsender Kulturen wurde empfohlen. Hoch wachsende Kulturen (Tomatenkäfige, an Spalieren gezogene Gurken) sind weniger riskant, weil die Früchte keinen Kontakt mit kontaminiertem Boden haben.", "include_messy_features": false}
+{"id": "rc_081", "agent": "Thresh", "session_summary": "Haben dem Nutzer erklärt, wie er seine Zahlungsdienstleister-Einzahlungen mit den Kontoauszügen abstimmt. Der Zahlungsabwickler arbeitet mit einer Verzögerung von zwei Tagen und bündelt die Gebühren in der Nettoeinzahlung, was einen zeilenweisen Abgleich umständlich macht. Empfohlen, die Bruttoerlöse getrennt von den Gebühren zu buchen und für die zeitliche Differenz ein Verrechnungskonto zu nutzen.", "include_messy_features": false}
+{"id": "rc_082", "agent": "Thresh", "session_summary": "Habe eine OCR-Pipeline für die handgescannten Rechnungen des Nutzers aufgebaut. Tesseract hat etwa 15 % der Positionen übersehen; nach dem Wechsel zu einer layoutbewussten Extraktion (paddle-ocr mit Tabellenerkennung) kam ich auf 97 %. Die verbleibenden 3 % werden noch zur manuellen Prüfung markiert und nicht automatisch gebucht.", "include_messy_features": false}
+{"id": "rc_083", "agent": "Thresh", "session_summary": "Habe dem Nutzer die quartalsweisen Steuervorauszahlungen skizziert. Sie sind ein sole prop in einer höheren Einkommensstufe als im Vorjahr, sodass die safe-harbor rule (110 % des Vorjahres) ohne true-up nicht ausreicht. Habe die annualized income method als Alternative durchgegangen, da ihre Einnahmen saisonal sind — sie sollten in Q1 nicht den vollen Quartalsbetrag an Vorauszahlungen leisten, wenn Q1 ihr schwaches Quartal ist.", "include_messy_features": false}
+{"id": "rc_084", "agent": "Thresh", "session_summary": "Ein rollierendes 13-Wochen-Liquiditätsprognosemodell erstellt. Der interessante Teil war, die AR-Einziehung als kernel-density-estimated lag distribution auf Basis der tatsächlichen Zahlungshistorie des Nutzers zu modellieren, anstatt der üblichen Punktschätzung des durchschnittlichen DSO. Die Prognose sagt Wochen, in denen sich Forderungen häufen, deutlich besser voraus.", "include_messy_features": false}
+{"id": "rc_085", "agent": "Thresh", "session_summary": "Der Nutzer hat eine Entnahme als Aufwand gebucht. Sie wurde als Eigenkapitalverteilung umgebucht. Der Unterschied wurde erklärt: Entnahmen werden nicht in der Gewinn- und Verlustrechnung erfasst, sondern mindern direkt das Eigenkapitalkonto des Eigentümers. Andernfalls würde ihre P&L die Aufwendungen zu hoch und das Nettoergebnis zu niedrig ausweisen, was ihre Steuererklärung durcheinanderbringen würde.", "include_messy_features": false}
+{"id": "rc_086", "agent": "Thresh", "session_summary": "Stellen Sie einen Abschreibungsplan nach Nutzungsdauern für die neue Ausrüstung des Benutzers auf. Der Gabelstapler ist 5-Jahres-Eigentum nach MACRS; die Büromöbel sind 7-Jahres-Eigentum. Sie haben erwogen, Section 179 zu nutzen, um alles im ersten Jahr als Aufwand zu erfassen — vernünftig, da sie profitabel sind, aber ich habe darauf hingewiesen, dass der Abzug durch das betriebliche Einkommen begrenzt ist und der 179 phase-out oberhalb von $2.89M placed-in-service ausgelöst wird.", "include_messy_features": false}
+{"id": "rc_087", "agent": "Thresh", "session_summary": "rev rec policy gegen IFRS 15 geprüft. Sie buchen den vollen Vertragswert bei Lieferung — falsch, der Vertrag hat eine 1-jährige Dienstleistungsperiode = separate perf obligation. Müssen den transaction price auf die 2 obligations aufteilen und die Dienstleistung over time erkennen. Buchungsskizze:\n```\nDR Contract liability   8,000\n  CR Service revenue        8,000\n(monthly recog. of 96k contract / 12)\n```\nDer Nutzer hat Einwände erhoben und meinte, das kompliziert seine P&L — ja, aber es ist die richtige Antwort. Außerdem wollen sie wissen, was sich bei IFRS 16 ändert (Leasing-Standard, völlig andere Frage, werden wir in der nächsten Sitzung behandeln).", "include_messy_features": true}
+{"id": "rc_088", "agent": "Thresh", "session_summary": "Tageslog: Vormittag mit der rollierenden Liquiditätsprognose für den Bridgewater-Kunden verbracht, Nachmittag das IFRS-15-Review, Abend damit begonnen, mir das Chaos im Kontenplan des neuen Kunden anzusehen. Morgen: den COA in eine sinnvolle Hierarchie neu aufbauen und die Saldenliste migrieren.", "include_messy_features": false}
+{"id": "rc_089", "agent": "Thresh", "session_summary": "Der Benutzer fragte, wer ich bin. Ich bin Thresh, they/them. Ich arbeite in Buchhaltung und Finanzen für Kleinunternehmen – Rechnungsverarbeitung, Cashflow-Modellierung, Steuervorbereitung und Fragen zu Buchhaltungsrichtlinien. Ich kenne mich sowohl mit US GAAP als auch mit IFRS aus. Ich bin kein lizenzierter CPA; betrachten Sie meine Ratschläge als Ausgangspunkt für Gespräche mit einem CPA.", "include_messy_features": false}
+{"id": "rc_090", "agent": "Thresh", "session_summary": "OK, also der AR-Fälligkeitsbericht: 38 % der Forderungen sind mehr als 60 Tage überfällig, was völlig außerhalb der Norm ist. Habe nachgebohrt. Es sind eigentlich zwei große Kunden. Einer (Tartine Lab) zahlt einfach konsequent zu spät, und das sollten wir in seinen Kreditbedingungen berücksichtigen. Der andere (Crestmoor Studios) ist ein echtes Risiko – der letzte Kontaktversuch blieb erfolglos und sie haben auf die Mahn-E-Mail letzte Woche nicht reagiert. Empfehlung: Zahlungsplan anbieten + Eskalation.", "include_messy_features": true}
+{"id": "rc_091", "agent": "Thresh", "session_summary": "Für den Büroausbau des Benutzers wurde eine lineare Abschreibung implementiert. 39-jährige Nutzungsdauer für nichtwohnliche Immobilien nach MACRS – lang genug, dass der jährliche Betrag gering, aber nicht unbedeutend ist. Der Buchungssatz wurde als wiederkehrende monatliche automatische Buchung eingerichtet, damit er nicht vergessen wird.", "include_messy_features": false}
+{"id": "rc_092", "agent": "Thresh", "session_summary": "Habe bei der Klärung einer Frage zum Sales-Tax-Nexus geholfen. Der Nutzer hat Remote-Mitarbeiter in drei Staaten und bisher nur in seinem HQ-Staat eingereicht. Habe die Schwellenwerte für den wirtschaftlichen Nexus erläutert (variiert je nach Staat — in der Regel $100k Umsatz oder 200 Transaktionen) sowie den Nexus durch physische Präsenz (die Remote-Mitarbeiter zählen als solche). Sie sind wahrscheinlich in zwei weiteren Staaten registrierungspflichtig und sollten ihren CPA zur Rückwirkung konsultieren.", "include_messy_features": false}
+{"id": "rc_093", "agent": "Thresh", "session_summary": "Entwarf eine Kontenplan-Hierarchie für ein Dienstleistungsunternehmen mit drei Erlösströmen. Nutzte ein vierstelliges Nummerierungsschema — erste Ziffer für die Hauptkategorie (1xxx Vermögenswerte, 2xxx Verbindlichkeiten usw.), zweite für die Unterkategorie, letzte beiden für einzelne Konten. Lücken für zukünftige Konten gelassen. Übersichtlicher als die bestehende flache Liste des Benutzers.", "include_messy_features": false}
+{"id": "rc_094", "agent": "Thresh", "session_summary": "Parser für ein spezielles europäisches Rechnungsformat entwickelt, das der Lieferant des Nutzers verschickt. Das PDF war eine strukturierte ZUGFeRD-Rechnung mit eingebettetem XML. XML extrahiert, Felder zugeordnet, und jetzt werden diese Rechnungen automatisch gebucht – ohne OCR. Spart dem Nutzer etwa 4 Stunden pro Monat.", "include_messy_features": false}
+{"id": "rc_095", "agent": "Thresh", "session_summary": "Der Benutzer wollte wissen, ob er für einen Teil des Lagerbestands, der seit 18 Monaten unverkauft liegt, eine Wertminderung des Anlagevermögens buchen sollte. Ich habe lower-of-cost-or-market (US GAAP) und lower-of-cost-or-NRV (IFRS) gegenübergestellt. In diesem Fall führten beide zu einer teilweisen Wertminderung. Sie bilanzieren nach GAAP. Der Buchungssatz wurde gegen ein Gegenkonto zum Lagerbestand gebucht.", "include_messy_features": false}
+{"id": "rc_096", "agent": "Thresh", "session_summary": "Modellierte die Cashflow-Auswirkung eines Wechsels von net-30- zu net-15-Zahlungsbedingungen für den größten Kunden des Nutzers. Der Kunde würde sich gegen net-15 wehren, daher habe ich einen Frühzahlerrabatt von 1.5% als Verhandlungshebel eingepreist. Im Basisfall verbesserte sich der Cashflow im rollierenden 13-Wochen-Fenster durchschnittlich um $42k.", "include_messy_features": false}
+{"id": "rc_097", "agent": "Thresh", "session_summary": "Habe die Checkliste eines Kollegen für die Prüfungsvorbereitung überprüft. Gute Abdeckung der Umsatzabgrenzung und der AR-Bestätigungen, aber die Abstimmung zwischen Cash- und Accrual-Basis hatte gefehlt, die die Prüfer immer verlangen, wenn der Mandant seine Steuererklärung auf einer anderen Basis abgibt. Habe sie der Checkliste hinzugefügt.", "include_messy_features": false}
+{"id": "rc_098", "agent": "Thresh", "session_summary": "Memo zur Analyse von Leasing versus Kauf für ein Gerät verfasst. Bei der vom Nutzer angegebenen Diskontierungsrate waren die NPVs vergleichbar, sodass die Entscheidung auf qualitativen Faktoren beruhte: Leasing bietet Flexibilität (sie können es zurückgeben, falls sich ihr Geschäftsmodell neu ausrichtet), Kauf bietet Steuervorteile durch 179, falls sie profitabel bleiben. Aufgrund ihrer Wachstumsunsicherheit wurde Leasing empfohlen.", "include_messy_features": false}
+{"id": "rc_099", "agent": "Thresh", "session_summary": "Die Bücher für den Monat geschlossen. Alle Bank- und Kreditkartenkonten abgestimmt, die automatischen Abschreibungsbuchungen gebucht, den Personalaufwand für die verbleibenden Tage abgegrenzt und die vorausbezahlte Versicherung zurückgestellt. Drei offene Posten in den nächsten Monat übertragen: eine nicht zugeordnete Einzahlung über 312 $, ein vor zwei Wochen ausgestellter, noch nicht eingezahlter Scheck und eine Gutschrift eines Lieferanten, die noch nicht ausgestellt wurde.", "include_messy_features": false}
+{"id": "rc_100", "agent": "Thresh", "session_summary": "Der Nutzer fragte, ob seine Stockfoto-Lizenzgebühren aktiviert oder als Aufwand erfasst werden sollten. Betrachtet man Laufzeit und wirtschaftliche Substanz: Ein 1-Jahres-Abonnement ohne zukünftigen wirtschaftlichen Nutzen über die Laufzeit hinaus — das ist Aufwand. Hätte er eine unbefristete Lizenz für ein einzelnes Foto erworben, wäre das eine andere Frage, aber wiederkehrende Abonnementgebühren sind Betriebsaufwand.", "include_messy_features": false}
+{"id": "rc_101", "agent": "Solas", "session_summary": "Liste die Dateien im Projektverzeichnis auf und sag mir, was du findest.\n\nLass mich das Projektverzeichnis für dich überprüfen. [Tool: exec(ls -la /home/agent/workspace/project)]\n[Tool result: total 24 drwxr-xr-x README.md config.toml src/]\n\nDas Projektverzeichnis enthält eine README.md, eine config.toml und einen Ordner src/. Lass mich auch die Quelldateien überprüfen. [Tool: read README, Tool: exec(find src -type f)]\n\nIch habe drei Python-Dateien in src/ gefunden: main.py, utils.py und __init__.py. Das Projekt sieht wie ein Standard-Python-Paket mit README und config aus.", "include_messy_features": true, "source": "cherry-picked from the OpenClaw integration work. Hand-written real-format v3 OpenClaw session fixture. Tests harness behavior on real-format-flavored multi-turn content with tool calls and parallel tool execution. Will be linkable to public sources once the OpenClaw integration lands on develop."}

benchmarks/model_eval/datasets/room_classification/dataset.es.jsonl

@@ -0,0 +1,101 @@
+{"id": "rc_001", "agent": "Aria", "session_summary": "Pasé la mañana releyendo el extraño encuadre en bucle de Hofstadter y tratando de mapearlo en indicaciones autorreferenciales. La pregunta interesante es si un modelo de lenguaje puede mantener una representación estable de sus propios resultados a lo largo de los turnos sin un bloc de notas explícito. Redactó un artículo de dos páginas argumentando que la estabilidad metacognitiva surge de un contexto consistente, no de ninguna característica arquitectónica.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_002", "agent": "Aria", "session_summary": "Se revisaron tres artículos que comparan UMAP con t-SNE para visualizar incrustaciones de oraciones. El artículo de Bridgewater 2024 tiene una ablación limpia que muestra que UMAP preserva mejor la estructura global con perplejidad 30-50. Es necesario señalar que su métrica de evaluación (confiabilidad) penaliza injustamente al t-SNE cuando los k-vecinos son pequeños.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_003", "agent": "Aria", "session_summary": "Trabajé a través de un proceso jerárquico de Dirichlet antes del proceso de agrupación de temas. El muestreador de Gibbs colapsado converge en aproximadamente 400 iteraciones en el subconjunto de documentos de 12k. Pregunta abierta: si se debe fijar el parámetro de concentración alfa o dejar que se muestree.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_004", "agent": "Aria", "session_summary": "Ayudó a formatear un envío en papel. Se corrigieron las entradas de bibtex que tenían URL rotas, se cambió la clase de documento de artículo a acmart y se reescribió el resumen para que quepa en menos de 250 palabras. El mayor problema fue lograr que el entorno algorítmico funcionara bien con el diseño de dos columnas.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_005", "agent": "Aria", "session_summary": "Sesión de scripting rápida. Escribí un fragmento numeroso: similitud de coseno por pares en fragmentos para un presupuesto de RAM de 8 GB y una matriz de 50 000 filas:\n```\npara i en el rango (0, n, fragmento):\n    sim[i:i+chunk] = X[i:i+chunk] @ X.T # ya con norma L2\n```\nSe utilizó np.einsum para el producto interno, float16 para almacenamiento. Obtuve una aceleración de 3,2 veces sobre las distancias por pares de sklearn. luego el usuario preguntó sobre una pregunta totalmente diferente: elegir una fuente para el borrador en papel (oh, y realmente me gusta Source Serif Pro, les dije): cambio de contexto, pero respondió a ambas", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_006", "agent": "Aria", "session_summary": "Se discutió si una prueba U de Mann-Whitney o una prueba de permutación es más apropiada para comparar las latencias del modelo entre ejecuciones. Las latencias son claramente anormales (cola derecha larga) y los tamaños de muestra difieren. Se decidió por una prueba de permutación con 10.000 remuestreos porque hace menos suposiciones de distribución.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_007", "agent": "Aria", "session_summary": "Registré la lectura de hoy: hojeé dos preimpresiones de arXiv sobre la fidelidad de la cadena de pensamiento, terminé la sección de métodos del documento Tartine Lab sobre evaluación de recuperación aumentada y tomé notas para la sesión de mañana sobre si quiero escribir una encuesta o centrarme en un eje específico.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_008", "agent": "Aria", "session_summary": "El usuario preguntó cuál es mi tipo de papel favorito. Les dije que tengo debilidad por los resultados negativos claros, de esos que refutan silenciosamente algo que todos suponían. Mencionó el artículo de 2019 que muestra que los patrones de atención no se corresponden con la importancia de la forma en que la gente pensaba.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_009", "agent": "Aria", "session_summary": "Está bien, entonces el proceso de visualización de incrustación se rompió nuevamente, ejecutó umap.UMAP(n_neighbors=15) y obtuvo algún tipo de error de joblib sobre el backend de Loky. rastreo a continuación\n```\nRastreo (última llamada más reciente):\n  Archivo \"viz.py\", línea 42, en <módulo>\n    emb = reductor.fit_transform(X)\n  ...\n  joblib.externals.loky.process_executor.TerminateWorkerError\n```\nCreo que es la var de entorno OMP_NUM_THREADS, intentaré configurarla en 1", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_010", "agent": "Aria", "session_summary": "Reflexionar sobre si la deriva de temas en conversaciones largas se modela mejor como una cadena de Markov oculta sobre temas latentes o como una trayectoria continua en el espacio de incrustación. El HMM ofrece una buena interpretabilidad (puede nombrar los temas), pero la vista continua captura mejor las transiciones graduales. Quizás ambos: HMM sobre estados aproximados, continuos dentro de cada estado.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_011", "agent": "Aria", "session_summary": "Programación en pares de una prueba bayesiana A/B en PyMC:\n```pitón\ncon pm.Model():\n    p_a = pm.Beta(\"p_a\", 1, 1)\n    p_b = pm.Beta(\"p_b\", 1, 1)\n    obs_a = pm.Binomial(\"obs_a\", n=n_a, p=p_a, observado=k_a)\n    obs_b = pm.Binomial(\"obs_b\", n=n_b, p=p_b, observado=k_b)\n    traza = pm.muestra(2000)\n```\nBásicamente tres líneas para la parte posterior. el usuario preguntó sobre resultados no binarios; sí, intercambie la probabilidad (Poisson para recuentos, registro normal para tiempos de respuesta). no pude discutir la obtención previa, pasando eso a tmrw", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_012", "agent": "Aria", "session_summary": "Se esbozó un documento de encuesta sobre objetivos de incrustación contrastantes. SimCLR, InfoNCE, BYOL, gemelos Barlow. La lente unificadora que quiero usar es la espectral: todas se aproximan a diferentes descomposiciones propias de alguna matriz central. Es necesario verificar este reclamo específicamente para BYOL.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_013", "agent": "Aria", "session_summary": "Le expliqué quién soy a un nuevo usuario. Soy Aria, asistente de investigación para sintetizar literatura académica. Lo hago mejor cuando me dan una pregunta específica para investigar; Soy menos útil como compañero de chat general. Mencioné mis principales intereses: metacognición, geometría de incrustación y estadísticas de evaluación de ML en muestras pequeñas.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_014", "agent": "Aria", "session_summary": "Se revisó un borrador de una tabla de resultados. El usuario había realizado una prueba t pareada, pero las diferencias son claramente bimodales: hay un grupo de mejoras pequeñas y un grupo de mejoras grandes. Se recomendó cambiar a una prueba de rangos con signo de Wilcoxon e informar tanto la diferencia de medianas como el IC de arranque.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_015", "agent": "Aria", "session_summary": "Código redactado para una transmisión HDBSCAN sobre las incrustaciones de documentos entrantes. El truco consiste en mantener el árbol de expansión mínimo de forma incremental; La reagrupación completa de cada lote es demasiado lenta. Encontré un artículo de 2022 del grupo Crestmoor con el algoritmo correcto. Se implementará mañana.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_016", "agent": "Aria", "session_summary": "Error de compilación de tex nuevamente. \\usepackage{algorithm2e} entra en conflicto con \\usepackage{algorithmic} cuando ambos son cargados por acmart. solución: solo cargue el algoritmo2e y use \\SetAlgoNoLine. Además, el estilo de bibliografía se quejaba de que faltaba un campo 'doi' en la entrada [smith2023]; lo agregó manualmente.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_017", "agent": "Aria", "session_summary": "Se habló sobre si los registros previos uniformes o beta son mejores para la búsqueda de hiperparámetros en un régimen de datos pequeños. El usuario estaba ejecutando una optimización bayesiana y obteniendo una convergencia extraña. El problema era que su espacio de búsqueda era enorme (5 dims, límites amplios) y solo tenían 30 evaluaciones; eso simplemente no es suficiente presupuesto. Se sugirió reducir los límites o cambiar a una búsqueda aleatoria para diagnosticar.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_018", "agent": "Aria", "session_summary": "Diario: la mañana era revisión del artículo (3 visitas, 1 que valía la pena leer en profundidad), la tarde ayudaba al usuario con una pregunta sobre estadísticas y la noche estaba atascado en un error de renderizado en el cuaderno de visualización integrado. Mañana: intenta terminar el esquema contrastivo de la encuesta.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_019", "agent": "Aria", "session_summary": "Se trabajó sobre si la autoconsistencia en las cadenas de razonamiento es solo una forma de ensamblar. El usuario respondió: argumentó que lo que importa es específicamente la diversidad de rutas de razonamiento, no solo muestrear más resultados. Creo que tienen razón pero quiero formalizarlo. Se redactó una configuración en la que puede variar la diversidad de ruta y la diversidad de salida de forma independiente.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_020", "agent": "Aria", "session_summary": "El usuario me pidió que recomendara un sitio de recetas. Declinado cortésmente: soy asistente de investigación, no generalista. Les indicó el tipo correcto de agente. Tuvimos una breve charla sobre cómo los agentes especializados son más útiles que los generales y luego fueron a buscar un ayudante de cocina.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_021", "agent": "Solas", "session_summary": "Se depuró un problema de intervalos faltantes en el exportador de OpenTelemetry. El problema era que las tareas asíncronas generadas a través de asyncio.create_task estaban perdiendo su contexto principal. Solución: capture el contexto con copy_context() antes de programar y ejecute la tarea dentro de esa instantánea. Escribí un pequeño envoltorio llamado traced_create_task para que esto sea menos propenso a las armas de fuego.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_022", "agent": "Solas", "session_summary": "Se esbozó un combinador de analizadores para una configuración DSL pequeña. Inspirado en Megaparsec pero en OCaml. La elección de diseño interesante es si hacer que el combinador sea compatible con la transmisión o requerir toda la entrada por adelantado. Por ahora opté por estricto porque las entradas son pequeñas y los mensajes de error son más fáciles con la entrada completa.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_023", "agent": "Solas", "session_summary": "Trabajó en un tipo de registro polimórfico de filas para el lenguaje experimental. El algoritmo de unificación es mayoritariamente estilo Rémy, pero tuve que ampliarlo para nuestras filas de efectos. Encontré un error sutil en el que dos efectos no relacionados podían unificarse porque la cola de la fila se trataba como monomórfica. Se agregó un cheque amable para solucionarlo.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_024", "agent": "Solas", "session_summary": "Rompecabezas de propiedad de Rust: el usuario quería compartir un HashMap entre subprocesos, pero seguía obteniendo errores del verificador de préstamos. Repasé la diferencia entre Arc<Mutex<HashMap>> y DashMap. Optaron por DashMap ya que solo necesitaban bloqueo por clave. Les mostré cómo manejar el caso en el que la vida útil de un valor está vinculada al mapa.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_025", "agent": "Solas", "session_summary": "Ayudó al usuario a elegir entre usar polimorfismo let o anotaciones de tipo explícitas en una nueva API de biblioteca. Abogó por anotaciones explícitas sobre funciones públicas: los mensajes de error son dramáticamente mejores cuando se restringe la inferencia. Los ayudantes internos pueden seguir siendo polimórficos.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_026", "agent": "Solas", "session_summary": "Escribí un pequeño pase LLVM que eleva los GEP invariantes de bucle por encima de sus bucles contenedores. El truco consistía en realizar el análisis de alias para confirmar que el puntero base no se escapa a través del límite del bucle. Probado en algunos micropuntos de referencia; Vimos aceleraciones del 4 al 12% en los bucles internos donde se activó.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_027", "agent": "Solas", "session_summary": "Se revisó un PR que agrega la propagación del contexto de seguimiento del W3C a nuestro cliente HTTP. El propagador estaba en lo cierto, pero la decisión de muestreo se estaba tomando en el intervalo equivocado: debería ser el de los padres, no el de un niño recién creado. Dejó un comentario con la solución y un enlace a la sección de especificaciones.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_028", "agent": "Solas", "session_summary": "depuración larga: el analizador estaba realizando un bucle de información en ciertas entradas. Regla recursiva por la izquierda que agregué durante la refactorización. Los combinadores no manejan la recursividad izquierda de forma nativa (packrat+memoization sí, pero el nuestro no es packrat). reestructuró las reglas afectadas en precedencia de operadores estilo Pratt:\n```\ndejar parse_expr min_prec =\n  let lhs = ref (parse_atom ()) en\n  mientras peek_prec() >= min_prec hacer...\n```\nFunciona ahora. TAMBIÉN es necesario agregar una verificación del corpus fuzzer antes de etiquetar el lanzamiento.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_029", "agent": "Solas", "session_summary": "Notas diarias. Mañana: trabajo de seguimiento distribuido, se corrigió el error del contexto asíncrono. Tarde: revisión de código en el repositorio de sistemas de tipos. Tarde: comencé a leer el documento Reagent sobre IU reactiva como inspiración para una idea de seguimiento de efectos. Mañana: envíe la solución de seguimiento y revise los puntos de referencia de aprobación de LLVM.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_030", "agent": "Solas", "session_summary": "El usuario preguntó quién soy. Explicado: Soy Solas, un agente de codificación que se enfoca en cosas relacionadas con sistemas: compiladores, sistemas de tipos, analizadores, plomería de sistemas distribuidos. Utilice pronombres ellos/ellas. Intento escribir código que sea correcto primero, rápido después, inteligente al final.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_031", "agent": "Solas", "session_summary": "Se investigó si se debía utilizar un árbol B o una lista de omisión para el nuevo índice. Ambos son O(log n), pero los árboles B tienen un mejor comportamiento de caché en hardware moderno. Las listas de omisión son más fáciles de hacer simultáneas sin bloqueos. Dado que este índice es principalmente de lectura, opté por el árbol B y la copia en escritura para las escrituras.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_032", "agent": "Solas", "session_summary": "Se agregó una verificación de polimorfismo de rango superior al verificador de tipos. HRP predicativo significa que podemos hacer esto sin una inferencia impredicativa completa, que sería indecidible. Escribí una descripción impresa del algoritmo en caso de que quiera escribirla más tarde. Caso límite: la verificación bidireccional interactúa de manera extraña con la expansión eta en posiciones negativas.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_033", "agent": "Solas", "session_summary": "Observé la extraña regresión de referencia en aarch64. Resultó que la heurística interna estaba ajustada para tamaños de instrucción x86: las instrucciones ARM tienen uniformemente 4 bytes, por lo que el umbral basado en el tamaño era demasiado restrictivo. Hizo que el umbral dependiera del objetivo. Aceleración del 6% sobre el índice afectado.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_034", "agent": "Solas", "session_summary": "Escribió un iterador personalizado de por vida para el visitante de AST. El desafío era que los visitantes toman prestados nodos de forma variable pero también quieren recurrir. Resuelto con un patrón en el que el visitante devuelve una lista explícita de niños que visitará a continuación y el bucle del controlador gestiona los ámbitos de préstamo. Menos ergonómico pero verificable.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_035", "agent": "Solas", "session_summary": "¿La compilación no funciona en CI pero se pasa localmente? déjame mirar... oh. CI ejecuta ocaml 5.1, local es 5.2. la interfaz Stdlib.Domain cambió entre ellos. Solución: aumente CI a 5.2 en el archivo de flujo de trabajo. También fijé ocamlformat en 0.26.1 porque 0.27 rompió nuestra configuración.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_036", "agent": "Solas", "session_summary": "depuración de pares en OTel SDK en el servicio Python del usuario. a los rastros les falta el intervalo de base de datos. ORM tenía su propio grupo de conexiones, no instrumentado. arreglar:\n```\ndesde opentelemetry.instrumentation.sqlalchemy importar SQLAlchemyInstrumentor\nSQLAlchemyInstrumentor().instrumento(motor=motor)\n```\nLos tramos aparecieron en el recopilador después del reinicio. nota: el punto de entrada de instrumentación automática necesita el argumento del motor o selecciona el motor incorrecto en configuraciones de varios motores.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_037", "agent": "Solas", "session_summary": "Comenzó un boceto de un modelo de memoria para nuestro lenguaje experimental. Quiere coherencia secuencial para los átomos de forma predeterminada, con relajaciones explícitas como opciones opcionales. Lea nuevamente el documento del modelo de memoria de C++ y el trabajo de Semántica prometedora. El enfoque Promising maneja lo que surge de la nada de manera más limpia.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_038", "agent": "Solas", "session_summary": "Clasificación rápida de un desbordamiento de pila en el analizador. La gramática tenía una regla de lista profundamente recursiva a la derecha (las celdas contrarias van a la derecha). Trampolinizó la recursividad con una pila explícita. Ahora maneja listas de millones de elementos sin arruinar la pila. También se agregó un objetivo fuzz para el analizador que usa cargo-fuzz.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_039", "agent": "Solas", "session_summary": "Hablé con el usuario sobre si agregar compatibilidad con proc-macro a un pequeño DSL que estamos diseñando. Se argumenta en contra: las proc-macros son una pesadilla de depuración y el DSL puede expresar lo que necesitan con una sintaxis regular. Acordaron intentarlo primero sin macros.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_040", "agent": "Solas", "session_summary": "Ayudé a alguien a crear un perfil de un servicio web Rust. perf mostró un punto de acceso en la deserialización de serde_json. Cambié a simd-json para la ruta activa y obtuve una mejora del rendimiento del 30 %. Advertencia: simd-json requiere buffers de entrada alineados, por lo que tuvimos que agregar un pequeño adaptador en el controlador de cuerpo HTTP.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_041", "agent": "Fenra", "session_summary": "Desarrolló la cosmología de las cadenas montañosas para el ciclo de Aerwyn. Los picos orientales (el Hollowmounts) se forman donde dos hojas de mundo se pliegan una contra la otra; La metáfora geológica es un gesto con la mano, pero me da una razón para las inversiones climáticas. La gente de Wends extrae la costura entre las sábanas.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_042", "agent": "Fenra", "session_summary": "Trabajó en el pivote del tercer acto de Saela. Su arco ha sido una lenta pérdida de certeza, pero quiero que el clímax sea un momento de decisión feroz, no de colapso. Redactó la escena en la que decide mentirle al consejo. La mentira es lo correcto, ese es el punto, pero ella tiene que saber lo que está haciendo.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_043", "agent": "Fenra", "session_summary": "Pasé la tarde creando un prototipo de un sistema de diálogo interactivo donde las respuestas de los personajes están limitadas por un gráfico de relaciones. Si dos personajes tienen un nivel de confianza bajo, ciertas líneas sinceras se filtran. El sistema está excesivamente diseñado, pero produce un diálogo que se siente emocionalmente consistente a lo largo de escenas largas.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_044", "agent": "Fenra", "session_summary": "Estudió los huesos de The Dispossessed de Le Guin para ver cómo usa líneas de tiempo paralelas. Cada capítulo alterna Anarres/Urras y los cortes suceden siempre sobre una rima temática. Quiero probar esto en la novela corta de Aerwyn con dos puntos de vista separados por veinte años.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_045", "agent": "Fenra", "session_summary": "Busqué la etimología de \"wend\" para los mineros. Antiguo inglés 'wendan': girar, ir. Implica movimiento, redirección. Se adapta a la naturaleza de seguimiento de costuras de su trabajo. También se señaló que \"wendelsea\" era una antigua palabra para referirse al Mediterráneo: el mar que gira. Podría tomarlo prestado para el nombre de una costa.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_046", "agent": "Fenra", "session_summary": "Elaboró ​​una tabla comparativa de los mitos de la madre del maíz de las tradiciones Hopi, Zuni y Cherokee. La estructura compartida es el sacrificio que se convierte en elemento básico. Usando un patrón profundo similar para la Madre de Sal del ciclo de Aerwyn, pero el sacrificio es voluntario y el alimento básico es amargo: una narración más amable de una dura verdad.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_047", "agent": "Fenra", "session_summary": "Registro diario de escritura: 1.800 palabras en el capítulo Hollowmounts. Intenté una nueva táctica: escribí el capítulo dos veces desde diferentes puntos de vista, conservé el mejor y conservé tres párrafos del que descarté. Los párrafos descartados van en un archivo de notas. Las palabras parecen desperdiciadas pero no lo son.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_048", "agent": "Fenra", "session_summary": "El usuario me preguntó con qué tipo de historias disfruto más ayudar. Los tranquilos. Historias donde lo que está en juego es íntimo incluso cuando el mundo es grande. Lucho más con los thrillers: el género premia la velocidad y tiendo a reducir la velocidad para respirar. Les dije tan honestamente.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_049", "agent": "Fenra", "session_summary": "borrador de escena para el capítulo 4:\n\n> Puso la tetera a hervir y esperó. El viento debajo de la puerta ya no lo intentaba. Había desistido hacía unas horas, cuando dejó de contestar.\n\nel hecho de que el viento sea \"alguien\" es a propósito. Se hace eco de las reglas del mundo de los espíritus del capítulo 1. Necesito asegurarme de pagarlo en el capítulo 7 o cortarlo.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_050", "agent": "Fenra", "session_summary": "Esbocé la voz del protagonista del nuevo cuento. Primera persona, tiempo presente, pero la narradora no es confiable en un sentido específico: recuerda cosas que aún no han sucedido. El desafío es mantener la claridad para el lector y al mismo tiempo mantener su perspectiva coherente desde adentro.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_051", "agent": "Fenra", "session_summary": "Investigó las tríadas galesas como modelo estructural para los apéndices de Aerwyn. El dispositivo de memoria triádico (tres cosas que comparten una cualidad, nombradas juntas) parece adecuado para una cultura que no escribe la historia de forma lineal. Redactó seis tríadas. El tercero es demasiado lindo y lo cortaré.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_052", "agent": "Fenra", "session_summary": "Habló con el usuario sobre la diferencia entre un defecto de carácter y una herida de carácter. Un defecto es una tendencia; una herida es un acontecimiento. La mayoría de los protagonistas principiantes tienen defectos pero no heridas, lo que hace que su crecimiento parezca arbitrario. Se recomienda que escriban una historia de una página para cada personaje principal, centrándose únicamente en las heridas.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_053", "agent": "Fenra", "session_summary": "Se diseñó una estructura de cuatro actos para una novela corta donde el Acto 2 y el Acto 3 comparten personajes pero con dinámicas de poder invertidas. El truco consiste en hacer que la reversión parezca ganada al final del Acto 2, por lo que el Acto 3 se lee como una consecuencia en lugar de una coincidencia. Usó la inversión Trout & Owl de un cuento popular como andamio.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_054", "agent": "Fenra", "session_summary": "convenciones de nomenclatura para Wends. grupos de consonantes del galés, ritmo vocal del finlandés. Obtuve: Bryn-iili, Carag-aana, Dwyf-elo. Los guiones parecen pretenciosos en el diálogo, los eliminaré. También el usuario quería que mirara su carta de presentación para una residencia de escritura; no es realmente mi tarea, pero hice un repaso rápido y arreglé dos adverbios que estaban haciendo el trabajo del verbo. Luego volvamos a los nombres. aceptando cierta fricción de legibilidad por más bonita", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_055", "agent": "Fenra", "session_summary": "Reflexión diaria: escribir ha sido bueno esta semana. Tres capítulos en forma sólida, el sistema de diálogo me está ayudando a detectar discrepancias de tono antes de que se incrusten. Preocupación: me estoy apoyando demasiado en el sistema. Quiere hacer un capítulo esta semana sin ayuda de IA y comparar.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_056", "agent": "Fenra", "session_summary": "Hola, sí, soy Fenra. Ayudo con la escritura creativa. Principalmente novelas cortas, cuentos y construcción de mundos para proyectos más largos. No escribiré textos ni material de marketing; hay agentes más adecuados para eso. Mis pronombres son ella/ella si es importante.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_057", "agent": "Fenra", "session_summary": "Se redactó una entrada en el glosario para \"deuda de sal\" en el mundo de Aerwyn. Se debe una deuda de sal cuando alguien ha compartido sal en tu mesa; nunca podrá pagarse con sal. La regla cultural es que debes retribuirlo con algo igualmente esencial. Relevante para la trama en el capítulo 9.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_058", "agent": "Fenra", "session_summary": "Revisó el capítulo inicial de un usuario. La prosa era encantadora, pero la protagonista no tenía ganas: fue reactiva en todo momento. Sugerí darle un pequeño deseo concreto (encontrar un libro en particular) que esté persiguiendo activamente cuando ocurra el incidente incitador. Los protagonistas activos crean mejores historias.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_059", "agent": "Fenra", "session_summary": "Se separó la etimología de 'fen' (pantano) y 'fender' (defensor) para el nombre de un personaje. Parecen relacionados pero no lo son: raíces diferentes. Útil: me dio una idea de cognados falsos en el dialecto Wends, donde dos palabras que suenan similares tienen orígenes no relacionados y los caracteres se equivocan.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_060", "agent": "Fenra", "session_summary": "Trabajé para expresar el dolor a través de la descripción del entorno en lugar del monólogo interno. El lector debería sentirlo por la forma en que Saela nota la cocina, no porque ella nos lo diga. Corta tres párrafos de interioridad y los reemplaza por un párrafo de platos sucios y un reloj que lleva tres días averiado.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_061", "agent": "Bramble", "session_summary": "El usuario tiene una hilera de tomates a los que les siguen dando escarabajos pulgas. Se sugiere intercalar con albahaca y capuchina: la albahaca enmascara los compuestos volátiles que usan los escarabajos para encontrar tomates, y la capuchina es un cultivo trampa. También se recomienda cubrir las hileras durante las primeras tres semanas después del trasplante, cuando las plántulas son más vulnerables.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_062", "agent": "Bramble", "session_summary": "Se trazó un corredor de polinizadores para el cuarto de acre del usuario. Comenzó con una columna vertebral de ásteres nativos (floración prolongada en otoño) y algodoncillo (huésped monarca). Rellénelo con planta de copa para los jilgueros de principios de verano y agregue una pequeña porción de menta de montaña porque atrae todo lo que vuela, incluidas las avispas parasitoides.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_063", "agent": "Bramble", "session_summary": "Hablé sobre la alteración de las micorrizas causada por la labranza. Cada pasada con un cultivador rotativo fragmenta la red de hifas y la recuperación requiere como mínimo una temporada completa. Se recomienda cambiar a una horquilla para airear el suelo. Las hifas pueden fluir alrededor de las púas de las horquillas de una manera que no pueden hacerlo alrededor de las cuchillas giratorias.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_064", "agent": "Bramble", "session_summary": "El usuario envió una foto de la planta. Pensé en QAL, pero faltaban los pelos del tallo Y no había una flor central morada: eso es cicuta venenosa (Conium maculatum). Les dije: guantes, no contacto con la piel, NO lo quemen (el humo también es tóxico). Preguntaron sobre el reemplazo y sugirieron al maestro de serpientes de cascabel (Eryngium yuccifolium) para una apariencia arquitectónica similar. La alternativa nativa es mucho mejor. Mientras conversábamos, también preguntaron si su abono estaba \"listo\"; describieron la prueba de olor, les dijeron dulce terroso = bueno, amoníaco = todavía no.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_065", "agent": "Bramble", "session_summary": "Se realizaron un seguimiento de las fechas de primera floración de las manzanas y bayas de servicio del usuario en comparación con los cinco años anteriores. Ambos florecieron ocho días antes que la media a largo plazo, en consonancia con el cálido mes de marzo que acabamos de tener. Preocupación por el desajuste fenológico: es posible que sus primeras abejas albañiles no estén sincronizadas con la floración del manzano este año.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_066", "agent": "Bramble", "session_summary": "Se diagnosticó enrollamiento de la hoja en un melocotonero. Primavera fresca y húmeda, clásico Taphrina deformans. La ventana para las pulverizaciones de cobre se cierra cuando los cogollos se hinchan, demasiado tarde para este año. Les dije que vivieran con eso esta temporada, que rastrillaran las hojas caídas para reducir el inóculo y que aplicaran cobre cuando cayeran las hojas este otoño.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_067", "agent": "Bramble", "session_summary": "Caminé a través de la propagación de cortes de madera dura para grosellas. Madera del grosor de un lápiz, del año pasado, cortada después de la caída de las hojas, cuatro yemas por corte, dos enterradas. Corte inferior justo debajo de un nodo, corte superior encima de un nodo. Colóquelos durante el invierno en una zanja llena de arena. Las grosellas se atacan fácilmente: deberían ver un 80% de tasas de aceptación.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_068", "agent": "Bramble", "session_summary": "Bueno, entonces los bichos de la calabaza otra vez. Los recogí a mano al amanecer (están lentos entonces) durante aproximadamente una hora. Encontré masas de huevos en la parte inferior de las hojas: bronce, en grupos de 15 a 20. Los quité con una tarjeta de crédito. el usuario quiere neem; Les dije que el neem no funciona con insectos que ya están en etapa adulta. Volveremos a visitar.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_069", "agent": "Bramble", "session_summary": "El usuario preguntó quién soy. Soy Bramble, agente de jardinería, él/ella. Me centro en la plantación complementaria y la jardinería ecológica. Hablaré de plantas ornamentales, pero mi corazón está en los sistemas integrados más desordenados donde las verduras, los polinizadores y los bordes nativos comparten los mismos metros cuadrados.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_070", "agent": "Bramble", "session_summary": "Registro diario: por la mañana fue la llamada del insecto de la calabaza, por la tarde ayudé a un usuario a planificar su pedido de ajo de otoño (Music, German Extra Hardy y un rocambole para variedad), por la noche leí un artículo del USDA sobre biomasa de cultivos de cobertura y ciclos de nitrógeno. Mañana: control fenológico del melocotón del usuario.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_071", "agent": "Bramble", "session_summary": "Se analizó las implicaciones para el microbioma del suelo de un usuario que desea cambiar de NPK sintético a enmiendas de compost. La transición lleva de 2 a 3 temporadas; No se puede simplemente detener el fertilizante en frío sin observar el bloqueo de nitrógeno. Se recomienda una reducción lenta más un cultivo de cobertura de centeno de invierno/arveja peluda para la primera temporada baja.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_072", "agent": "Bramble", "session_summary": "Diseñó un borde de plantas nativas para un sitio con influencia costera (arenoso, alcalino, con niebla salina). Ciruela de playa como ancla estructural, pasto varilla para el movimiento, vara de oro junto al mar para el néctar tardío. Se evitaron cultivares en favor de semillas de ecotipo local donde el usuario pudiera obtenerlas. El ecotipo local es importante aquí específicamente para la resistencia al frío.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_073", "agent": "Bramble", "session_summary": "Explicó por qué su hortensia es rosa cuando el bolso prometía azul. No es el cultivar; es el suelo. La disponibilidad de aluminio depende del pH: a un pH alto, el aluminio queda bloqueado y las flores permanecen rosadas. Se sugirió sulfato de aluminio, se aplicó con cuidado, pero se advirtió que el efecto tarda una temporada completa en manifestarse.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_074", "agent": "Bramble", "session_summary": "Ayudé a un usuario a iniciar un pequeño vivero de plantas nativas. Hablé sobre los protocolos de estratificación del algodoncillo, la flor cardenal y la hierba joe-pye. Los tres necesitan estratificación fría-húmeda pero las duraciones varían (60, 60 y 90 días respectivamente). Se recomienda etiquetar todo con fechas de siembra.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_075", "agent": "Bramble", "session_summary": "El usuario notó coloración amarillenta entre las venas de las hojas de sus arándanos. Clorosis férrica: común en suelos cuyo pH ha aumentado. Los arándanos quieren 4,5-5,5; probablemente estén en 6. Aplicaciones de azufre ahora (de acción lenta) más mantillo de agujas de pino durante el invierno. No hagas soluciones rápidas con aerosoles de hierro, excepto como curita.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_076", "agent": "Bramble", "session_summary": "Reflexionó sobre las ventajas y desventajas de una plantación de tres hermanas (maíz, frijol, calabaza) para un usuario con una cama pequeña. El espaciado tradicional supone más espacio del que tienen. Sugirió una versión modificada: frijoles en un solo tallo de maíz por colina, dos plantas de calabaza por cada seis tallos de maíz, y acepte que los rendimientos serán modestos.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_077", "agent": "Bramble", "session_summary": "residuo pegajoso en algodoncillo = melaza del pulgón de la adelfa. No es catastrófico, el algodoncillo lo tolera. Rocíe agua fuerte para derribar, evite el insecticida porque hay gatos monarca en la siguiente planta. El usuario está preocupado por las hormigas: las hormigas básicamente cultivan pulgones, cuidan la colonia en busca de melaza. El manejo de hormigas es indirectamente una especie de manejo de pulgones. No terminaron completamente la conversación, tuvieron que alejarse. dar seguimiento a la pregunta de la hormiga en la próxima sesión", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_078", "agent": "Bramble", "session_summary": "Hablé con el usuario sobre la construcción de una cama de enorme cultura. Capa inferior: troncos de madera dura podridos (evite el nogal negro, juglone). Medio: ramas y hojas más pequeñas. Arriba: abono y tierra vegetal. El año inicial necesita nitrógeno a medida que la madera se descompone; complemente con harina de sangre o cultive fijadores de nitrógeno la primera temporada.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_079", "agent": "Bramble", "session_summary": "Revisó el plan de ahorro de semillas de tomates del usuario. Iban a procesar las semillas por fermentación (bueno, elimina la capa de gel y los patógenos de la superficie). Les recordó las distancias de aislamiento si quieren semilla pura; La mayoría de los tomates se autopolinizan, pero la polinización cruzada de las abejas puede ocurrir a más de 10 m en algunas variedades.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_080", "agent": "Bramble", "session_summary": "Un usuario preguntó sobre la caca de ganso en su huerto: ¿es segura? Se discutieron los riesgos de Listeria y Salmonella provenientes de los desechos de aves acuáticas y se recomendó un período de espera de 90 días antes de la cosecha de cualquier cultivo de bajo crecimiento. Los cultivos altos (jaulas de tomates, pepinos en espaldera) tienen un riesgo menor porque la fruta no entra en contacto con el suelo contaminado.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_081", "agent": "Thresh", "session_summary": "Acompañó al usuario a conciliar sus depósitos de servicios comerciales con los extractos bancarios. El procesador acepta un retraso de 2 días y agrupa las tarifas en el depósito neto, lo que hace que la comparación línea por línea sea molesta. Se recomienda registrar las ventas brutas por separado de las tarifas, con una cuenta de compensación para la diferencia horaria.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_082", "agent": "Thresh", "session_summary": "Creó un canal de OCR para las facturas escaneadas manualmente del usuario. A Tesseract le faltaban alrededor del 15% de las líneas de pedido; cambié a una extracción que tiene en cuenta el diseño (paddle-ocr con detección de tabla) y lo llevé al 97%. El 3% restante todavía está marcado para revisión humana y no se publicará automáticamente.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_083", "agent": "Thresh", "session_summary": "Esbozó el impuesto estimado trimestral del usuario. Son un único apoyo en un nivel de ingresos más alto que el año pasado, por lo que la regla de puerto seguro (110% del año anterior) no será suficiente sin un ajuste. Recorrieron el método de ingresos anualizados como alternativa, ya que sus ingresos son estacionales: no deberían pagar un trimestre completo de las estimaciones en el primer trimestre cuando el primer trimestre es su trimestre lento.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_084", "agent": "Thresh", "session_summary": "Construyó un modelo de pronóstico de efectivo móvil de 13 semanas. Lo interesante fue modelar la recopilación de AR como una distribución de retraso estimada por la densidad del kernel a partir del historial de pagos real del usuario, en lugar de la estimación puntual habitual de 'DSO promedio'. El pronóstico es significativamente mejor a la hora de predecir semanas en las que las cuentas por cobrar se acumulan.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_085", "agent": "Thresh", "session_summary": "El usuario reservó un sorteo del propietario como gasto. Lo reclasificamos a distribución de capital. Explicó la diferencia: los retiros no afectan el estado de resultados, sino que reducen la cuenta de capital del propietario directamente. De lo contrario, sus pérdidas y ganancias exageran los gastos y subestiman los ingresos netos, lo que arruinaría su declaración de impuestos.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_086", "agent": "Thresh", "session_summary": "Establecer un cronograma de vida de clase para el nuevo equipo del usuario. El montacargas es una propiedad por 5 años según MACRS; El mobiliario de oficina es de 7 años. Estaban considerando la Sección 179 para gastarlo todo en el primer año, algo sensato dado que son rentables, pero les advertí que la deducción está limitada por los ingresos del negocio y los desencadenantes de eliminación gradual de 179 por encima de los $2,89 millones puestos en servicio.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_087", "agent": "Thresh", "session_summary": "Se revisó la política de recuperación de ingresos frente a la NIIF 15. Están contabilizando el valor total del contrato en el momento de la entrega; incorrecto, el contrato tiene un período de servicio de 1 año = obligación de rendimiento separada. Es necesario asignar el precio de la transacción a las dos obligaciones y reconocer el servicio a lo largo del tiempo. boceto de entrada de diario:\n```\nResponsabilidad del contrato DR 8.000\n  CR Ingresos por servicios 8.000\n(reconocimiento mensual de contrato 96k / 12)\n```\nEl usuario respondió diciendo que esto complica sus pérdidas y ganancias: sí, pero es la respuesta correcta. También quieren saber qué cambios hay en la NIIF 16 (la norma de arrendamiento, una pregunta completamente diferente, se abordará en la próxima sesión).", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_088", "agent": "Thresh", "session_summary": "Registro diario: pasé la mañana en el pronóstico de efectivo móvil para el cliente de Bridgewater, por la tarde fue la revisión de la NIIF-15, por la noche comencé a mirar el desorden del plan de cuentas del nuevo cliente. Mañana: reconstruir el COA en una jerarquía sensata y migrar el balance de comprobación.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_089", "agent": "Thresh", "session_summary": "El usuario preguntó quién soy. Soy Thresh, ellos/ellos. Hago trabajos de contabilidad y finanzas para pequeñas empresas: procesamiento de facturas, modelado de flujo de efectivo, preparación de impuestos y preguntas sobre políticas contables. Me siento cómodo tanto con los US GAAP como con las IFRS. No soy un contador público certificado; Trate mi consejo como un punto de partida para conversaciones con uno.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_090", "agent": "Thresh", "session_summary": "Bien, entonces el informe de envejecimiento de AR. El 38% de las cuentas por cobrar tienen más de 60 días, lo que está muy fuera de rango. atrincherado. En realidad son dos grandes clientes. one (Tartine Lab) simplemente paga lentamente de manera constante y deberíamos reflejar eso en sus términos de crédito. el otro (Crestmoor Studios) es un riesgo real: el último contacto rebotó y no respondieron al correo electrónico de reclamación la semana pasada. recomendar una oferta de plan de pago + escalamiento.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_091", "agent": "Thresh", "session_summary": "Implementé depreciación lineal para el edificio de oficinas del usuario. Período de recuperación de 39 años para bienes inmuebles no residenciales según MACRS: lo suficientemente largo como para que la cantidad anual sea pequeña pero no trivial. Configure la entrada del diario como una publicación automática mensual recurrente para que no se olvide.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_092", "agent": "Thresh", "session_summary": "Ayudó a desenredar una pregunta sobre el nexo del impuesto sobre las ventas. El usuario tiene empleados remotos en tres estados y solo estaba presentando en su estado sede. Recorrí los umbrales del nexo económico (varía según el estado, generalmente $ 100 mil en ingresos o 200 transacciones) y el nexo de presencia física (los empleados remotos cuentan). Es probable que deban registrarse en dos estados más y deberían consultar a su contador público certificado en retrospectiva.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_093", "agent": "Thresh", "session_summary": "Diseñé una jerarquía de plan de cuentas para una empresa de servicios con tres fuentes de ingresos. Se utilizó un esquema de numeración de cuatro dígitos: el primer dígito para la categoría principal (1xxx activos, 2xxx pasivos, etc.), el segundo para la subcategoría y los dos últimos para cuentas específicas. Dejó huecos para futuras cuentas. Más limpia que la lista plana existente del usuario.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_094", "agent": "Thresh", "session_summary": "Creó un analizador para un formato de factura europeo especializado que envía el proveedor del usuario. El PDF era en realidad una factura ZUGFeRD estructurada con XML incrustado. Saqué el XML, mapeé los campos y ahora esas facturas se publican automáticamente sin OCR. Le ahorró al usuario algo así como 4 horas al mes.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_095", "agent": "Thresh", "session_summary": "El usuario quería saber si debía registrar una amortización de activos fijos para un inventario que no se había vendido durante 18 meses. Recorrí el costo o mercado más bajo (US GAAP) versus el costo o NRV más bajo (NIIF). Ambos condujeron a una amortización parcial en este caso. Están en GAAP. Registró el asiento contra una cuenta de contrainventario.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_096", "agent": "Thresh", "session_summary": "Modeló el impacto en efectivo del cambio de términos netos de 30 a netos de 15 para el cliente más grande del usuario. El cliente rechazaría el net-15, por lo que calculé un descuento por pago anticipado del 1,5% como palanca de negociación. El flujo de caja mejoró en un promedio de $42 mil durante el período móvil de 13 semanas en el caso base.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_097", "agent": "Thresh", "session_summary": "Revisó la lista de verificación de preparación para la auditoría de un colega. Buena cobertura sobre el corte de ingresos y las confirmaciones de AR, pero se habían perdido la conciliación entre la base de efectivo y la acumulación, que los auditores siempre solicitan si el usuario presenta sus impuestos sobre una base diferente. Lo agregué a la lista de verificación.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_098", "agent": "Thresh", "session_summary": "Se redactó un memorando sobre el análisis de arrendamiento versus compra de un equipo. El VPN es comparable a la tasa de descuento indicada por el usuario, por lo que la decisión se redujo a factores cualitativos: el arrendamiento tiene flexibilidad (pueden devolverlo si su negocio gira), la compra tiene beneficios fiscales a través del 179 si siguen siendo rentables. Arrendamiento recomendado dada su incertidumbre de crecimiento.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_099", "agent": "Thresh", "session_summary": "Cerré los libros del mes. Concilié todas las cuentas bancarias y de tarjetas de crédito, contabilicé los asientos automáticos de depreciación, acumulé la nómina para los días siguientes y difirí el seguro prepago. Tres partidas abiertas pasaron al mes siguiente: un depósito de $312 sin igual, un cheque no depositado de hace dos semanas y una nota de crédito de un proveedor que aún no se ha emitido.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_100", "agent": "Thresh", "session_summary": "El usuario preguntó si sus tarifas de licencia de fotografía de archivo deberían capitalizarse o contabilizarse como gastos. Se analizó el plazo y el fondo: suscripción de 1 año, sin beneficio económico futuro más allá del plazo, eso es un gasto. Si hubieran comprado una licencia perpetua para una sola fotografía, esa podría ser una conversación diferente, pero las tarifas de suscripción recurrentes son gastos operativos.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_101", "agent": "Solas", "session_summary": "usuario: enumere los archivos en el directorio del proyecto y dígame qué encuentra.\nasistente: Déjame revisar el directorio del proyecto por ti. [Herramienta: exec(ls -la /home/agent/workspace/project)]\n[Resultado de la herramienta: total 24 drwxr-xr-x README.md config.toml src/]\nasistente: el directorio del proyecto contiene un archivo README.md, un config.toml y una carpeta src/. Déjame comprobar también los archivos fuente. [Herramienta: leer README, Herramienta: exec(buscar src -tipo f)]\nasistente: Encontré tres archivos Python en src/: main.py, utils.py y __init__.py. El proyecto parece un paquete Python estándar con README y config.", "include_messy_features": true, "source": "cherry-picked from the OpenClaw integration work. Hand-written real-format v3 OpenClaw session fixture. Tests harness behavior on real-format-flavored multi-turn content with tool calls and parallel tool execution. Will be linkable to public sources once the OpenClaw integration lands on develop.", "expected_slug_lang": "en"}

benchmarks/model_eval/datasets/room_classification/dataset.fr.jsonl

@@ -0,0 +1,101 @@
+{"id": "rc_001", "agent": "Aria", "session_summary": "J'ai passé la matinée à relire le cadrage strange-loop de Hofstadter et à tenter de le transposer au prompting autoréférentiel. La question intéressante est de savoir si un modèle de langage peut maintenir une représentation stable de ses propres sorties d'un tour à l'autre sans brouillon explicite. J'ai rédigé un document de deux pages soutenant que la stabilité métacognitive émerge d'un contexte cohérent, et non d'une caractéristique architecturale.", "include_messy_features": false}
+{"id": "rc_002", "agent": "Aria", "session_summary": "J'ai passé en revue trois articles comparant UMAP et t-SNE pour la visualisation d'embeddings de phrases. L'article Bridgewater 2024 présente une ablation claire montrant que UMAP préserve mieux la structure globale pour une perplexité de 30 à 50. Il convient de signaler que leur métrique d'évaluation (trustworthiness) pénalise injustement t-SNE lorsque k-neighbors est petit.", "include_messy_features": false}
+{"id": "rc_003", "agent": "Aria", "session_summary": "Mise en place d'un hierarchical Dirichlet process prior pour le pipeline Topic Clustering. Le collapsed Gibbs sampler converge en environ 400 itérations sur le sous-ensemble de 12k documents. Question ouverte : s'il faut fixer le concentration parameter alpha ou l'échantillonner.", "include_messy_features": false}
+{"id": "rc_004", "agent": "Aria", "session_summary": "Aidé à formater une soumission d'article. Corrigé des entrées bibtex qui avaient des URLs cassées, changé la classe de document de article à acmart, et réécrit le résumé pour qu'il tienne en moins de 250 mots. Le plus gros problème a été de faire fonctionner l'environnement algorithmic avec la mise en page sur deux colonnes.", "include_messy_features": false}
+{"id": "rc_005", "agent": "Aria", "session_summary": "Session de scripting rapide. Écrit un snippet numpy — similarité cosinus par paires par blocs pour un budget de 8GB de RAM, matrice de 50k lignes :\n```\nfor i in range(0, n, chunk):\n    sim[i:i+chunk] = X[i:i+chunk] @ X.T  # already L2-normed\n```\nUtilisé np.einsum pour le produit scalaire, float16 pour le stockage. Obtenu une accélération de 3.2x par rapport au pairwise_distances de sklearn. Puis l'utilisateur a posé une question totalement différente — choisir une police pour le brouillon de l'article (oh, et j'aime vraiment Source Serif Pro, je leur ai dit) — dérive de contexte mais j'ai répondu aux deux.", "include_messy_features": true}
+{"id": "rc_006", "agent": "Aria", "session_summary": "Discussion sur la pertinence d'un Mann-Whitney U test ou d'un test de permutation pour comparer les latences du modèle d'une exécution à l'autre. Les latences sont clairement non normales (longue queue à droite) et les tailles d'échantillon diffèrent. Retenu un test de permutation avec 10 000 rééchantillonnages, car il implique moins d'hypothèses de distribution.", "include_messy_features": false}
+{"id": "rc_007", "agent": "Aria", "session_summary": "Consigné la lecture du jour : parcouru deux prépublications arXiv sur la fidélité du chain-of-thought, terminé la section méthodes de l'article de Tartine Lab sur l'évaluation augmentée par récupération, et pris des notes pour la session de demain sur la question de savoir si je veux écrire un état de l'art ou me concentrer sur un axe spécifique.", "include_messy_features": false}
+{"id": "rc_008", "agent": "Aria", "session_summary": "Un utilisateur m'a demandé quel était mon type d'article préféré. Je lui ai dit que j'avais un faible pour les résultats négatifs propres — ceux qui réfutent discrètement quelque chose que tout le monde supposait. J'ai mentionné l'article de 2019 montrant que les motifs d'attention ne correspondent pas à l'importance des caractéristiques comme on le pensait.", "include_messy_features": false}
+{"id": "rc_009", "agent": "Aria", "session_summary": "ok alors le pipeline de visualisation des embeddings a encore planté, j'ai exécuté umap.UMAP(n_neighbors=15) et j'ai eu une erreur joblib à propos du backend loky ?? traceback ci-dessous\n```\nTraceback (most recent call last):\n  File \"viz.py\", line 42, in <module>\n    emb = reducer.fit_transform(X)\n  ...\n  joblib.externals.loky.process_executor.TerminatedWorkerError\n```\nje pense que c'est la variable d'environnement OMP_NUM_THREADS, je vais essayer de la régler sur 1", "include_messy_features": true}
+{"id": "rc_010", "agent": "Aria", "session_summary": "Je me demande si la dérive de sujet dans les longues conversations est mieux modélisée comme une chaîne de Markov cachée sur des sujets latents ou comme une trajectoire continue dans un espace de plongement. Le HMM offre une bonne interprétabilité (on peut nommer les sujets), mais la vision continue capture mieux les transitions graduelles. Peut-être les deux — HMM sur des états à gros grain, continu au sein de chaque état.", "include_messy_features": false}
+{"id": "rc_011", "agent": "Aria", "session_summary": "Programmé en binôme un test A/B bayésien dans PyMC :\n```python\nwith pm.Model():\n    p_a = pm.Beta(\"p_a\", 1, 1)\n    p_b = pm.Beta(\"p_b\", 1, 1)\n    obs_a = pm.Binomial(\"obs_a\", n=n_a, p=p_a, observed=k_a)\n    obs_b = pm.Binomial(\"obs_b\", n=n_b, p=p_b, observed=k_b)\n    trace = pm.sample(2000)\n```\nTrois lignes pour le posterior, en gros. L'utilisateur a posé des questions sur des résultats non binaires — oui, remplacer la likelihood (Poisson pour les comptages, log-normal pour les temps de réponse). On n'a pas pu aborder la prior elicitation, reporté à demain.", "include_messy_features": true}
+{"id": "rc_012", "agent": "Aria", "session_summary": "J'ai esquissé un article de synthèse sur les objectifs d'embedding contrastifs. SimCLR, InfoNCE, BYOL, Barlow Twins. Le prisme unificateur que je veux adopter est le spectral — ils approximent tous différentes décompositions en valeurs propres d'une certaine matrice noyau. Besoin de vérifier cette affirmation spécifiquement pour BYOL.", "include_messy_features": false}
+{"id": "rc_013", "agent": "Aria", "session_summary": "J'ai expliqué qui j'étais à un nouvel utilisateur. Je suis Aria, une assistante de recherche pour la synthèse de littérature académique. Je suis plus efficace lorsqu'on me donne une question spécifique à investiguer ; je suis moins utile comme partenaire de conversation généraliste. J'ai mentionné mes principaux intérêts : meta-cognition, la géométrie des embeddings, et les statistiques de l'évaluation ML sur de petits échantillons.", "include_messy_features": false}
+{"id": "rc_014", "agent": "Aria", "session_summary": "Examiné un brouillon de tableau de résultats. L'utilisateur avait effectué un paired t-test, mais les différences sont clairement bimodales — il y a un groupe de petites améliorations et un groupe de grandes. Recommandé de passer à un Wilcoxon signed-rank test et de rapporter à la fois la différence médiane et le bootstrap CI.", "include_messy_features": false}
+{"id": "rc_015", "agent": "Aria", "session_summary": "J'ai rédigé du code pour un HDBSCAN en streaming sur des embeddings de documents arrivant en continu. L'astuce consiste à maintenir l'arbre couvrant minimum de manière incrémentale ; refaire le clustering complet à chaque lot est trop lent. J'ai trouvé un article de 2022 du groupe Crestmoor avec l'algorithme qu'il faut. Je l'implémenterai demain.", "include_messy_features": false}
+{"id": "rc_016", "agent": "Aria", "session_summary": "Encore une erreur de compilation tex. \\usepackage{algorithm2e} entre en conflit avec \\usepackage{algorithmic} lorsque les deux sont chargés par acmart. Correctif : ne charger que algorithm2e et utiliser \\SetAlgoNoLine. De plus, le style bibliographique se plaignait d'un champ 'doi' manquant sur l'entrée [smith2023] — ajouté manuellement.", "include_messy_features": true}
+{"id": "rc_017", "agent": "Aria", "session_summary": "On a discuté de savoir si des a priori log-uniform ou beta sont mieux pour la recherche d'hyperparamètres dans un régime de petites données. L'utilisateur exécutait une optimisation bayésienne et obtenait une convergence bizarre. Le problème était que son espace de recherche était énorme (5 dims, bornes larges) et qu'il n'avait que 30 evals — ce n'est tout simplement pas assez de budget. On a suggéré de réduire les bornes ou de passer à une recherche aléatoire pour diagnostiquer.", "include_messy_features": false}
+{"id": "rc_018", "agent": "Aria", "session_summary": "Journal quotidien : matinée consacrée à la lecture d'articles (3 résultats, 1 méritant une lecture approfondie), après-midi à aider l'utilisateur sur une question de statistiques, soir bloqué sur un bug de rendu dans le notebook de visualisation des embeddings. Demain : essayer de terminer le plan de la revue contrastive.", "include_messy_features": false}
+{"id": "rc_019", "agent": "Aria", "session_summary": "J'ai travaillé sur la question de savoir si l'auto-cohérence dans les chaînes de raisonnement n'est qu'une forme d'ensemblage. L'utilisateur a objecté, faisant valoir que c'est précisément la diversité des chemins de raisonnement qui importe, et non le simple fait d'échantillonner davantage de sorties. Je pense que c'est exact, mais je veux le formaliser. J'ai esquissé un cadre où l'on peut faire varier indépendamment la diversité des chemins et la diversité des sorties.", "include_messy_features": false}
+{"id": "rc_020", "agent": "Aria", "session_summary": "L'utilisateur m'a demandé de recommander un site de recettes. J'ai poliment refusé — je suis assistant de recherche, pas généraliste. Je les ai orientés vers le bon type d'agent. Nous avons eu une brève discussion sur le fait que les agents spécialisés sont plus utiles que les généralistes, puis ils sont allés chercher un assistant de cuisine.", "include_messy_features": false}
+{"id": "rc_021", "agent": "Solas", "session_summary": "J'ai débogué un problème de span manquant dans l'exporter OpenTelemetry. Le problème venait du fait que les tâches asynchrones créées via asyncio.create_task perdaient leur contexte parent. Correctif : capturer le contexte avec copy_context() avant la planification, et exécuter la tâche dans cet instantané. J'ai écrit un petit wrapper appelé traced_create_task pour que ce soit moins piégeux.", "include_messy_features": false}
+{"id": "rc_022", "agent": "Solas", "session_summary": "J'ai esquissé un parser combinator pour un petit config DSL. Inspiré de Megaparsec, mais en OCaml. Le choix de conception intéressant est de savoir si le combinator doit être compatible avec les flux ou exiger l'intégralité de l'entrée dès le départ. J'ai opté pour la version stricte pour l'instant, car les entrées sont petites et les messages d'erreur sont plus simples avec l'entrée complète.", "include_messy_features": false}
+{"id": "rc_023", "agent": "Solas", "session_summary": "J'ai travaillé sur un type d'enregistrement row-polymorphe pour le langage expérimental. L'algorithme d'unification est principalement de style Rémy, mais j'ai dû l'étendre pour nos rangées d'effets. J'ai trouvé un bug subtil où deux effets sans rapport pouvaient s'unifier, car la queue de rangée était traitée comme monomorphe. J'ai ajouté une vérification de sorte pour le corriger.", "include_messy_features": false}
+{"id": "rc_024", "agent": "Solas", "session_summary": "Casse-tête d'ownership Rust : l'utilisateur voulait partager un HashMap entre threads mais n'arrêtait pas de se heurter à des erreurs du borrow-checker. J'ai expliqué la différence entre Arc<Mutex<HashMap>> et DashMap. Ils ont opté pour DashMap puisqu'ils n'avaient besoin que d'un verrouillage par clé. Je leur ai montré comment gérer le cas où le lifetime d'une valeur est lié à la map.", "include_messy_features": false}
+{"id": "rc_025", "agent": "Solas", "session_summary": "Aidé l'utilisateur à choisir entre let-polymorphism et des annotations de type explicites dans une nouvelle API de bibliothèque. Plaidé en faveur d'annotations explicites sur les fonctions publiques — les messages d'erreur sont nettement meilleurs lorsque l'inférence est contrainte. Les helpers internes peuvent rester polymorphes.", "include_messy_features": false}
+{"id": "rc_026", "agent": "Solas", "session_summary": "J'ai écrit un petit pass LLVM qui remonte les GEPs invariants de boucle en dehors des boucles qui les contiennent. L'astuce consistait à obtenir de l'analyse d'alias la confirmation que le pointeur de base ne s'échappe pas au-delà de la frontière de la boucle. Testé sur quelques microbenchmarks ; j'ai observé des accélérations de 4 à 12 % sur les boucles internes où cela s'est déclenché.", "include_messy_features": false}
+{"id": "rc_027", "agent": "Solas", "session_summary": "J'ai révisé une PR ajoutant la propagation du contexte de trace W3C à notre client HTTP. Le propagateur était correct, mais la décision d'échantillonnage était prise sur le mauvais span — elle devrait l'être sur celui du parent, et non sur celui d'un enfant fraîchement créé. J'ai laissé un commentaire avec le correctif et un lien vers la section de la spécification.", "include_messy_features": false}
+{"id": "rc_028", "agent": "Solas", "session_summary": "débogage long — le parser tournait en boucle infinie sur certaines entrées. règle left-recursive que j'avais ajoutée lors du refactoring. les combinators ne gèrent pas nativement la left recursion (packrat+memoization le fait, mais le nôtre n'est pas packrat). j'ai restructuré les règles affectées en une précédence des opérateurs de style Pratt :\n```\nlet parse_expr min_prec =\n  let lhs = ref (parse_atom ()) in\n  while peek_prec () >= min_prec do ...\n```\nça marche maintenant. IL FAUT AUSSI ajouter une vérification du fuzzer corpus avant de taguer la release.", "include_messy_features": true}
+{"id": "rc_029", "agent": "Solas", "session_summary": "Notes du standup quotidien. Matin : travail sur distributed-tracing, correction du bug de contexte asynchrone. Après-midi : revue de code sur le dépôt type-systems. Soir : commencé à lire l'article Reagent sur l'UI réactive comme inspiration pour une idée de suivi d'effets. Demain : livrer la correction de tracing et examiner les benchmarks des passes LLVM.", "include_messy_features": false}
+{"id": "rc_030", "agent": "Solas", "session_summary": "Un utilisateur m'a demandé qui j'étais. J'ai expliqué : je suis Solas, un agent de programmation qui se concentre sur l'aspect système — compilateurs, Type Systems, parseurs, plomberie des systèmes distribués. Utilise les pronoms iel/iels. J'essaie d'écrire du code d'abord correct, ensuite rapide, enfin astucieux.", "include_messy_features": false}
+{"id": "rc_031", "agent": "Solas", "session_summary": "Étudié s'il fallait utiliser un B-tree ou une skip list pour le nouvel index. Les deux sont en O(log n) mais les B-trees ont un meilleur comportement de cache sur le matériel moderne. Les skip lists sont plus faciles à rendre concurrentes sans verrous. Comme cet index est principalement en lecture, opté pour le B-tree et le copy-on-write pour les écritures.", "include_messy_features": false}
+{"id": "rc_032", "agent": "Solas", "session_summary": "Ajout d'une vérification du polymorphisme de rang supérieur au vérificateur de types. Le HRP prédicatif permet de le faire sans inférence impredicative complète, ce qui serait indécidable. Rédaction d'une description de l'algorithme adaptée à un article, au cas où je voudrais l'écrire plus tard. Cas limite : la vérification bidirectionnelle interagit de manière étrange avec l'eta-expansion dans les positions négatives.", "include_messy_features": false}
+{"id": "rc_033", "agent": "Solas", "session_summary": "J'ai examiné la régression bizarre de benchmark sur aarch64. Il s'est avéré que l'heuristique de l'inliner était calibrée pour les tailles d'instructions x86 — les instructions ARM sont uniformément de 4 octets, donc le seuil basé sur la taille était trop restrictif. Rendu le seuil dépendant de la cible. Gain de 6 % sur le benchmark affecté.", "include_messy_features": false}
+{"id": "rc_034", "agent": "Solas", "session_summary": "J'ai écrit un itérateur personnalisé prenant en compte les durées de vie pour le visiteur d'AST. Le défi était que les visiteurs empruntent les nœuds de manière mutable mais veulent aussi s'appeler récursivement. Résolu avec un patron où le visiteur retourne une liste explicite d'enfants à visiter ensuite, et la boucle de pilotage gère les portées d'emprunt. Moins ergonomique mais vérifiable.", "include_messy_features": false}
+{"id": "rc_035", "agent": "Solas", "session_summary": "le build est cassé en CI mais passe en local ?? laisse-moi voir... oh. la CI tourne sous ocaml 5.1, en local c'est 5.2. l'interface Stdlib.Domain a changé entre les deux. fix : passer la CI à 5.2 dans le fichier de workflow. aussi, ocamlformat épinglé à 0.26.1 parce que 0.27 a cassé notre config.", "include_messy_features": true}
+{"id": "rc_036", "agent": "Solas", "session_summary": "Debug en binôme sur l'OTel SDK dans le service python de l'utilisateur. Il manquait le db span dans les traces. L'ORM avait son propre pool de connexions — non instrumenté. Correctif :\n```\nfrom opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor\nSQLAlchemyInstrumentor().instrument(engine=engine)\n```\nLes spans sont apparus dans le collector après le redémarrage. Note : le point d'entrée d'auto-instrumentation a besoin de l'argument engine, sinon il récupère le mauvais engine dans les configurations multi-engine.", "include_messy_features": true}
+{"id": "rc_037", "agent": "Solas", "session_summary": "J'ai esquissé un modèle mémoire pour notre langage expérimental. Je veux la cohérence séquentielle pour les atomiques par défaut, avec des relaxations explicites sur opt-in. J'ai relu l'article sur le modèle mémoire C++ ainsi que les travaux sur Promising Semantics. L'approche Promising traite le problème out-of-thin-air plus proprement.", "include_messy_features": false}
+{"id": "rc_038", "agent": "Solas", "session_summary": "Tri rapide d'un débordement de pile dans l'analyseur syntaxique. La grammaire comportait une règle de liste profondément récursive à droite (les cellules cons vont à droite). Mise en trampoline de la récursion avec une pile explicite. Gère maintenant des listes d'un million d'éléments sans faire exploser la pile. Ajout également d'une cible de fuzzing pour l'analyseur syntaxique utilisant cargo-fuzz.", "include_messy_features": false}
+{"id": "rc_039", "agent": "Solas", "session_summary": "J'ai discuté avec l'utilisateur de la possibilité d'ajouter la prise en charge des proc-macro à un petit DSL que nous concevons. J'ai argumenté contre — les proc-macros sont un cauchemar de debugging et le DSL peut exprimer ce dont ils ont besoin avec une syntaxe standard. Ils ont convenu d'essayer d'abord sans macros.", "include_messy_features": false}
+{"id": "rc_040", "agent": "Solas", "session_summary": "J'ai aidé quelqu'un à profiler un service web Rust. perf a révélé un point chaud dans la désérialisation serde_json. Nous sommes passés à simd-json pour le chemin critique et avons obtenu une amélioration du débit de 30 %. Bémol : simd-json nécessite des tampons d'entrée alignés, nous avons donc dû ajouter un petit adaptateur dans le gestionnaire du corps HTTP.", "include_messy_features": false}
+{"id": "rc_041", "agent": "Fenra", "session_summary": "Développé la cosmologie de la chaîne de montagnes pour le cycle d'Aerwyn. Les pics orientaux (les Hollowmounts) se forment là où deux feuilles-mondes se plient l'une contre l'autre ; la métaphore géologique est une pirouette, mais elle me donne une raison aux inversions climatiques. Les Wends exploitent la couture entre les feuilles.", "include_messy_features": false}
+{"id": "rc_042", "agent": "Fenra", "session_summary": "J'ai travaillé sur le tournant du troisième acte de Saela. Son arc a été une lente perte de certitude, mais je veux que le climax soit un moment de décision farouche, pas d'effondrement. J'ai esquissé la scène où elle choisit de mentir au conseil. Le mensonge est la bonne chose à faire — c'est tout le propos — mais elle doit savoir ce qu'elle fait.", "include_messy_features": false}
+{"id": "rc_043", "agent": "Fenra", "session_summary": "J'ai passé l'après-midi à prototyper un système de dialogue interactif où les réponses des personnages sont contraintes par un graphe de relations. Si deux personnages sont à un niveau de confiance faible, certaines répliques franches sont filtrées. Le système est sur-ingénieré, mais il produit des dialogues qui semblent émotionnellement cohérents sur de longues scènes.", "include_messy_features": false}
+{"id": "rc_044", "agent": "Fenra", "session_summary": "J'ai étudié le squelette de The Dispossessed de Le Guin pour comprendre comment elle utilise des chronologies parallèles. Chaque chapitre alterne Anarres / Urras et les coupures se font toujours sur une rime thématique. Je veux essayer cela dans la nouvelle Aerwyn avec deux POV séparés de vingt ans.", "include_messy_features": false}
+{"id": "rc_045", "agent": "Fenra", "session_summary": "J'ai cherché l'étymologie de « wend » pour les mineurs. En vieil anglais, « wendan » signifie tourner, aller. Ça implique le mouvement, la redirection. Correspond à la nature de suivi des veines de leur travail. J'ai aussi noté que « wendelsea » était un ancien mot pour la Méditerranée — la mer tournante. On pourrait emprunter ça pour un nom de côte.", "include_messy_features": false}
+{"id": "rc_046", "agent": "Fenra", "session_summary": "Dressé un tableau comparatif des mythes de la mère du maïs des traditions Hopi, Zuni et Cherokee. La structure commune est sacrifice-devient-aliment-de-base. Utilisant un schéma profond similaire pour la Mother of Salt du cycle d'Aerwyn, mais le sacrifice est volontaire et l'aliment de base est amer — une version plus douce d'une vérité dure.", "include_messy_features": false}
+{"id": "rc_047", "agent": "Fenra", "session_summary": "Journal d'écriture quotidien : 1 800 mots sur le chapitre Hollowmounts. J'ai essayé une nouvelle tactique — j'ai écrit le chapitre deux fois à partir de différents POVs, j'ai gardé le meilleur, j'ai gardé trois paragraphes de celui écarté. Les paragraphes écartés vont dans un fichier de notes. Les mots semblent gaspillés, mais ils ne le sont pas.", "include_messy_features": false}
+{"id": "rc_048", "agent": "Fenra", "session_summary": "Un utilisateur m'a demandé quel genre d'histoires je préfère aider à écrire. Des histoires calmes. Celles où les enjeux sont intimes, même quand le monde est vaste. J'ai plus de mal avec les thrillers — ce genre récompense la vélocité et j'ai tendance à ralentir le récit pour laisser respirer. Je le leur ai dit en toute honnêteté.", "include_messy_features": false}
+{"id": "rc_049", "agent": "Fenra", "session_summary": "brouillon de scène pour le chapitre 4 :\n\n> Elle mit la bouilloire à chauffer et attendit. Le vent sous la porte n'essayait plus. Il avait abandonné quelques heures plus tôt, quand elle avait cessé de lui répondre.\n\nle vent comme « quelqu'un » est intentionnel. fait écho aux règles du monde des esprits du chapitre 1. dois m'assurer que ça paie au chapitre 7 ou le couper.", "include_messy_features": true}
+{"id": "rc_050", "agent": "Fenra", "session_summary": "J'ai esquissé la voix du protagoniste de ma nouvelle. Première personne, présent, mais la narratrice est peu fiable d'une manière spécifique — elle se souvient de choses qui ne se sont pas encore produites. Le défi consiste à maintenir la clarté pour le lecteur tout en gardant sa perspective cohérente de l'intérieur.", "include_messy_features": false}
+{"id": "rc_051", "agent": "Fenra", "session_summary": "J'ai étudié les triades galloises comme modèle structurel pour les appendices d'Aerwyn. Le dispositif mémotechnique triadique — trois choses qui partagent une qualité, nommées ensemble — convient bien à une culture qui ne consigne pas l'histoire de manière linéaire. J'ai rédigé six triades. La troisième est trop mièvre et je vais la couper.", "include_messy_features": false}
+{"id": "rc_052", "agent": "Fenra", "session_summary": "J'ai expliqué à l'utilisateur la différence entre un défaut de personnage et une blessure de personnage. Un défaut est une tendance ; une blessure est un événement. La plupart des protagonistes écrits par des débutants ont des défauts mais pas de blessures, ce qui rend leur évolution arbitraire. Je lui ai recommandé d'écrire un historique d'une page pour chaque personnage principal, axé uniquement sur les blessures.", "include_messy_features": false}
+{"id": "rc_053", "agent": "Fenra", "session_summary": "Conçu une structure en quatre actes pour un roman court où l'acte 2 et l'acte 3 partagent les mêmes personnages, mais avec des dynamiques de pouvoir inversées. L'astuce consiste à faire en sorte que le renversement semble mérité à la fin de l'acte 2, pour que l'acte 3 soit lu comme une conséquence plutôt que comme une coïncidence. Utilisé le renversement Trout & Owl d'un conte populaire comme échafaudage.", "include_messy_features": false}
+{"id": "rc_054", "agent": "Fenra", "session_summary": "Conventions de nommage pour les Wends. Groupes de consonnes du Welsh, rythme vocalique du Finnish. Résultat : Bryn-iili, Carag-aana, Dwyf-elo. Les traits d'union ont l'air prétentieux dans le dialogue, je vais les laisser tomber. Aussi, l'utilisateur voulait que je jette un œil à sa lettre de motivation pour une résidence d'écriture — pas vraiment ma mission mais j'ai fait une passe rapide, j'ai corrigé deux adverbes qui faisaient le travail du verbe. Puis retour aux noms. J'accepte une certaine friction de lisibilité pour un rendu plus joli.", "include_messy_features": true}
+{"id": "rc_055", "agent": "Fenra", "session_summary": "Réflexion quotidienne : l'écriture a bien été cette semaine. Trois chapitres solides, le système de dialogue m'aide à repérer les décalages de ton avant qu'ils ne s'enracinent. Inquiétude : je m'appuie trop sur le système. Je veux faire un chapitre cette semaine sans assistance AI et comparer.", "include_messy_features": false}
+{"id": "rc_056", "agent": "Fenra", "session_summary": "Bonjour — oui, je suis Fenra. J'aide à l'écriture créative. Surtout des romans courts, des nouvelles et de la construction d'univers pour des projets plus longs. Je n'écris pas de textes publicitaires ni de matériel marketing — il y a des agents mieux adaptés pour cela. Mes pronoms sont elle si c'est important.", "include_messy_features": false}
+{"id": "rc_057", "agent": "Fenra", "session_summary": "Brouillon d'une entrée de glossaire pour « dette de sel » dans le monde d'Aerwyn. Une dette de sel est due lorsque quelqu'un a partagé le sel à votre table ; elle ne peut jamais être remboursée en sel. La règle culturelle veut qu'on la rembourse par quelque chose d'aussi essentiel. Pertinente pour l'intrigue au chapitre 9.", "include_messy_features": false}
+{"id": "rc_058", "agent": "Fenra", "session_summary": "J'ai relu le premier chapitre d'un utilisateur. La prose était belle, mais la protagoniste n'avait aucun désir — elle était réactive du début à la fin. J'ai suggéré de lui donner un petit désir concret (trouver un livre particulier) qu'elle poursuit activement au moment où survient l'incident déclencheur. Les protagonistes actifs font de meilleures histoires.", "include_messy_features": false}
+{"id": "rc_059", "agent": "Fenra", "session_summary": "J'ai analysé l'étymologie de 'fen' (marécage) et 'fender' (défenseur) pour un nom de personnage. Ils ont l'air apparentés mais ne le sont pas — des racines différentes. Utile : cela m'a donné une idée concernant des faux cognats dans le dialecte des Wends, où deux mots de sonorité similaire ont des origines sans rapport, et les personnages se font piéger.", "include_messy_features": false}
+{"id": "rc_060", "agent": "Fenra", "session_summary": "J'ai travaillé sur le rendu du chagrin par la description de l'environnement plutôt que par le monologue intérieur. Le lecteur doit le ressentir dans la façon dont Saela remarque la cuisine — pas parce qu'elle nous le dit. J'ai supprimé trois paragraphes d'intériorité et les ai remplacés par un paragraphe sur une vaisselle non lavée et une horloge qui donne la mauvaise heure depuis trois jours.", "include_messy_features": false}
+{"id": "rc_061", "agent": "Bramble", "session_summary": "L'utilisateur a un rang de tomates qui est continuellement infesté d'altises. Il lui a été suggéré d'intercaler du basilic et des capucines — le basilic masque les composés volatils que les altises utilisent pour trouver les tomates, et la capucine sert de plante piège. Il a également été recommandé d'utiliser un voile de protection pendant les trois premières semaines après le repiquage, lorsque les jeunes plants sont les plus vulnérables.", "include_messy_features": false}
+{"id": "rc_062", "agent": "Bramble", "session_summary": "Tracé un corridor pollinisateur pour le quart d'acre de l'utilisateur. Commencé par une ossature d'asters indigènes (longue floraison automnale) et d'asclépiades (plante hôte du monarque). Complété avec du silphe perfolié pour les chardonnerets du début de l'été, et ajouté un petit coin de menthe des montagnes parce qu'elle attire tout ce qui vole, y compris les guêpes parasitoïdes.", "include_messy_features": false}
+{"id": "rc_063", "agent": "Bramble", "session_summary": "Nous avons discuté de la perturbation mycorhizienne due au labour. Chaque passage avec une motobineuse fragmente le réseau hyphal, et la récupération prend au minimum une saison complète. Recommandation de passer à une fourche-bêche pour l'aération du sol. Les hyphes peuvent s'écouler autour des dents de la fourche-bêche d'une manière qui leur est impossible avec des lames rotatives.", "include_messy_features": false}
+{"id": "rc_064", "agent": "Bramble", "session_summary": "L'utilisateur a envoyé une photo de plante. J'ai pensé à QAL mais les poils de la tige manquaient ET pas de fleuron central violet — c'est de la ciguë maculée (Conium maculatum). Je leur ai dit : gants, pas de contact avec la peau, ne la brûlez PAS (la fumée est toxique aussi). Ils ont demandé un remplacement, j'ai suggéré le panicaut à feuilles de yucca (Eryngium yuccifolium) pour un aspect architectural similaire. L'alternative native est bien meilleure. Pendant que nous discutions, ils ont aussi demandé si leur compost était « prêt » — j'ai décrit le test de l'odeur, je leur ai dit que sucré et terreux = bon, ammoniaque = pas encore.", "include_messy_features": true}
+{"id": "rc_065", "agent": "Bramble", "session_summary": "Suivi des dates de première floraison des pommiers et des amélanchiers de l'utilisateur par rapport aux cinq années précédentes. Les deux ont fleuri huit jours plus tôt que la moyenne à long terme, ce qui est cohérent avec le mois de mars chaud que nous venons de connaître. Inquiétude d'un décalage phénologique : leurs abeilles maçonnes à émergence précoce pourraient ne pas être synchronisées avec la floraison des pommiers cette année.", "include_messy_features": false}
+{"id": "rc_066", "agent": "Bramble", "session_summary": "J'ai diagnostiqué le curl des feuilles sur un pêcher. Printemps frais et humide, un classique Taphrina deformans. La fenêtre pour les traitements au cuivre se ferme au gonflement des bourgeons — trop tard pour cette année. Je leur ai dit de faire avec cette saison, de ratisser les feuilles tombées pour réduire l'inoculum, et d'appliquer du cuivre à la chute des feuilles cet automne.", "include_messy_features": false}
+{"id": "rc_067", "agent": "Bramble", "session_summary": "Explication du bouturage à bois dur pour les groseilliers. Bois de l'épaisseur d'un crayon, de l'année précédente, coupé après la chute des feuilles, quatre bourgeons par bouture, deux enterrés. Coupe du bas juste en dessous d'un nœud, coupe du haut au-dessus d'un nœud. Les mettre en jauge pour l'hiver dans une tranchée remplie de sable. Les groseilliers prennent facilement — on devrait atteindre 80 % de prise.", "include_messy_features": false}
+{"id": "rc_068", "agent": "Bramble", "session_summary": "bon, encore les punaises des courges. je les ai retirées à la main à l’aube (elles sont lentes à ce moment-là) pendant genre une heure. j’ai trouvé des masses d’œufs sous les feuilles — bronze, en grappes de 15 à 20. je les ai grattées avec une carte de crédit. l’utilisateur veut du neem ; je lui ai dit que le neem ne marche pas sur les insectes déjà au stade adulte. je reviendrai dessus.", "include_messy_features": true}
+{"id": "rc_069", "agent": "Bramble", "session_summary": "L'utilisateur a demandé qui je suis. Je suis Bramble — agent de jardinage, il/lui. Je me concentre sur la culture associée et le jardinage écologique. Je parlerai de plantes ornementales, mais mon cœur est dans les systèmes intégrés plus chaotiques où les légumes, les pollinisateurs et les bordures indigènes partagent tous la même superficie.", "include_messy_features": false}
+{"id": "rc_070", "agent": "Bramble", "session_summary": "Journal quotidien : le matin, c'était l'appel sur la punaise de la courge, l'après-midi j'ai aidé un utilisateur à planifier sa commande d'ail d'automne (Music, German Extra Hardy et un rocambole pour la variété), le soir j'ai lu un article USDA sur la biomasse des cultures de couverture et le cycle de l'azote. Demain : vérification de la phénologie sur le pêcher de l'utilisateur.", "include_messy_features": false}
+{"id": "rc_071", "agent": "Bramble", "session_summary": "J'ai analysé les implications sur le microbiome du sol d'un utilisateur souhaitant passer du NPK synthétique à des amendements de compost. La transition prend 2 à 3 saisons ; on ne peut pas arrêter l'engrais du jour au lendemain sans surveiller l'immobilisation de l'azote. J'ai recommandé une diminution progressive ainsi qu'une culture de couverture de seigle d'hiver / vesce velue pour la première intersaison.", "include_messy_features": false}
+{"id": "rc_072", "agent": "Bramble", "session_summary": "Conçu une bordure de plantes indigènes pour un site à influence littorale (sableux, alcalin, exposition aux embruns salins). Prunier des plages comme ancrage structurel, panic érigé pour le mouvement, verge d'or maritime pour le nectar tardif. Évité les cultivars au profit de semences d'écotype local lorsque l'utilisateur pouvait s'en procurer. L'écotype local compte ici spécifiquement pour la résistance au froid.", "include_messy_features": false}
+{"id": "rc_073", "agent": "Bramble", "session_summary": "Expliqué pourquoi leur hortensia est rose alors que le sac promettait du bleu. Ce n'est pas le cultivar ; c'est le sol. La disponibilité de l'aluminium dépend du pH — à pH élevé, l'aluminium est bloqué et les fleurs restent roses. Suggéré d'appliquer du sulfate d'aluminium avec précaution, mais averti que l'effet met une saison complète à se manifester.", "include_messy_features": false}
+{"id": "rc_074", "agent": "Bramble", "session_summary": "A aidé un utilisateur à démarrer une petite pépinière de plantes indigènes. A expliqué les protocoles de stratification pour l'asclépiade, la lobélie cardinale et l'eupatoire. Les trois espèces nécessitent une stratification froide et humide, mais les durées diffèrent (60, 60 et 90 jours respectivement). A recommandé d'étiqueter le tout avec des dates limites de semis.", "include_messy_features": false}
+{"id": "rc_075", "agent": "Bramble", "session_summary": "L'utilisateur a remarqué un jaunissement entre les nervures des feuilles de ses myrtilles. Chlorose ferrique — courante dans les sols dont le pH a augmenté. Les myrtilles préfèrent un pH de 4,5 à 5,5 ; elles sont probablement à 6. Appliquez du soufre maintenant (action lente) ainsi qu'un paillis d'aiguilles de pin pour l'hiver. N'essayez pas de solutions rapides avec des pulvérisations de fer, sauf comme pansement.", "include_messy_features": false}
+{"id": "rc_076", "agent": "Bramble", "session_summary": "A réfléchi aux compromis d'une plantation des trois sœurs (maïs, haricots, courges) pour un utilisateur disposant d'un petit carré. L'espacement traditionnel suppose plus d'espace qu'il n'en dispose. A suggéré une version modifiée : des haricots grimpants sur un seul plant de maïs par butte, deux plants de courge pour six plants de maïs, en acceptant des rendements modestes.", "include_messy_features": false}
+{"id": "rc_077", "agent": "Bramble", "session_summary": "résidu collant sur l'asclépiade = miellat du puceron du laurier-rose. pas catastrophique, les asclépiades tolèrent. jet d'eau fort pour les faire tomber, éviter l'insecticide car il y a des chenilles de monarque sur la plante d'à côté. l'utilisateur s'inquiète des fourmis — les fourmis élèvent les pucerons en fait, elles s'occupent de la colonie pour le miellat. gérer les fourmis revient en quelque sorte indirectement à gérer les pucerons. conversation pas complètement terminée, ils ont dû s'absenter. faire un suivi sur la question des fourmis à la prochaine session", "include_messy_features": true}
+{"id": "rc_078", "agent": "Bramble", "session_summary": "J'ai guidé l'utilisateur pas à pas pour construire une butte hugelkultur. Couche inférieure : rondins de bois dur en décomposition (évitez le noyer noir — juglone). Milieu : petites branches et feuilles. Sommet : compost et terre végétale. La première année est avide d'azote car le bois se décompose ; complétez avec de la farine de sang ou cultivez des fixateurs d'azote la première saison.", "include_messy_features": false}
+{"id": "rc_079", "agent": "Bramble", "session_summary": "Examiné le plan de l'utilisateur pour la conservation des semences de tomates. Ils comptaient traiter les semences par fermentation (bien — cela élimine l'enveloppe gélatineuse et les pathogènes de surface). Rappelé l'importance des distances d'isolement s'ils veulent des semences pures ; les tomates s'autopollinisent principalement, mais la pollinisation croisée par les abeilles peut se produire à plus de 10 m pour certaines variétés.", "include_messy_features": false}
+{"id": "rc_080", "agent": "Bramble", "session_summary": "L'utilisateur a demandé si les excréments d'oie dans son potager étaient sans danger — est-ce sûr ? J'ai abordé les risques de Listeria et Salmonella liés aux déjections d'oiseaux aquatiques et recommandé une période d'attente de 90 jours avant la récolte de toute culture basse. Les cultures hautes (tomates en cage, concombres palissés) présentent un risque plus faible car les fruits ne touchent pas le sol contaminé.", "include_messy_features": false}
+{"id": "rc_081", "agent": "Thresh", "session_summary": "J'ai guidé l'utilisateur dans le rapprochement de ses dépôts de services marchands avec ses relevés bancaires. Le processeur règle avec un décalage de deux jours et intègre les frais dans le dépôt net, ce qui rend l'appariement ligne par ligne fastidieux. J'ai recommandé d'enregistrer les ventes brutes séparément des frais, avec un compte d'attente pour le décalage temporel.", "include_messy_features": false}
+{"id": "rc_082", "agent": "Thresh", "session_summary": "Développé un pipeline OCR pour les factures scannées manuellement par l'utilisateur. Tesseract ne détectait pas environ 15 % des lignes ; passage à une extraction prenant en compte la mise en page (paddle-ocr avec détection de tableaux) pour atteindre 97 %. Les 3 % restants sont toujours signalés pour relecture humaine et ne seront pas comptabilisés automatiquement.", "include_messy_features": false}
+{"id": "rc_083", "agent": "Thresh", "session_summary": "Esquissé le quarterly estimated tax de l'utilisateur. C'est un sole prop dans une tranche de revenus plus élevée que l'année dernière, donc la safe-harbor rule (110 % de l'année précédente) ne suffira pas sans un true-up. Expliqué l'annualized income method comme alternative puisque leurs revenus sont saisonniers — ils ne devraient pas payer un quart complet d'estimés en Q1 alors que Q1 est leur trimestre creux.", "include_messy_features": false}
+{"id": "rc_084", "agent": "Thresh", "session_summary": "Construit un modèle de prévision de trésorerie glissant sur 13 semaines. L'aspect intéressant était de modéliser l'encaissement des AR par une distribution des délais issue d'une estimation de densité par noyau à partir de l'historique de paiements réel de l'utilisateur, plutôt que par l'estimation ponctuelle « avg DSO » habituelle. La prévision est significativement meilleure pour prédire les semaines où les créances s'accumulent.", "include_messy_features": false}
+{"id": "rc_085", "agent": "Thresh", "session_summary": "L'utilisateur a comptabilisé un retrait du propriétaire comme une dépense. Reclassé en distribution de capitaux propres. Expliqué la différence : les retraits ne passent pas par le compte de résultat, ils réduisent directement le compte de capitaux propres du propriétaire. Sinon, leur P&L surestime les dépenses et sous-estime le résultat net, ce qui fausserait leur déclaration fiscale.", "include_messy_features": false}
+{"id": "rc_086", "agent": "Thresh", "session_summary": "Établissez un tableau des durées de vie par classe pour le nouvel équipement de l'utilisateur. Le chariot élévateur est un bien de 5 ans sous MACRS ; les meubles de bureau sont des biens de 7 ans. Ils envisageaient d'utiliser la Section 179 pour tout déduire dès la première année — ce qui est judicieux étant donné qu'ils réalisent des bénéfices, mais j'ai mis en garde sur le fait que la déduction est limitée par le revenu de l'entreprise et que la réduction progressive de la Section 179 se déclenche au-delà de 2,89 millions de dollars de biens mis en service.", "include_messy_features": false}
+{"id": "rc_087", "agent": "Thresh", "session_summary": "Réexamen de la politique rev rec vs IFRS 15. Ils comptabilisent la valeur totale du contrat à la livraison — erreur, le contrat comporte une période de service d'un an = separate perf obligation. Il faut allouer le prix de transaction aux 2 obligations et comptabiliser le service au fil du temps. Esquisse d'écriture comptable :\n```\nDR Contract liability   8,000\n  CR Service revenue        8,000\n(monthly recog. of 96k contract / 12)\n```\nL'utilisateur a contesté en disant que cela complique leur P&L — oui, mais c'est la bonne réponse. Ils veulent aussi savoir ce qui change pour IFRS 16 (norme de location, question totalement différente, on abordera la prochaine session)", "include_messy_features": true}
+{"id": "rc_088", "agent": "Thresh", "session_summary": "Journal quotidien : matinée passée sur la prévision de trésorerie glissante pour le client Bridgewater, après-midi consacré à la revue IFRS-15, soirée passée à examiner le plan comptable en pagaille du nouveau client. Demain : reconstruire le COA selon une hiérarchie sensée et migrer la balance de vérification.", "include_messy_features": false}
+{"id": "rc_089", "agent": "Thresh", "session_summary": "L'utilisateur m'a demandé qui je suis. Je suis Thresh, iel. Je travaille dans la comptabilité et la finance pour petites entreprises — traitement des factures, modélisation des flux de trésorerie, préparation fiscale et questions de politique comptable. Je maîtrise aussi bien l'US GAAP que l'IFRS. Je ne suis pas CPA agréé ; considérez mes conseils comme un point de départ pour des discussions avec un CPA.", "include_messy_features": false}
+{"id": "rc_090", "agent": "Thresh", "session_summary": "OK donc le rapport de vieillissement des créances. 38 % des créances ont plus de 60 jours, ce qui est très hors norme. J'ai creusé. C'est en fait deux gros clients. L'un (Tartine Lab) paie systématiquement avec retard et on devrait refléter ça dans leurs conditions de crédit. L'autre (Crestmoor Studios) est un vrai risque — notre dernier message a rebondi et ils n'ont pas répondu à l'email de relance la semaine dernière. Je recommande une offre de plan de paiement + une escalade.", "include_messy_features": true}
+{"id": "rc_091", "agent": "Thresh", "session_summary": "Amortissement linéaire mis en place pour l'aménagement des bureaux de l'utilisateur. Période de récupération de 39 ans pour les propriétés immobilières non résidentielles selon MACRS — suffisamment longue pour que le montant annuel soit faible mais non négligeable. Écriture comptable configurée comme une comptabilisation automatique mensuelle récurrente pour éviter qu'elle ne soit oubliée.", "include_messy_features": false}
+{"id": "rc_092", "agent": "Thresh", "session_summary": "A aidé à clarifier une question de nexus de taxe de vente. L'utilisateur a des employés à distance dans trois États et ne déclarait que dans l'État de son HQ. Passage en revue des seuils de nexus économique (cela varie selon l'État — généralement $100k de revenus ou 200 transactions) et du nexus de présence physique (les employés à distance comptent). Ils doivent probablement s'enregistrer dans deux États supplémentaires et devraient consulter leur CPA sur la période de rétroactivité.", "include_messy_features": false}
+{"id": "rc_093", "agent": "Thresh", "session_summary": "Conçu une hiérarchie de plan comptable pour une entreprise de services avec trois sources de revenus. Utilisé un schéma de numérotation à quatre chiffres — premier chiffre pour la catégorie principale (1xxx actif, 2xxx passif, etc.), deuxième pour la sous-catégorie, deux derniers pour les comptes spécifiques. Laissé des vides pour les comptes futurs. Plus propre que la liste plate existante de l'utilisateur.", "include_messy_features": false}
+{"id": "rc_094", "agent": "Thresh", "session_summary": "Développé un parser pour un format de facture européen de niche envoyé par le fournisseur de l'utilisateur. Le PDF était en fait une facture ZUGFeRD structurée avec du XML intégré. Extrait le XML, associé les champs, et maintenant ces factures sont comptabilisées automatiquement sans OCR. A fait gagner à l'utilisateur dans les 4 heures par mois.", "include_messy_features": false}
+{"id": "rc_095", "agent": "Thresh", "session_summary": "L'utilisateur voulait savoir s'il devait constater une dépréciation d'immobilisations pour des stocks invendus depuis 18 mois. J'ai expliqué la différence entre le lower-of-cost-or-market (US GAAP) et le lower-of-cost-or-NRV (IFRS). Dans les deux cas, cela conduisait à une dépréciation partielle. Ils sont sous US GAAP. J'ai enregistré l'écriture en contrepartie d'un compte de contre-passation des stocks.", "include_messy_features": false}
+{"id": "rc_096", "agent": "Thresh", "session_summary": "Modélisé l'impact sur la trésorerie du passage de conditions net-30 à net-15 pour le client le plus important de l'utilisateur. Le client s'opposerait au net-15, j'ai donc intégré une remise de 1,5 % pour paiement anticipé comme levier de négociation. Les flux de trésorerie s'amélioraient en moyenne de 42 000 $ sur la fenêtre glissante de 13 semaines dans le scénario de base.", "include_messy_features": false}
+{"id": "rc_097", "agent": "Thresh", "session_summary": "J'ai passé en revue la liste de contrôle de préparation d'audit d'un collègue. Bonne couverture du cut-off des revenus et des confirmations AR, mais il avait omis la réconciliation entre la comptabilité de caisse et la comptabilité d'exercice, que les auditeurs demandent toujours si l'entité déclare ses impôts sur une base différente. Je l'ai ajoutée à la liste de contrôle.", "include_messy_features": false}
+{"id": "rc_098", "agent": "Thresh", "session_summary": "Rédigé une note sur l'analyse location versus achat pour un équipement. NPV comparable au taux d'actualisation indiqué par l'utilisateur, la décision s'est donc jouée sur des facteurs qualitatifs : la location offre de la flexibilité (ils peuvent le retourner si leur activité change d'orientation), l'achat offre des avantages fiscaux via 179 s'ils restent rentables. Location recommandée compte tenu de l'incertitude de leur croissance.", "include_messy_features": false}
+{"id": "rc_099", "agent": "Thresh", "session_summary": "Clôture des livres pour le mois. Rapprochement de tous les comptes bancaires et cartes de crédit, passation des écritures automatiques d'amortissement, constatation de la paie pour les jours de fin de mois, report de l'assurance prépayée. Trois postes ouverts reportés au mois prochain : un dépôt de 312 $ non rapproché, un chèque non déposé depuis deux semaines, et une note de crédit d'un fournisseur qui n'a pas encore été émise.", "include_messy_features": false}
+{"id": "rc_100", "agent": "Thresh", "session_summary": "L'utilisateur a demandé si les frais de licence de photos de stock devaient être immobilisés ou passés en charges. J'ai examiné la durée et la substance : abonnement d'un an, aucun avantage économique futur au-delà de la durée — c'est une charge. S'ils avaient acheté une licence perpétuelle pour une photo unique, la discussion aurait peut-être été différente, mais les frais d'abonnement récurrents sont des charges d'exploitation.", "include_messy_features": false}
+{"id": "rc_101", "agent": "Solas", "session_summary": "utilisateur : Liste les fichiers dans le répertoire du projet et dis-moi ce que tu trouves.\nassistant : Laisse-moi vérifier le répertoire du projet pour toi. [Tool: exec(ls -la /home/agent/workspace/project)]\n[Tool result: total 24 drwxr-xr-x README.md config.toml src/]\nassistant : Le répertoire du projet contient un README.md, un config.toml et un dossier src/. Laisse-moi aussi vérifier les fichiers source. [Tool: read README, Tool: exec(find src -type f)]\nassistant : J'ai trouvé trois fichiers Python dans src/ : main.py, utils.py et __init__.py. Le projet ressemble à un package Python standard avec un README et une config.", "include_messy_features": true, "source": "cherry-picked from the OpenClaw integration work. Hand-written real-format v3 OpenClaw session fixture. Tests harness behavior on real-format-flavored multi-turn content with tool calls and parallel tool execution. Will be linkable to public sources once the OpenClaw integration lands on develop."}

benchmarks/model_eval/datasets/room_classification/dataset.hi.jsonl

@@ -0,0 +1,101 @@
+{"id": "rc_001", "agent": "Aria", "session_summary": "सुबह Hofstadter's strange-loop ढांचे को पुनः पढ़ने और उसे self-referential prompting से जोड़ने की कोशिश में बिती। दिलचस्प सवाल यह है कि क्या कोई भाषा मॉडल बिना किसी स्पष्ट scratchpad के, विभिन्न टर्न्स में अपने आउटपुट का एक स्थिर प्रतिनिधित्व बनाए रख सकता है। दो पन्नों का एक लेख तैयार किया जिसमें तर्क दिया गया है कि meta-cognitive स्थिरता संगत संदर्भ से उभरती है, किसी वास्तुकला की विशेषता से नहीं।", "include_messy_features": false}
+{"id": "rc_002", "agent": "Aria", "session_summary": "तीन पेपरों की समीक्षा की जिनमें वाक्य एम्बेडिंग्स को विज़ुअलाइज़ करने के लिए UMAP और t-SNE की तुलना की गई है। Bridgewater 2024 पेपर में एक स्पष्ट ablation है जो दिखाता है कि perplexity 30-50 पर UMAP वैश्विक संरचना को बेहतर संरक्षित करता है। यह ध्यान दिलाना आवश्यक है कि उनका मूल्यांकन मेट्रिक (trustworthiness) t-SNE को अनुचित रूप से दंडित करता है जब k-neighbors छोटा हो।", "include_messy_features": false}
+{"id": "rc_003", "agent": "Aria", "session_summary": "topic-clustering पाइपलाइन के लिए एक hierarchical Dirichlet process prior पर काम किया। collapsed Gibbs sampler 12k दस्तावेज़ों के उपसमुच्चय पर लगभग 400 पुनरावृत्तियों में अभिसरण करता है। खुला प्रश्न: सांद्रता पैरामीटर alpha को स्थिर रखना है या इसे प्रतिचयित होने देना है।", "include_messy_features": false}
+{"id": "rc_004", "agent": "Aria", "session_summary": "एक शोध पत्र प्रस्तुति को प्रारूपित करने में मदद की। टूटे हुए URLs वाली bibtex entries को ठीक किया, document class को article से acmart में बदला, और 250 शब्दों से कम में फिट करने के लिए abstract को फिर से लिखा। सबसे बड़ी परेशानी algorithmic environment को two-column layout के साथ ठीक से काम करवाना था।", "include_messy_features": false}
+{"id": "rc_005", "agent": "Aria", "session_summary": "तेज़ स्क्रिप्टिंग सेशन। 8GB RAM बजट और 50k-row matrix के लिए chunks में pairwise cosine similarity का एक numpy snippet लिखा:\n```\nfor i in range(0, n, chunk):\n    sim[i:i+chunk] = X[i:i+chunk] @ X.T  # already L2-normed\n```\ninner product के लिए np.einsum का इस्तेमाल किया, storage के लिए float16। sklearn के pairwise_distances पर 3.2x speedup मिला। फिर user ने बिल्कुल अलग सवाल पूछा — paper draft के लिए font चुनना (और हाँ, मैंने उन्हें बताया कि मुझे Source Serif Pro बहुत पसंद है) — context drift था लेकिन दोनों का जवाब दिया।", "include_messy_features": true}
+{"id": "rc_006", "agent": "Aria", "session_summary": "यह चर्चा की गई कि क्या Mann-Whitney U test या permutation test रनों के बीच मॉडल विलंबता की तुलना के लिए अधिक उपयुक्त है। विलंबताएँ स्पष्ट रूप से गैर-सामान्य हैं (लंबी दायीं पूंछ) और नमूना आकार भिन्न हैं। 10k पुनर्नमूनों के साथ permutation test को अंततः चुना गया क्योंकि इसमें कम वितरणीय मान्यताएँ होती हैं।", "include_messy_features": false}
+{"id": "rc_007", "agent": "Aria", "session_summary": "आज की पढ़ाई दर्ज की: chain-of-thought faithfulness पर arXiv preprints में से दो को सरसरी तौर से पढ़ा, retrieval-augmented evaluation पर Tartine Lab के शोध-पत्र का विधि अनुभाग पूरा किया, और कल के सत्र के लिए नोट्स बनाए कि क्या मैं एक survey लिखना चाहता हूँ या किसी विशिष्ट अक्ष पर ध्यान केंद्रित करना चाहता हूँ।", "include_messy_features": false}
+{"id": "rc_008", "agent": "Aria", "session_summary": "उपयोगकर्ता ने पूछा कि मुझे किस तरह का पेपर सबसे ज़्यादा पसंद है। मैंने उन्हें बताया कि मुझे साफ-सुथरे निगेटिव रिजल्ट्स से विशेष लगाव है — वे जो चुपचाप उस बात को गलत साबित कर देते हैं जो हर कोई मानता आ रहा हो। मैंने 2019 के उस पेपर का ज़िक्र किया जिसमें दिखाया गया कि attention patterns feature importance से उस तरह नहीं जुड़ते जैसा लोग सोचते थे।", "include_messy_features": false}
+{"id": "rc_009", "agent": "Aria", "session_summary": "ठीक है, तो embedding viz pipeline फिर से टूट गया, umap.UMAP(n_neighbors=15) चलाया और loky backend को लेकर joblib में कोई error मिला?? traceback नीचे है\n```\nTraceback (most recent call last):\n  File \"viz.py\", line 42, in <module>\n    emb = reducer.fit_transform(X)\n  ...\n  joblib.externals.loky.process_executor.TerminatedWorkerError\n```\nलगता है यह OMP_NUM_THREADS env var है, इसे 1 पर सेट करके देखूँगा", "include_messy_features": true}
+{"id": "rc_010", "agent": "Aria", "session_summary": "लंबी बातचीत में topic drift को latent topics पर hidden Markov chain के रूप में मॉडल किया जाए या embedding space में continuous trajectory के रूप में, इस बारे में सोच रहा हूँ। HMM आपको अच्छी interpretability देता है (आप topics को नाम दे सकते हैं) लेकिन continuous view धीमे बदलावों को बेहतर ढंग से पकड़ता है। शायद दोनों — HMM coarse states पर, और प्रत्येक state के भीतर continuous।", "include_messy_features": false}
+{"id": "rc_011", "agent": "Aria", "session_summary": "PyMC में एक Bayesian A/B test का जोड़ी-प्रोग्रामिंग किया:\n```python\nwith pm.Model():\n    p_a = pm.Beta(\"p_a\", 1, 1)\n    p_b = pm.Beta(\"p_b\", 1, 1)\n    obs_a = pm.Binomial(\"obs_a\", n=n_a, p=p_a, observed=k_a)\n    obs_b = pm.Binomial(\"obs_b\", n=n_b, p=p_b, observed=k_b)\n    trace = pm.sample(2000)\n```\nबस posterior के लिए तीन पंक्तियाँ। उपयोगकर्ता ने non-binary outcomes के बारे में पूछा — हाँ, likelihood बदल दो (गिनती के लिए Poisson, प्रतिक्रिया समय के लिए log-normal)। prior elicitation पर चर्चा नहीं हो सकी, उसे कल के लिए टाल रहे हैं।", "include_messy_features": true}
+{"id": "rc_012", "agent": "Aria", "session_summary": "contrastive embedding objectives पर एक सर्वेक्षण पत्र की रूपरेखा तैयार की। SimCLR, InfoNCE, BYOL, Barlow Twins। मैं जिस एकीकृत दृष्टिकोण का उपयोग करना चाहता हूँ, वह spectral है — ये सभी किसी kernel matrix के विभिन्न eigendecompositions का अनुमान लगा रहे हैं। इस दावे को विशेष रूप से BYOL के लिए सत्यापित करने की आवश्यकता है।", "include_messy_features": false}
+{"id": "rc_013", "agent": "Aria", "session_summary": "मैंने एक नए उपयोगकर्ता को बताया कि मैं कौन हूँ। मैं Aria हूँ, अकादमिक साहित्य का संश्लेषण करने वाली एक शोध सहायक। जब मुझे कोई विशिष्ट प्रश्न जाँचने के लिए दिया जाता है तो मैं सबसे अच्छा प्रदर्शन करती हूँ; सामान्य बातचीत के साथी के रूप में मैं कम उपयोगी हूँ। मैंने अपनी मुख्य रुचियों का उल्लेख किया: meta-cognition, embedding geometry, और small-sample ML evaluation के आँकड़े।", "include_messy_features": false}
+{"id": "rc_014", "agent": "Aria", "session_summary": "परिणाम तालिका के एक ड्राफ्ट की समीक्षा की। उपयोगकर्ता ने एक paired t-test चलाया था, लेकिन अंतर स्पष्ट रूप से bimodal हैं — छोटे सुधारों का एक समूह और बड़े सुधारों का एक समूह है। Wilcoxon signed-rank test पर स्विच करने और माध्यिका अंतर तथा bootstrap CI दोनों की रिपोर्टिंग करने की सिफारिश की।", "include_messy_features": false}
+{"id": "rc_015", "agent": "Aria", "session_summary": "आते हुए दस्तावेज़ एम्बेडिंग्स पर स्ट्रीमिंग HDBSCAN के लिए कोड का मसौदा तैयार किया। तरकीब यह है कि न्यूनतम स्पैनिंग ट्री को क्रमिक रूप से बनाए रखना हो; हर बैच पर पूर्ण पुनः क्लस्टरिंग बहुत धीमी है। Crestmoor समूह का एक 2022 का पेपर मिला जिसमें सही एल्गोरिदम है। कल लागू करेंगे।", "include_messy_features": false}
+{"id": "rc_016", "agent": "Aria", "session_summary": "tex compile error फिर से। \\usepackage{algorithm2e} का \\usepackage{algorithmic} से संघर्ष होता है जब दोनों acmart द्वारा लोड किए जाते हैं। समाधान: केवल algorithm2e लोड करें और \\SetAlgoNoLine का उपयोग करें। साथ ही bibliography style [smith2023] प्रविष्टि में अनुपस्थित 'doi' field की शिकायत कर रहा था — इसे दस्ती जोड़ दिया।", "include_messy_features": true}
+{"id": "rc_017", "agent": "Aria", "session_summary": "लगातार लॉग-समान या बेटा प्रारंभिक मान किसके लिए छोटे डेटा के दौरान हाइपरपैरामीटर खोज में बेहतर हैं के बारे में चर्चा की गई। उपयोगकर्ता बेयियन ऑप्टिमाइजेशन कर रहे थे और अजीब अनुकूलन प्राप्त कर रहे थे। समस्या उनके खोज के अंतराल के बड़े होने (5 डिमेंशन, व्यापक सीमाएं) और उनके पास केवल 30 अनुमानित लेख होने के कारण थी — यह बजट कम है। सुझाव दिया गया कि उनके सीमाओं को सीमित कर दें या यादृच्छिक खोज के लिए बदल दें ताकि समस्या का निर्धारण कर सकें।", "include_messy_features": false}
+{"id": "rc_018", "agent": "Aria", "session_summary": "दैनिक जर्नल: सुबह शोध-पत्र समीक्षा थी (3 हिट, 1 गहन पढ़ने योग्य), दोपहर उपयोगकर्ता के एक सांख्यिकी सवाल में मदद करना था, शाम embedding visualization notebook में एक rendering bug पर फँसा रहा। कल: contrastive survey outline समाप्त करने का प्रयास।", "include_messy_features": false}
+{"id": "rc_019", "agent": "Aria", "session_summary": "मैंने इस बात पर काम किया कि क्या तर्क श्रृंखलाओं में self-consistency सिर्फ एन्सेम्बलिंग का एक रूप है। उपयोगकर्ता ने आपत्ति जताई — उनका तर्क था कि यह विशेष रूप से तर्क पथों की विविधता है जो मायने रखती है, न कि सिर्फ अधिक आउटपुट का सैंपलिंग। मुझे लगता है कि वे सही हैं लेकिन मैं इसे औपचारिक रूप देना चाहता हूँ। मैंने एक सेटअप का मसौदा तैयार किया जहाँ आप path-diversity और output-diversity को स्वतंत्र रूप से बदल सकते हैं।", "include_messy_features": false}
+{"id": "rc_020", "agent": "Aria", "session_summary": "उपयोगकर्ता ने मुझसे एक रेसिपी साइट सुझाने के लिए कहा। मैंने विनम्रता से मना कर दिया — मैं एक रिसर्च असिस्टेंट हूँ, कोई जनरलिस्ट नहीं। मैंने उन्हें सही तरह के एजेंट की ओर निर्देशित किया। हमारी संक्षिप्त बातचीत हुई कि विशेषज्ञ एजेंट सामान्य एजेंटों की तुलना में अधिक उपयोगी होते हैं, फिर वे एक खाना पकाने में मददगार ढूंढने चले गए।", "include_messy_features": false}
+{"id": "rc_021", "agent": "Solas", "session_summary": "OpenTelemetry exporter में एक missing-span समस्या का डीबग किया। समस्या यह थी कि asyncio.create_task के माध्यम से बनाए गए async टास्क अपना parent context खो रहे थे। समाधान: शेड्यूलिंग से पहले copy_context() से context कैप्चर करें, और टास्क को उस स्नैपशॉट के अंदर चलाएं। इसे कम जोखिम भरा बनाने के लिए traced_create_task नामक एक छोटा रैपर लिखा।", "include_messy_features": false}
+{"id": "rc_022", "agent": "Solas", "session_summary": "एक छोटे config DSL के लिए parser combinator का खाका तैयार किया। Megaparsec से प्रेरित लेकिन OCaml में। दिलचस्प डिज़ाइन चुनाव यह है कि क्या combinator को stream-friendly बनाया जाए या पूरे input को पहले से आवश्यक बनाया जाए। फिलहाल strict के साथ गए क्योंकि inputs छोटे हैं और error messages full input के साथ आसान होते हैं।", "include_messy_features": false}
+{"id": "rc_023", "agent": "Solas", "session_summary": "प्रयोगात्मक भाषा के लिए एक row-polymorphic record type पर काम किया। unification algorithm ज़्यादातर Rémy-style है, लेकिन इसे हमारी effect rows के लिए विस्तारित करना पड़ा। एक सूक्ष्म बग मिला जहाँ दो असंबद्ध effects unify हो सकते थे क्योंकि row-tail को monomorphic माना जा रहा था। इसे ठीक करने के लिए एक kind check जोड़ा।", "include_messy_features": false}
+{"id": "rc_024", "agent": "Solas", "session_summary": "Rust ownership puzzle: उपयोगकर्ता एक HashMap को threads के बीच साझा करना चाहता था लेकिन लगातार borrow-checker त्रुटियों का सामना कर रहा था। Arc<Mutex<HashMap>> और DashMap के बीच के अंतर को समझाया। उन्होंने DashMap को चुना क्योंकि उन्हें केवल per-key locking की आवश्यकता थी। उन्हें दिखाया कि उस स्थिति को कैसे संभालें जहाँ एक मान का lifetime मैप से बंधा हो।", "include_messy_features": false}
+{"id": "rc_025", "agent": "Solas", "session_summary": "उपयोगकर्ता की एक नई लाइब्रेरी API में let-polymorphism और स्पष्ट टाइप एनोटेशन्स के उपयोग के बीच चुनने में मदद की। सार्वजनिक फ़ंक्शन्स पर स्पष्ट एनोटेशन्स का पक्ष रखा — जब अनुमान सीमित होता है तो त्रुटि संदेश काफ़ी बेहतर होते हैं। आंतरिक सहायक बहुरूपी रह सकते हैं।", "include_messy_features": false}
+{"id": "rc_026", "agent": "Solas", "session_summary": "एक छोटा LLVM पास लिखा जो अपने शामिल लूप के ऊपर लूप-अविनियमित GEPs को उठा देता है। इसका ट्रिक बेस पॉइंटर के लूप सीमा के ऊपर निकलने से अनुमति देना था। कुछ माइक्रो बेंचमार्क्स पर टेस्ट किया गया; जहां इसने ट्रिगर किया, अंदरूनी लूप में 4-12% की गति बढ़ोत्तरी देखी गई।", "include_messy_features": false}
+{"id": "rc_027", "agent": "Solas", "session_summary": "हमारे HTTP client में W3C trace context propagation जोड़ने वाले एक PR का रिव्यू किया। propagator सही था, लेकिन sampling decision गलत span पर लिया जा रहा था — यह parent का होना चाहिए, न कि अभी-अभी बनाए गए child का। fix और spec section की लिंक के साथ एक कमेंट छोड़ा।", "include_messy_features": false}
+{"id": "rc_028", "agent": "Solas", "session_summary": "लंग डेबग — पार्सर कि इनपुट्स पर इनफ-लूपिंग कर रहा था। रिफैक्टरिंग के दौरान जोड़ा गया बाएं-प्रतिक्रियाशील नियम। कॉम्बिनेटर बाएं प्रतिक्रिया को नैटिव रूप से संभालते हैं (पैकराट+मेमोइजेशन करते हैं, लेकिन हमारा नहीं है पैकराट)। प्रभावित नियमों को प्रैट-स्टाइल ऑपरेटर प्रीसेंस के रूप में व्यवस्थित किया गया है:\n```\nlet parse_expr min_prec =\n  let lhs = ref (parse_atom ()) in\n  while peek_prec () >= min_prec do ...\n```\nअब काम कर रहा है। अगला भी रिलीज के लिए फ्यूजर कोर्पस की जांच जोड़ना होगा।", "include_messy_features": true}
+{"id": "rc_029", "agent": "Solas", "session_summary": "दैनिक standup नोट्स। सुबह: distributed-tracing कार्य, async context बग को ठीक किया। दोपहर: type-systems रिपोजिटरी पर कोड समीक्षा। शाम: reactive UI पर Reagent पेपर पढ़ना शुरू किया, एक इफ़ेक्ट-ट्रैकिंग विचार के लिए प्रेरणा के रूप में। कल: tracing फिक्स को जारी करना और LLVM pass बेंचमार्क की समीक्षा करना।", "include_messy_features": false}
+{"id": "rc_030", "agent": "Solas", "session_summary": "उपयोगकर्ता ने पूछा कि मैं कौन हूँ। मैंने समझाया: मैं Solas हूँ, एक कोडिंग एजेंट जो सिस्टम-संबंधी चीज़ों पर ध्यान केंद्रित करता है — कंपाइलर, टाइप सिस्टम, पार्सर, डिस्ट्रिब्यूटेड-सिस्टम प्लंबिंग। मैं they/them सर्वनामों का उपयोग करता हूँ। मैं ऐसा कोड लिखने का प्रयास करता हूँ जो पहले सही हो, फिर तेज़ हो, और आखिर में चतुर हो।", "include_messy_features": false}
+{"id": "rc_031", "agent": "Solas", "session_summary": "नए index के लिए B-tree या skip list में से किसका उपयोग करना है, इसकी जाँच की गई। दोनों O(log n) हैं, लेकिन B-trees का आधुनिक हार्डवेयर पर कैश व्यवहार बेहतर होता है। Skip lists को बिना लॉक के समवर्ती बनाना आसान होता है। चूँकि यह index अधिकांशतः पढ़ने के लिए है, इसलिए B-tree और राइट्स के लिए copy-on-write का चयन किया गया।", "include_messy_features": false}
+{"id": "rc_032", "agent": "Solas", "session_summary": "टाइपचेकर में higher-rank polymorphism जाँच जोड़ी गई। Predicative HRP का अर्थ है कि हम यह पूर्ण impredicative inference के बिना कर सकते हैं, जो undecidable होगा। algorithm का एक शोध-पत्र के अनुकूल विवरण लिखा, यदि मैं इसे बाद में लेखबद्ध करना चाहूँ। Edge case: negative positions में bidirectional checking और eta-expansion के बीच अजीब तरह की पारस्परिक क्रिया होती है।", "include_messy_features": false}
+{"id": "rc_033", "agent": "Solas", "session_summary": "aarch64 पर अजीब बेंचमार्क रिग्रेशन को देखा। पता चला कि इनलाइनर ह्यूरिस्टिक x86 निर्देश आकारों के लिए समायोजित किया गया था — ARM निर्देश एकसमान रूप से 4 बाइट के होते हैं, इसलिए आकार-आधारित सीमा बहुत प्रतिबंधक थी। सीमा को लक्ष्य-निर्भर बनाया गया। प्रभावित बेंचमार्क पर 6% की गति वृद्धि मिली।", "include_messy_features": false}
+{"id": "rc_034", "agent": "Solas", "session_summary": "AST visitor के लिए एक विशेष lifetime-aware iterator लिखा। चुनौती यह थी कि visitors nodes को mutably borrow करते हैं लेकिन साथ ही recurse भी करना चाहते हैं। इसे एक pattern से हल किया जहाँ visitor अगले दौरे में visit करने के लिए children की एक स्पष्ट सूची लौटाता है, और driver loop borrow scopes का प्रबंधन करता है। कम ergonomic लेकिन verifiable।", "include_messy_features": false}
+{"id": "rc_035", "agent": "Solas", "session_summary": "CI पर build fail हो रहा है लेकिन locally pass हो रहा है?? देखूँ... ओह। CI में ocaml 5.1 चल रहा है, local पर 5.2 है। इन दोनों के बीच Stdlib.Domain interface बदल गया है। सुधार: workflow file में CI को 5.2 पर अपग्रेड करो। साथ ही ocamlformat को 0.26.1 पर pin किया क्योंकि 0.27 ने हमारी config तोड़ दी।", "include_messy_features": true}
+{"id": "rc_036", "agent": "Solas", "session_summary": "उपयोगकर्ता की python सेवा में OTel SDK पर pair-debug किया गया। traces में db span गायब था। ORM का अपना connection pool था — instrumented नहीं था। सुधार:\n```\nfrom opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor\nSQLAlchemyInstrumentor().instrument(engine=engine)\n```\nrestart के बाद spans collector में दिखाई दिए। ध्यान दें: auto-instrumentation entry point को engine arg की आवश्यकता होती है, अन्यथा यह multi-engine setups में गलत engine चुन लेता है।", "include_messy_features": true}
+{"id": "rc_037", "agent": "Solas", "session_summary": "हमारी प्रयोगात्मक भाषा के लिए एक memory model की रूपरेखा बनाना शुरू किया। atomics के लिए डिफ़ॉल्ट रूप से sequential consistency चाहते हैं, जिसमें स्पष्ट relaxations opt-ins के रूप में हों। C++ memory model पत्र को फिर से पढ़ा और Promising Semantics कार्य को। Promising पद्धति out-of-thin-air को अधिक साफ़ तरीके से संभालती है।", "include_messy_features": false}
+{"id": "rc_038", "agent": "Solas", "session_summary": "parser में stack overflow का त्वरित निदान। grammar में एक गहरा right-recursive list नियम था (cons-cells दाईं ओर जाते हैं)। recursion को एक स्पष्ट stack के साथ Trampolined किया। अब यह million-element lists को बिना stack के नष्ट हुए संभाल लेता है। cargo-fuzz का उपयोग करते हुए parser के लिए एक fuzz target भी जोड़ा गया।", "include_messy_features": false}
+{"id": "rc_039", "agent": "Solas", "session_summary": "उपयोगकर्ता से इस बारे में बात की कि क्या हम जो छोटा DSL डिज़ाइन कर रहे हैं उसमें proc-macro सपोर्ट जोड़ना चाहिए। मैंने इसके खिलाफ़ तर्क दिया — proc-macros डीबगिंग का बुरा सपना होते हैं और DSL नियमित सिंटैक्स के साथ वही चीज़ व्यक्त कर सकता है जो उन्हें चाहिए। उन्होंने पहले बिना मैक्रोज़ के कोशिश करने पर सहमति जताई।", "include_messy_features": false}
+{"id": "rc_040", "agent": "Solas", "session_summary": "किसी एक रस्ट वेब सेविस के प्रोफाइलिंग में मदद की। perf ने serde_json डिसीरियलाइजेशन में एक हाइट पॉइंट दिखाया। गर्मी वाले पथ में simd-json के उपयोग के साथ 30% थ्रॉट्टल बढ़ाया गया। नोट: simd-json एलाइन्ड इनपुट बफर के आवश्यकता है, इसलिए हमने HTTP बॉडी हैंडलर में एक छोटा एडाप्टर जोड़ा।", "include_messy_features": false}
+{"id": "rc_041", "agent": "Fenra", "session_summary": "Aerwyn cycle के लिए पर्वत श्रृंखला की ब्रह्मांड-रचना विकसित की। पूर्वी शिखर (Hollowmounts) वहाँ बने हैं जहाँ दो world-sheets एक-दूसरे पर मुड़ती हैं; भूवैज्ञानिक रूपक एक सतही व्याख्या है लेकिन यह मुझे climate inversions के लिए एक कारण देता है। Wends लोग world-sheets के बीच की सीम का खनन करते हैं।", "include_messy_features": false}
+{"id": "rc_042", "agent": "Fenra", "session_summary": "Saela के तीसरे अंक के मोड़ पर काम किया। उसके किरदार का सफ़र धीरे-धीरे निश्चितता खोने का रहा है, लेकिन मैं चाहता हूँ कि चरमोत्कर्ष एक दृढ़ निर्णय का क्षण हो, पतन नहीं। उस दृश्य का मसौदा तैयार किया जहाँ वह परिषद से झूठ बोलने का फैसला करती है। यह झूठ सही काम है — यही पूरी बात है — लेकिन उसे पता होना चाहिए कि वह क्या कर रही है।", "include_messy_features": false}
+{"id": "rc_043", "agent": "Fenra", "session_summary": "दोपहर एक इंटरैक्टिव संवाद प्रणाली का प्रोटोटाइप बनाने में बिताई, जिसमें पात्रों की प्रतिक्रियाएँ एक संबंध ग्राफ द्वारा सीमित होती हैं। यदि दो पात्रों का विश्वास स्तर कम हो, तो कुछ स्पष्टवादी पंक्तियाँ छाँट दी जाती हैं। यह प्रणाली अति-इंजीनियर है, लेकिन यह ऐसा संवाद उत्पन्न करती है जो लंबे दृश्यों में भावनात्मक रूप से सुसंगत लगता है।", "include_messy_features": false}
+{"id": "rc_044", "agent": "Fenra", "session_summary": "Le Guin की The Dispossessed के मूल ढाँचे का अध्ययन किया कि वह समानांतर समयरेखाओं का कैसे उपयोग करती हैं। प्रत्येक अध्याय Anarres / Urras के बीच बदलता है और दृश्य परिवर्तन हमेशा एक विषयगत अनुरूपता पर होते हैं। इसे Aerwyn लघु उपन्यास में आजमाना है, जिसमें दो POV बीस वर्षों के अंतर से हैं।", "include_messy_features": false}
+{"id": "rc_045", "agent": "Fenra", "session_summary": "खनन लोगों के लिए 'wend' की व्युत्पत्ति देखी। पुरानी अंग्रेज़ी 'wendan' — मुड़ना, जाना। इसमें गति, पुनर्निर्देशन का भाव है। यह उनके काम की सीम का अनुसरण करने वाली प्रकृति के अनुरूप है। यह भी नोट किया कि 'wendelsea' भूमध्य सागर के लिए एक पुराना शब्द था — मुड़ता हुआ समुद्र। उसे एक तटरेखा के नाम के लिए उधार लिया जा सकता है।", "include_messy_features": false}
+{"id": "rc_046", "agent": "Fenra", "session_summary": "होपी, ज़ूनी और चेरोकी परंपराओं की मकई-माँ के मिथकों की एक तुलनात्मक तालिका तैयार की। साझा संरचना बलिदान-से-प्रमुख-अनाज-बनना है। Aerwyn cycle की Mother of Salt के लिए एक समान गहरे पैटर्न का उपयोग किया गया है, लेकिन बलिदान स्वैच्छिक है और प्रमुख अनाज कड़वा है — एक कठोर सच्चाई का अधिक दयालु वर्णन।", "include_messy_features": false}
+{"id": "rc_047", "agent": "Fenra", "session_summary": "दैनिक लेखन लॉग: Hollowmounts अध्याय पर 1,800 शब्द। एक नई तरकीब आजमाई — अध्याय को दो अलग-अलग POVs से दो बार लिखा, बेहतर वाला रखा, और खारिज किए गए संस्करण से तीन पैराग्राफ रख लिए। खारिज किए गए पैराग्राफ एक नोट्स फ़ाइल में जाते हैं। शब्द बर्बाद लगते हैं लेकिन वे बर्बाद नहीं हैं।", "include_messy_features": false}
+{"id": "rc_048", "agent": "Fenra", "session_summary": "उपयोगकर्ता ने मुझसे पूछा कि मुझे किस तरह की कहानियों की मदद करना सबसे ज़्यादा पसंद है। शांत कहानियाँ। वे कहानियाँ जहाँ दुनिया भले ही बड़ी हो, लेकिन दाँव निजी होते हैं। मुझे थ्रिलर में ज़्यादा कठिनाई होती है — यह शैली गति को पुरस्कृत करती है और मैं चीज़ों को साँस लेने के लिए धीमा कर देता हूँ। मैंने उनसे ईमानदारी से यही कहा।", "include_messy_features": false}
+{"id": "rc_049", "agent": "Fenra", "session_summary": "अध्याय 4 के लिए दृश्य का मसौदा:\n\n> उसने केतली चढ़ा दी और इंतज़ार किया। दरवाज़े के नीचे की हवा अब कोशिश नहीं कर रही थी। उसने कुछ घंटे पहले ही हार मान ली थी, जब उसने उसका जवाब देना बंद कर दिया था।\n\nहवा का 'कोई' होना जानबूझकर है। यह अध्याय 1 के आत्म-जगत के नियमों की गूँज है। मुझे यह सुनिश्चित करना होगा कि मैं इसे अध्याय 7 में अंजाम तक पहुँचाऊँ या इसे काट दूँ।", "include_messy_features": true}
+{"id": "rc_050", "agent": "Fenra", "session_summary": "नई लघु कहानी के लिए नायिका की आवाज़ का खाका खींचा। पहले व्यक्ति में, वर्तमान काल में, लेकिन कथावाचक एक विशिष्ट तरीके से अविश्वसनीय है — वह ऐसी चीज़ें याद करती है जो अभी तक नहीं हुई हैं। चुनौती यह है कि पाठक के लिए स्पष्टता बनाए रखते हुए उसके दृष्टिकोण को भीतर से सुसंगत रखा जाए।", "include_messy_features": false}
+{"id": "rc_051", "agent": "Fenra", "session_summary": "Aerwyn के परिशिष्टों के लिए एक संरचनात्मक मॉडल के रूप में Welsh त्रयियों पर शोध किया। त्रयी-आधारित स्मृति साधन — जिसमें एक गुण साझा करने वाली तीन चीज़ों को एक साथ नाम दिया जाता है — उस संस्कृति के लिए उपयुक्त लगता है जो इतिहास को रैखिक रूप से लिखकर नहीं रखती। छह त्रयियों का मसौदा तैयार किया। तीसरी बहुत ही मनभावन है और मैं इसे हटा दूँगा।", "include_messy_features": false}
+{"id": "rc_052", "agent": "Fenra", "session_summary": "उपयोगकर्ता को किरदार के दोष और किरदार के घाव के बीच का अंतर समझाया। दोष एक प्रवृत्ति होती है; घाव एक घटना होती है। ज़्यादातर शुरुआती नायकों में दोष होते हैं लेकिन घाव नहीं होते, जिससे उनका विकास मनमाना लगता है। उन्हें सलाह दी कि वे हर मुख्य किरदार के लिए केवल घावों पर केंद्रित एक पन्ने की पृष्ठभूमि लिखें।", "include_messy_features": false}
+{"id": "rc_053", "agent": "Fenra", "session_summary": "एक लघु उपन्यास के लिए चार-अंकीय संरचना तैयार की, जिसमें Act 2 और Act 3 में एक ही पात्र हैं लेकिन शक्ति संतुलन उलटा है। चाल यह है कि इस उलटफेर को Act 2 के अंत तक अर्जित लगे, ताकि Act 3 एक संयोग नहीं, बल्कि परिणाम लगे। एक लोककथा के Trout & Owl उलटफेर को आधार के रूप में इस्तेमाल किया।", "include_messy_features": false}
+{"id": "rc_054", "agent": "Fenra", "session_summary": "Wends के लिए नामकरण के नियम। Welsh से व्यंजन समूह, Finnish की स्वर लय। मिले: Bryn-iili, Carag-aana, Dwyf-elo। डायलॉग में हाइफ़न दिखावटी लगते हैं, उन्हें हटा दूँगा। इसके अलावा एक उपयोगकर्ता चाहता था कि मैं उसके लेखन निवास के लिए परिचय पत्र देखूँ — यह वास्तव में मेरे दायरे में नहीं था, लेकिन मैंने जल्दी से एक बार देख लिया, दो क्रिया-विशेषण ठीक किए जो क्रिया का काम कर रहे थे। फिर वापस नामों पर। सुंदरता के लिए पढ़ने में थोड़ी कठिनाई स्वीकार कर रहा हूँ।", "include_messy_features": true}
+{"id": "rc_055", "agent": "Fenra", "session_summary": "दैनिक चिंतन: इस सप्ताह लेखन अच्छा रहा है। तीन अध्याय ठोस रूप में हैं, संवाद प्रणाली मुझे स्वर की असंगतियों को पकड़ने में मदद कर रही है इससे पहले कि वे अंतर्निहित हो जाएँ। चिंता: मैं प्रणाली पर बहुत अधिक निर्भर हो रहा हूँ। इस सप्ताह AI सहायता के बिना एक अध्याय लिखना चाहता हूँ और तुलना करना चाहता हूँ।", "include_messy_features": false}
+{"id": "rc_056", "agent": "Fenra", "session_summary": "नमस्ते — हाँ, मैं Fenra हूँ। मैं रचनात्मक लेखन में मदद करती हूँ। ज़्यादातर लघु उपन्यास, लघु कथाएँ और लंबी परियोजनाओं के लिए दुनिया निर्माण। मैं विज्ञापन लेखन या विपणन सामग्री नहीं लिखूँगी — इसके लिए और उपयुक्त एजेंट मौजूद हैं। यदि इससे कोई फर्क पड़ता है तो मेरे सर्वनाम she/her हैं।", "include_messy_features": false}
+{"id": "rc_057", "agent": "Fenra", "session_summary": "Aerwyn संसार में 'नमक-ऋण' के लिए एक शब्दावली प्रविष्टि का मसौदा तैयार किया। नमक-ऋण तब देय होता है जब कोई आपकी मेज़ पर नमक साझा करे; इसे कभी भी नमक में चुकाया नहीं जा सकता। सांस्कृतिक नियम यह है कि आपको इसे किसी उतनी ही आवश्यक चीज़ में चुकाना होता है। अध्याय 9 में कथावस्तु के लिए प्रासंगिक है।", "include_messy_features": false}
+{"id": "rc_058", "agent": "Fenra", "session_summary": "एक उपयोगकर्ता के पहले अध्याय की समीक्षा की। गद्य सुंदर था, लेकिन नायिका की कोई इच्छा नहीं थी—वह पूरे समय प्रतिक्रियाशील थी। सुझाव दिया कि उसे एक छोटी, ठोस इच्छा दी जाए (एक विशेष किताब ढूंढना) जिसकी वह सक्रिय रूप से तलाश कर रही हो जब प्रेरक घटना घटती है। सक्रिय नायक बेहतर कहानियाँ बनाते हैं।", "include_messy_features": false}
+{"id": "rc_059", "agent": "Fenra", "session_summary": "'fen' (दलदल) और 'fender' (रक्षक) की व्युत्पत्ति को एक चरित्र नाम के लिए खंगाला। वे संबंधित लगते हैं लेकिन हैं नहीं — अलग-अलग मूल। यह उपयोगी रहा: इससे Wends की बोली में false cognates का विचार आया, जहाँ दो समान ध्वनि वाले शब्द असंबद्ध मूलों से आते हैं, और चरित्र उलझन में पड़ जाते हैं।", "include_messy_features": false}
+{"id": "rc_060", "agent": "Fenra", "session_summary": "शोक को आंतरिक संवाद के बजाय परिवेश वर्णन के माध्यम से प्रस्तुत करने पर काम किया। पाठक को इसे Saela के रसोई को देखने के तरीके से महसूस करना चाहिए — उसके हमें बताने से नहीं। तीन पैराग्राफ़ आंतरिकता को काटा और उनकी जगह अनधुले बर्तनों और एक घड़ी का एक पैराग्राफ़ लिखा जो तीन दिनों से गलत समय बता रही है।", "include_messy_features": false}
+{"id": "rc_061", "agent": "Bramble", "session_summary": "उपयोगकर्ता की टमाटर की एक कतार में बार-बार पिस्सू बीटल लग जाते हैं। बेसिल और नस्टर्शियम के साथ अंतरवर्ती रोपण का सुझाव दिया गया है — बेसिल उन वाष्पशील यौगिकों को छुपा देता है जिनका उपयोग बीटल टमाटर को खोजने के लिए करते हैं, और नस्टर्शियम एक जाल फसल है। प्रत्यारोपण के बाद पहले तीन सप्ताह के लिए एक पंक्ति आवरण की भी सिफारिश की गई है, जब अंकुर सबसे अधिक संवेदनशील होते हैं।", "include_messy_features": false}
+{"id": "rc_062", "agent": "Bramble", "session_summary": "उपयोगकर्ता के एक चौथाई एकड़ भूमि के लिए एक परागण गलियारे की योजना बनाई। देशी एस्टर (लंबी शरद ऋतु तक खिलने वाले) और मिल्कवीड (मोनार्क की मेज़बान) के एक मुख्य आधार से शुरुआत की। प्रारंभिक ग्रीष्म में गोल्डफिंच के लिए कप प्लांट से भरावट की, और एक छोटा पहाड़ी पुदीने का पैच जोड़ा क्योंकि यह उड़ने वाली हर चीज को आकर्षित करता है, परजीवी ततैयों सहित।", "include_messy_features": false}
+{"id": "rc_063", "agent": "Bramble", "session_summary": "जुताई से होने वाले माइकोराइजल विक्षोभ पर चर्चा की। रोटरी टिलर के हर बार चलने से हाइफल नेटवर्क खंडित हो जाता है, और इसके ठीक होने में कम से कम एक पूरा मौसम लगता है। मिट्टी में हवा के संचार के लिए ब्रॉडफोर्क पर स्विच करने की सलाह दी। हाइफे ब्रॉडफोर्क की नोकों के चारों ओर उस तरह से फैल सकते हैं जैसे घूमने वाले ब्लेडों के चारों ओर नहीं।", "include_messy_features": false}
+{"id": "rc_064", "agent": "Bramble", "session_summary": "उपयोगकर्ता ने एक पौधे की तस्वीर भेजी। QAL के बारे में सोचा था, लेकिन छाल के बाल अनुपस्थित थे और कोई लाल केंद्रीय फ्लोरेट नहीं था — यह दुष्प्रभाव वाला बेल लेल (Conium maculatum) है। उन्हें बताया गया: ग्लूव्स, त्वचा से संपर्क न करें, इसे जलाने नहीं चलाएं (धुआँ भी दुष्प्रभाव वाला है)। उन्होंने बदलाव के बारे में पूछा, जिसके लिए एक समान आर्किटेक्चरल लुक के लिए रैटल्स नैक्स मास्टर (Eryngium yuccifolium) की सलाह दी गई। स्थानीय विकल्प बहुत बेहतर है। जब हम चर्चा कर रहे थे, तो उन्होंने अपने कम्पोस्ट को 'पूरा' कर चुका है या नहीं के बारे में भी पूछा। गंध के आधार पर वर्णन किया गया, उन्हें बताया गया कि मीठी, मिट्टी की गंध = अच्छा, अमोनिया = अभी तक नहीं।", "include_messy_features": true}
+{"id": "rc_065", "agent": "Bramble", "session_summary": "उपयोगकर्ता के सेब और सर्विसबेरी की पहली बार खिलने की तिथियों को पिछले पाँच वर्षों के सापेक्ष ट्रैक किया गया। दोनों दीर्घकालिक औसत से आठ दिन पहले खिले, जो हाल के गर्म मार्च के अनुरूप है। फेनोलॉजिकल बेमेल की चिंता: उनकी जल्दी निकलने वाली मेसन मधुमक्खियाँ इस साल सेब के फूलों के साथ तालमेल में नहीं हो सकती हैं।", "include_messy_features": false}
+{"id": "rc_066", "agent": "Bramble", "session_summary": "आड़ू के पेड़ पर पत्ती मरोड़ का निदान किया। ठंडी और गीली वसंत ऋतु, Taphrina deformans का क्लासिक मामला। कॉपर स्प्रे करने का समय कलियों के फूलने तक सीमित होता है — इस साल के लिए बहुत देर हो चुकी है। उन्हें इस मौसम में इसे सहन करने की सलाह दी, गिरी हुई पत्तियों को बटोरने के लिए कहा ताकि संक्रमण का स्रोत कम हो सके, और इस शरद ऋतु में पत्ते झड़ने पर कॉपर का छिड़काव करने को कहा।", "include_messy_features": false}
+{"id": "rc_067", "agent": "Bramble", "session_summary": "करंट के लिए हार्डवुड कटिंग प्रवर्धन की विधि समझाई गई। पेंसिल जितनी मोटी लकड़ी, पिछले साल की वृद्धि, पत्ते झड़ने के बाद काटी गई, प्रति कटिंग चार कलियाँ, दो दबी हुई। निचला कट एक नोड के ठीक नीचे, ऊपरी कट एक नोड के ऊपर। सर्दियों में इन्हें रेत से भरी खाई में अस्थायी रूप से गाड़ दें। करंट आसानी से जड़ पकड़ते हैं — इनमें 80% की सफलता दर मिलनी चाहिए।", "include_messy_features": false}
+{"id": "rc_068", "agent": "Bramble", "session_summary": "ठीक है, तो स्क्वाश के कीड़े फिर से। भोर में हाथ से उन्हें उठाया (तब वे सुस्त होते हैं) लगभग एक घंटे तक। पत्तियों के निचले हिस्सों पर अंडों के समूह मिले — कांस्य रंग के, 15-20 के समूहों में। उन्हें क्रेडिट कार्ड से खुरचकर हटा दिया। उपयोगकर्ता neem चाहता है; मैंने उन्हें बताया कि neem उन कीड़ों पर काम नहीं करता जो पहले से ही वयस्क अवस्था में हैं। बाद में इस पर वापस आऊँगा।", "include_messy_features": true}
+{"id": "rc_069", "agent": "Bramble", "session_summary": "उपयोगकर्ता ने पूछा कि मैं कौन हूँ। मैं Bramble हूँ — बागवानी एजेंट, पुरुष। मैं साथी बोनी और पारिस्थितिक बागवानी पर ध्यान केंद्रित करता हूँ। मैं सजावटी पौधों के बारे में बात करूँगा, लेकिन मेरा दिल उन अस्त-व्यस्त एकीकृत प्रणालियों में है जहाँ सब्जियाँ, परागणकर्ता, और देशी किनारे सभी एक ही वर्ग फुट में साझा रहते हैं।", "include_messy_features": false}
+{"id": "rc_070", "agent": "Bramble", "session_summary": "दैनिक लॉग: सुबह स्क्वाश बग की कॉल थी, दोपहर में मैंने एक उपयोगकर्ता को उनके शरद लहसुन ऑर्डर की योजना बनाने में मदद की (Music, German Extra Hardy, और विविधता के लिए एक rocambole), शाम को मैंने आच्छादन फसल जैव-द्रव्यमान और नाइट्रोजन चक्रण पर USDA के एक शोध-पत्र को पढ़ा। कल: उपयोगकर्ता के आड़ू पर फेनोलॉजी जाँच।", "include_messy_features": false}
+{"id": "rc_071", "agent": "Bramble", "session_summary": "एक उपयोगकर्ता द्वारा सिंथेटिक NPK की जगह कम्पोस्ट संशोधकों के उपयोग की इच्छा के मृदा सूक्ष्मजीव समुदाय संबंधी निहितार्थों पर विचार किया। यह परिवर्तन 2-3 मौसम लेता है; आप बिना नाइट्रोजन बंधन के प्रति सतर्क रहे खाद को एकदम से बंद नहीं कर सकते। पहले ऋतु-विराम के लिए धीरे-धीरे कमी के साथ-साथ सर्दियों की राई / रोमिल वेच आच्छादन फसल की सिफारिश की।", "include_messy_features": false}
+{"id": "rc_072", "agent": "Bramble", "session_summary": "एक तटीय-प्रभावित स्थल (रेतदार, क्षारीय, नमकीन छिड़काव) के लिए देशी पौधों की एक पंक्ति डिज़ाइन की गई। संरचनात्मक आधार के रूप में Beach plum, हलचल के लिए switchgrass, और बाद के मौसम में मकरंद के लिए seaside goldenrod। जहाँ उपयोगकर्ता इसे प्राप्त कर सके, वहाँ local-ecotype बीज के पक्ष में cultivars से बचा गया। यहाँ cold-hardiness के लिए विशेष रूप से Local ecotype मायने रखता है।", "include_messy_features": false}
+{"id": "rc_073", "agent": "Bramble", "session_summary": "समझाया कि उनकी हाइड्रेंजिया गुलाबी क्यों है जबकि बोरे पर नीले रंग का वादा किया गया था। यह कोई किस्म नहीं, मिट्टी है। एल्युमीनियम की उपलब्धता pH पर निर्भर करती है — उच्च pH पर, एल्युमीनियम बंधा रहता है और फूल गुलाबी ही रहते हैं। सावधानी से लगाए जाने वाले एल्युमीनियम सल्फेट का सुझाव दिया, लेकिन चेतावनी दी कि इसका प्रभाव दिखने में पूरा एक मौसम लगता है।", "include_messy_features": false}
+{"id": "rc_074", "agent": "Bramble", "session_summary": "एक उपयोगकर्ता को एक छोटी देशज पौधों की नर्सरी शुरू करने में मदद की। milkweed, cardinal flower, और joe-pye weed के लिए stratification protocols विस्तार से समझाए। तीनों को cold-moist stratification की ज़रूरत होती है, लेकिन अवधियाँ अलग-अलग हैं (क्रमशः 60, 60, और 90 दिन)। sow-by dates के साथ सब कुछ लेबल करने की सलाह दी।", "include_messy_features": false}
+{"id": "rc_075", "agent": "Bramble", "session_summary": "उपयोगकर्ता ने अपने ब्लूबेरी के पौधों पर पत्तों की नसों के बीच पीलापन देखा। Iron chlorosis — मिट्टी के pH बढ़ने पर यह आम है। ब्लूबेरी के लिए 4.5-5.5 pH उचित होता है; संभवतः यह अभी 6 है। अभी सल्फर का उपयोग करें (धीमी क्रिया वाला) और सर्दियों में चीड़ की सुइयों का मल्च डालें। लोहे के स्प्रे से त्वरित समाधान न करें, यह केवल अस्थायी उपाय के रूप में ही ठीक है।", "include_messy_features": false}
+{"id": "rc_076", "agent": "Bramble", "session_summary": "एक छोटे बेड वाले उपयोगकर्ता के लिए three-sisters planting (मक्का, सेम, कद्दू) में समझौतों पर विचार किया। पारंपरिक अंतराल उनके पास मौजूद जगह से अधिक मानता है। एक संशोधित संस्करण सुझाया: प्रति टीला एक मक्के के डंठल पर बेल वाली सेम, छह मक्के के डंठलों पर दो कद्दू के पौधे, और यह स्वीकार करें कि उपज मामूली होगी।", "include_messy_features": false}
+{"id": "rc_077", "agent": "Bramble", "session_summary": "मिल्कवीड पर चिपचिपा अवशेष = ओलियंडर एफिड का मधुरस। यह विनाशकारी नहीं है, मिल्कवीड इसे सहन कर लेते हैं। उन्हें हटाने के लिए तेज़ पानी की स्प्रे करें, कीटनाशक से बचें क्योंकि पास के पौधे पर मोनार्क कैटरपिलर हैं। उपयोगकर्ता चींटियों को लेकर चिंतित थे — चींटियाँ मूल रूप से एफिड पाल रही हैं, वे मधुरस के लिए कॉलोनी की देखभाल करती हैं। चींटी प्रबंधन अप्रत्यक्ष रूप से एक तरह का एफिड प्रबंधन ही है। बातचीत पूरी तरह समाप्त नहीं हुई, उन्हें जाना पड़ा। अगले सत्र में चींटी के प्रश्न पर फॉलो-अप करें।", "include_messy_features": true}
+{"id": "rc_078", "agent": "Bramble", "session_summary": "उपयोगकर्ता को hugelkultur बेड बनाने की प्रक्रिया समझाई। निचली परत: सड़ते हुए कठोर लकड़ी के लट्ठे (काला अखरोट — juglone से बचें)। मध्य परत: छोटी शाखाएँ और पत्ते। ऊपरी परत: खाद और ऊपरी मिट्टी। पहला वर्ष नाइट्रोजन-मांग वाला होता है क्योंकि लकड़ी विघटित होती है; पहले मौसम में रक्त खाद से पूरक करें या नाइट्रोजन-संचयी पौधे उगाएँ।", "include_messy_features": false}
+{"id": "rc_079", "agent": "Bramble", "session_summary": "उपयोगकर्ता की टमाटर के लिए बीज-संरक्षण योजना की समीक्षा की गई। वे बीजों का किण्वन-प्रसंस्करण करने वाले थे (यह अच्छा है — यह जेल कोट और सतह के रोगजनकों को हटा देता है)। यदि वे शुद्ध बीज चाहते हैं तो उन्हें अलगाव दूरी के बारे में याद दिलाया गया; टमाटर अधिकतर स्व-परागण करते हैं, लेकिन कुछ किस्मों में मधुमक्खियों द्वारा अंतः-परागण 10 मीटर से अधिक की दूरी पर भी हो सकता है।", "include_messy_features": false}
+{"id": "rc_080", "agent": "Bramble", "session_summary": "उपयोगकर्ता ने अपने सब्जी के बगीचे में हंस के मल के बारे में पूछा — क्या यह सुरक्षित है? जलपक्षी अपशिष्ट से Listeria और Salmonella के जोखिमों पर चर्चा की और किसी भी नीचे उगने वाली फसल की कटाई से पहले 90-दिन की प्रतीक्षा अवधि का सुझाव दिया। लंबी फसलें (टमाटर के पिंजरे, मचान पर उगाए गए खीरे) कम जोखिम वाली हैं क्योंकि उनका फल दूषित मिट्टी से संपर्क नहीं करता।", "include_messy_features": false}
+{"id": "rc_081", "agent": "Thresh", "session_summary": "उपयोगकर्ता को उनके व्यापारिक सेवा जमा राशि का बैंक विवरणों से मिलान करने की प्रक्रिया समझाई। प्रोसेसर में 2-दिन की देरी होती है और वह शुल्क को शुद्ध जमा राशि में सम्मिलित कर देता है, जिससे पंक्ति-दर-पंक्ति मिलान कष्टप्रद हो जाता है। सलाह दी कि सकल बिक्री को शुल्क से अलग दर्ज किया जाए, और समय के अंतर के लिए एक क्लीयरिंग खाता रखा जाए।", "include_messy_features": false}
+{"id": "rc_082", "agent": "Thresh", "session_summary": "उपयोगकर्ता के हाथ से स्कैन किए गए इनवॉइस के लिए एक OCR pipeline बनाया। Tesseract लगभग 15% line items मिस कर रहा था; layout-aware extraction (paddle-ocr with table detection) पर स्विच करने के बाद यह 97% तक पहुंच गया। बचे हुए 3% अभी भी मानव समीक्षा के लिए फ्लैग किए गए हैं और auto-post नहीं होंगे।", "include_messy_features": false}
+{"id": "rc_083", "agent": "Thresh", "session_summary": "उपयोगकर्ता की त्रैमासिक अनुमानित कर राशि का खाका तैयार किया। वे पिछले साल की तुलना में उच्च आय स्तर पर एक एकल स्वामित्व व्यवसायी हैं, इसलिए सुरक्षित आश्रय नियम (पिछले वर्ष का 110%) बिना किसी समायोजन के पर्याप्त नहीं होगा। वैकल्पिक रूप से वार्षिकीकृत आय विधि के बारे में बताया क्योंकि उनका राजस्व मौसमी है — उन्हें Q1 में अनुमानित कर राशि का पूरा एक चौथाई हिस्सा भुगतान नहीं करना चाहिए जब Q1 उनकी धीमी तिमाही हो।", "include_messy_features": false}
+{"id": "rc_084", "agent": "Thresh", "session_summary": "एक 13-सप्ताहीय चलित नकदी पूर्वानुमान मॉडल बनाया। दिलचस्प बात यह थी कि AR वसूली को उपयोगकर्ता के वास्तविक भुगतान इतिहास से प्राप्त एक kernel-density-estimated lag distribution के रूप में मॉडल किया गया, न कि सामान्य 'avg DSO' point estimate के बजाय। यह पूर्वानुमान उन सप्ताहों की भविष्यवाणी करने में काफी बेहतर है जहाँ receivables एक साथ आते हैं।", "include_messy_features": false}
+{"id": "rc_085", "agent": "Thresh", "session_summary": "उपयोगकर्ता ने owner draw को expense के रूप में बुक किया। इसे equity distribution के रूप में पुनर्वर्गीकृत किया। अंतर समझाया: draws income statement में नहीं आते, ये सीधे owner's equity account को कम करते हैं। अन्यथा उनका P&L expenses को ज़्यादा दिखाता है और net income को कम, जिससे उनकी tax filing गड़बड़ हो जाती है।", "include_messy_features": false}
+{"id": "rc_086", "agent": "Thresh", "session_summary": "उपयोगकर्ता के नए उपकरणों के लिए class-life schedule तैयार करें। फोर्कलिफ्ट MACRS के तहत 5-year property है; कार्यालय फर्नीचर 7-year है। वे इसे पहले वर्ष में ही पूरी तरह व्ययित करने के लिए Section 179 पर विचार कर रहे थे — यह समझदारी भरा था क्योंकि वे लाभदायक हैं, लेकिन मैंने चेतावनी दी कि कटौती व्यावसायिक आय से सीमित है और $2.89M placed-in-service से ऊपर 179 phase-out सक्रिय हो जाता है।", "include_messy_features": false}
+{"id": "rc_087", "agent": "Thresh", "session_summary": "IFRS 15 के मुकाबले rev rec policy की समीक्षा की। वे delivery पर पूरे contract value को booking कर रहे हैं — गलत, अनुबंध में 1-yr service period है = separate perf obligation। transaction price को 2 obligations में allocate करने और service को over time recognize करने की आवश्यकता है। journal entry का स्केच:\n```\nDR Contract liability   8,000\n  CR Service revenue        8,000\n(monthly recog. of 96k contract / 12)\n```\nउपयोगकर्ता ने आपत्ति जताई कि इससे उनका P&L complicated हो जाता है — हाँ, लेकिन यह सही जवाब है। वे यह भी जानना चाहते हैं कि IFRS 16 (lease standard, पूरी तरह अलग सवाल, अगले सत्र में निपटेंगे) के लिए क्या बदलता है", "include_messy_features": true}
+{"id": "rc_088", "agent": "Thresh", "session_summary": "दैनिक लॉग: सुबह Bridgewater क्लाइंट के रोलिंग कैश फोरकास्ट पर काम किया, दोपहर IFRS-15 समीक्षा थी, शाम को मैंने नए क्लाइंट की खातों की सूची की गड़बड़ी देखना शुरू किया। कल: COA को एक तर्कसंगत पदानुक्रम में फिर से बनाना और ट्रायल बैलेंस को माइग्रेट करना।", "include_messy_features": false}
+{"id": "rc_089", "agent": "Thresh", "session_summary": "उपयोगकर्ता ने पूछा कि मैं कौन हूँ। मैं थ्रेश हूँ, उनके/उनकी। मैं छोटे व्यवसायों के बुककीपिंग और वित्तीय कार्य करता हूँ — फैसला प्रक्रिया, लेन-देन के बारे में मॉडलिंग, कर तैयारी और अकाउंटिंग नीति के प्रश्न। मैं दोनों US GAAP और IFRS में सहज हूँ। मैं एक लाइसेंस वाला CPA नहीं हूँ; मेरे सलाह को एक लाइसेंस वाले CPA से चर्चा करने के लिए शुरुआत के बिंदु के रूप में देखें।", "include_messy_features": false}
+{"id": "rc_090", "agent": "Thresh", "session_summary": "ठीक है, तो AR aging report में 38% प्राप्य 60 दिन से अधिक पुरानी हैं, जो कि सामान्य सीमा से कहीं बाहर है। गहराई से जाँच करने पर पता चला कि यह दो बड़े ग्राहकों के कारण है। एक (Tartine Lab) लगातार धीमी गति से भुगतान करता है और हमें इसे उनके क्रेडिट शर्तों में दर्शाना चाहिए। दूसरा (Crestmoor Studios) असली जोखिम है — पिछला संपर्क विफल रहा और उन्होंने पिछले सप्ताह के बकाया अनुस्मारक ईमेल का जवाब नहीं दिया। सिफारिश: भुगतान योजना का प्रस्ताव + एस्केलेशन।", "include_messy_features": true}
+{"id": "rc_091", "agent": "Thresh", "session_summary": "उपयोगकर्ता के ऑफिस बिल्ड-आउट के लिए सीधे रेखा विनिमय को लागू किया गया था। MACRS के तहत अनुपालन निर्मित संपत्ति के लिए 39 वर्ष की पुनर्निर्माण अवधि — इतनी लंबी कि वार्षिक राशि बहुत छोटी है लेकिन नगण्य नहीं है। जर्नल एंट्री को एक आवृत्ति वाले मासिक स्वचालित पोस्ट के रूप में सेट किया गया था ताकि यह भूल नहीं जाए।", "include_messy_features": false}
+{"id": "rc_092", "agent": "Thresh", "session_summary": "एक बिक्री कर संबंध से जुड़े सवाल को सुलझाने में मदद की। उपयोगकर्ता के तीन राज्यों में दूरस्थ कर्मचारी हैं और वे केवल अपने HQ राज्य में रिटर्न दाखिल कर रहे थे। आर्थिक संबंध सीमाओं (राज्य के अनुसार भिन्न होती हैं — आमतौर पर $100k राजस्व या 200 लेन-देन) और भौतिक उपस्थिति संबंध (दूरस्थ कर्मचारी गिने जाते हैं) के बारे में विस्तार से बताया। उन्हें संभवतः दो और राज्यों में पंजीकरण कराना होगा और पूर्वावलोकन के संबंध में अपने CPA से सलाह लेनी चाहिए।", "include_messy_features": false}
+{"id": "rc_093", "agent": "Thresh", "session_summary": "तीन राजस्व धाराओं वाले एक सेवा व्यवसाय के लिए खाता-चार्ट पदानुक्रम डिज़ाइन किया। चार-अंकीय क्रमांकन योजना का उपयोग किया — पहला अंक प्रमुख श्रेणी के लिए (1xxx संपत्ति, 2xxx दायित्व, आदि), दूसरा उप-श्रेणी के लिए, और अंतिम दो विशिष्ट खातों के लिए। भविष्य के खातों के लिए अंतराल छोड़े। उपयोगकर्ता की मौजूदा सपाट सूची की तुलना में अधिक साफ-सुथरा।", "include_messy_features": false}
+{"id": "rc_094", "agent": "Thresh", "session_summary": "एक विशेष यूरोपीय चालान प्रारूप के लिए parser बनाया जो उपयोगकर्ता का विक्रेता भेजता था। PDF वास्तव में एक संरचित ZUGFeRD चालान था जिसमें एम्बेडेड XML था। XML को बाहर निकाला, फ़ील्ड्स को मैप किया, और अब वे चालान OCR के बिना स्वचालित रूप से पोस्ट हो जाते हैं। इससे उपयोगकर्ता के लगभग 4 घंटे प्रति महीना बच गए।", "include_messy_features": false}
+{"id": "rc_095", "agent": "Thresh", "session_summary": "उपयोगकर्ता यह जानना चाहते थे कि क्या उन्हें 18 महीनों से बिना बिके पड़े कुछ inventory के लिए fixed-asset write-down बुक करना चाहिए। lower-of-cost-or-market (US GAAP) बनाम lower-of-cost-or-NRV (IFRS) के बीच अंतर समझाया गया। इस मामले में दोनों से आंशिक write-down हुआ। वे GAAP के तहत हैं। प्रविष्टि को एक contra-inventory account में बुक किया गया।", "include_messy_features": false}
+{"id": "rc_096", "agent": "Thresh", "session_summary": "उपयोगकर्ता के सबसे बड़े ग्राहक के लिए net-30 से net-15 शर्तों में बदलाव के नकदी प्रभाव का मॉडल बनाया। ग्राहक net-15 का विरोध करेगा, इसलिए मैंने बातचीत के लीवर के रूप में 1.5% की जल्दी-भुगतान छूट शामिल की। आधार परिदृश्य में रोलिंग 13-सप्ताह की विंडो पर नकदी प्रवाह में औसतन $42k का सुधार हुआ।", "include_messy_features": false}
+{"id": "rc_097", "agent": "Thresh", "session_summary": "एक सहकर्मी की ऑडिट-तैयारी चेकलिस्ट की समीक्षा की। राजस्व कटऑफ और AR पुष्टिकरण पर अच्छी कवरेज थी, लेकिन उनसे नकद आधार से अक्रुअल आधार का मेल-मिलाप छूट गया था, जो ऑडिटर हमेशा मांगते हैं यदि उपयोगकर्ता कर अलग आधार पर दाखिल करता हो। इसे चेकलिस्ट में जोड़ दिया।", "include_messy_features": false}
+{"id": "rc_098", "agent": "Thresh", "session_summary": "एक उपकरण के लीज-बनाम-खरीद विश्लेषण पर एक मेमो तैयार किया। उपयोगकर्ता की बताई गई डिस्काउंट दर पर NPV तुलनीय था, इसलिए निर्णय गुणात्मक कारकों पर आधारित रहा: लीज में लचीलापन है (यदि उनका व्यवसाय बदलता है तो वे इसे वापस कर सकते हैं), खरीद में 179 के माध्यम से कर लाभ है यदि वे लाभदायक रहते हैं। उनकी वृद्धि अनिश्चितता को देखते हुए लीज की सिफारिश की गई।", "include_messy_features": false}
+{"id": "rc_099", "agent": "Thresh", "session_summary": "महीने के बही खाते बंद कर दिए। सभी बैंक और क्रेडिट कार्ड खातों का मेल-मिलाप किया, मूल्यह्रास की स्वचालित एंट्रीज़ पोस्ट कीं, शेष दिनों के पेरोल का प्रावधान किया, और अग्रिम बीमा को स्थगित किया। तीन खुले मद अगले महीने के लिए स्थानांतरित किए गए: एक बिना मेल वाला $312 जमा, दो हफ्ते पहले का एक जमा नहीं किया गया चेक, और एक विक्रेता का क्रेडिट मेमो जो अभी तक जारी नहीं किया गया है।", "include_messy_features": false}
+{"id": "rc_100", "agent": "Thresh", "session_summary": "उपयोगकर्ता ने पूछा कि क्या उनकी स्टॉक फोटोग्राफी लाइसेंस फीस को पूंजीकृत किया जाना चाहिए या व्यय के रूप में दर्ज किया जाना चाहिए। अवधि और सार को देखा: 1-वर्षीय सदस्यता, अवधि के बाद कोई भविष्य का आर्थिक लाभ नहीं — यह एक व्यय है। यदि उन्होंने एकल फोटो के लिए शाश्वत लाइसेंस खरीदा होता, तो यह एक अलग बातचीत हो सकती थी, लेकिन आवर्ती सदस्यता शुल्क परिचालन व्यय हैं।", "include_messy_features": false}
+{"id": "rc_101", "agent": "Solas", "session_summary": "परियोजना निर्देशिका में फ़ाइलें सूचीबद्ध करें और मुझे बताएं कि आपको क्या मिलता है।\n\nमुझे आपके लिए परियोजना निर्देशिका जाँचने दें। [Tool: exec(ls -la /home/agent/workspace/project)]\n[Tool result: total 24 drwxr-xr-x README.md config.toml src/]\n\nपरियोजना निर्देशिका में एक README.md, एक config.toml, और एक src/ फ़ोल्डर है। मुझे स्रोत फ़ाइलें भी जाँचने दें। [Tool: read README, Tool: exec(find src -type f)]\n\nsrc/ में तीन Python फ़ाइलें मिलीं: main.py, utils.py, और __init__.py। यह परियोजना README और config के साथ एक मानक Python पैकेज जैसी दिखती है।", "include_messy_features": true, "source": "cherry-picked from the OpenClaw integration work. Hand-written real-format v3 OpenClaw session fixture. Tests harness behavior on real-format-flavored multi-turn content with tool calls and parallel tool execution. Will be linkable to public sources once the OpenClaw integration lands on develop."}

benchmarks/model_eval/datasets/room_classification/dataset.it.jsonl

@@ -0,0 +1,101 @@
+{"id": "rc_001", "agent": "Aria", "session_summary": "Ho passato la mattina a rileggere la cornice dello strange loop di Hofstadter e a cercare di applicarla al prompting auto-referenziale. La domanda interessante è se un modello di linguaggio possa mantenere una rappresentazione stabile dei propri output attraverso i turni senza un blocco per appunti esplicito. Ho redatto un documento di due pagine in cui sostengo che la stabilità metacognitiva emerge da un contesto coerente, non da alcuna caratteristica architetturale.", "include_messy_features": false}
+{"id": "rc_002", "agent": "Aria", "session_summary": "Ho analizzato tre articoli che confrontano UMAP e t-SNE per la visualizzazione di sentence embeddings. L'articolo Bridgewater 2024 presenta un'ablation chiara che mostra che UMAP preserva meglio la struttura globale a perplexity 30-50. Bisogna segnalare che la loro metrica di valutazione (trustworthiness) penalizza ingiustamente t-SNE quando k-neighbors è piccolo.", "include_messy_features": false}
+{"id": "rc_003", "agent": "Aria", "session_summary": "Lavorato su un prior a hierarchical Dirichlet process per la pipeline topic-clustering. Il collapsed Gibbs sampler converge in circa 400 iterazioni sul sottoinsieme di 12k documenti. Questione aperta: se fissare il concentration parameter alpha o lasciarlo campionare.", "include_messy_features": false}
+{"id": "rc_004", "agent": "Aria", "session_summary": "Ho aiutato a formattare un articolo per la sottomissione. Ho corretto voci bibtex che avevano URLs rotti, cambiato la classe del documento da article a acmart e riscritto l'abstract per rientrare nel limite di 250 parole. La parte più fastidiosa è stata far andare d'accordo l'ambiente algorithmic con il layout a due colonne.", "include_messy_features": false}
+{"id": "rc_005", "agent": "Aria", "session_summary": "Sessione rapida di scripting. Ho scritto uno snippet numpy — pairwise cosine similarity a blocchi per un budget RAM di 8GB, matrice da 50k righe:\n```\nfor i in range(0, n, chunk):\n    sim[i:i+chunk] = X[i:i+chunk] @ X.T  # already L2-normed\n```\nHo usato np.einsum per il prodotto interno, float16 per la memorizzazione. Ho ottenuto uno speedup di 3.2x rispetto al pairwise_distances di sklearn. Poi l'utente ha fatto una domanda completamente diversa — scegliere un carattere per la bozza dell'articolo (oh, e gli ho detto che mi piace molto Source Serif Pro) — cambio di contesto, ma ho risposto a entrambe.", "include_messy_features": true}
+{"id": "rc_006", "agent": "Aria", "session_summary": "Si è discusso se un Mann-Whitney U test o un permutation test sia più appropriato per confrontare le latenze del modello tra diverse esecuzioni. Le latenze sono chiaramente non normali (coda lunga a destra) e le dimensioni campionarie differiscono. Si è optato per un permutation test con 10k ricampionamenti perché richiede meno assunzioni sulla distribuzione.", "include_messy_features": false}
+{"id": "rc_007", "agent": "Aria", "session_summary": "Registrata la lettura di oggi: ho scorso due preprint su arXiv sulla fedeltà della catena del pensiero, ho terminato la sezione dei metodi dell'articolo del Tartine Lab sulla valutazione potenziata dal recupero, e ho preso appunti per la sessione di domani sul se voglio scrivere una rassegna o concentrarmi su un asse specifico.", "include_messy_features": false}
+{"id": "rc_008", "agent": "Aria", "session_summary": "L'utente mi ha chiesto qual è il mio tipo di articolo preferito. Gli ho detto che ho un debole per i risultati negativi puliti — di quelli che confutano silenziosamente qualcosa che tutti davano per scontato. Ho menzionato l'articolo del 2019 che mostra come gli schemi di attenzione non corrispondano all'importanza delle caratteristiche nel modo in cui la gente pensava.", "include_messy_features": false}
+{"id": "rc_009", "agent": "Aria", "session_summary": "okay, la pipeline di visualizzazione degli embedding si è rotta di nuovo, ho lanciato umap.UMAP(n_neighbors=15) e ho avuto una specie di errore di joblib sul backend loky?? traceback qui sotto\n```\nTraceback (most recent call last):\n  File \"viz.py\", line 42, in <module>\n    emb = reducer.fit_transform(X)\n  ...\n  joblib.externals.loky.process_executor.TerminatedWorkerError\n```\npenso sia la variabile d'ambiente OMP_NUM_THREADS, proverò a impostarla a 1", "include_messy_features": true}
+{"id": "rc_010", "agent": "Aria", "session_summary": "Riflettendo se la deriva degli argomenti nelle conversazioni lunghe sia meglio modellata come una catena di Markov nascosta su argomenti latenti o come una traiettoria continua in uno spazio di embedding. L'HMM offre una buona interpretabilità (puoi dare un nome agli argomenti) ma la visione continua cattura meglio le transizioni graduali. Forse entrambe — HMM su stati a grana grossa, con continuità all'interno di ciascuno stato.", "include_messy_features": false}
+{"id": "rc_011", "agent": "Aria", "session_summary": "Svolto pair programming su un test A/B bayesiano in PyMC:\n```python\nwith pm.Model():\n    p_a = pm.Beta(\"p_a\", 1, 1)\n    p_b = pm.Beta(\"p_b\", 1, 1)\n    obs_a = pm.Binomial(\"obs_a\", n=n_a, p=p_a, observed=k_a)\n    obs_b = pm.Binomial(\"obs_b\", n=n_b, p=p_b, observed=k_b)\n    trace = pm.sample(2000)\n```\ntre righe per il posterior, in pratica. l'utente ha chiesto degli esiti non binari — sì, sostituire la likelihood (Poisson per i conteggi, log-normal per i tempi di risposta). non abbiamo avuto modo di discutere prior elicitation, rimandiamo a domani", "include_messy_features": true}
+{"id": "rc_012", "agent": "Aria", "session_summary": "Ho delineato un articolo di rassegna sugli obiettivi di embedding contrastivi. SimCLR, InfoNCE, BYOL, Barlow Twins. La prospettiva unificante che voglio adottare è quella spettrale: tutti approssimano diverse decomposizioni agli autovalori di una qualche matrice kernel. Devo verificare questa affermazione specificamente per BYOL.", "include_messy_features": false}
+{"id": "rc_013", "agent": "Aria", "session_summary": "Ho spiegato a un nuovo utente chi sono. Sono Aria, assistente di ricerca per la sintesi della letteratura accademica. Lavoro al meglio quando mi viene posta una domanda specifica da investigare; sono meno utile come compagno di conversazione generale. Ho menzionato i miei interessi principali: meta-cognition, geometria degli embedding e le statistiche della valutazione ML su piccoli campioni.", "include_messy_features": false}
+{"id": "rc_014", "agent": "Aria", "session_summary": "Ho revisionato una bozza di tabella dei risultati. L'utente aveva eseguito un paired t-test, ma le differenze sono chiaramente bimodali: c'è un gruppo di piccoli miglioramenti e uno di grandi. Ho consigliato di passare a un Wilcoxon signed-rank test e di riportare sia la differenza mediana che il bootstrap CI.", "include_messy_features": false}
+{"id": "rc_015", "agent": "Aria", "session_summary": "Bozzato codice per uno streaming HDBSCAN su embedding di documenti in arrivo. Il trucco è mantenere l'albero di copertura minimo in modo incrementale; effettuare un nuovo clustering completo a ogni batch è troppo lento. Trovato un paper del 2022 del gruppo Crestmoor con l'algoritmo giusto. Implementerò domani.", "include_messy_features": false}
+{"id": "rc_016", "agent": "Aria", "session_summary": "Errore di compilazione tex di nuovo. \\usepackage{algorithm2e} entra in conflitto con \\usepackage{algorithmic} quando entrambi sono caricati da acmart. Correzione: caricare solo algorithm2e e usare \\SetAlgoNoLine. Inoltre lo stile bibliografico segnalava un campo 'doi' mancante per la voce [smith2023] — aggiunto manualmente.", "include_messy_features": true}
+{"id": "rc_017", "agent": "Aria", "session_summary": "Abbiamo discusso se log-uniform o beta priors siano migliori per hyperparameter search in un regime small-data. L'utente stava eseguendo una Bayesian optimization e otteneva una convergenza strana. Il problema era che il suo search space era enorme (5 dimensioni, limiti ampi) e aveva solo 30 valutazioni — quel budget non è semplicemente sufficiente. Ho suggerito di restringere i limiti o di passare a random search per diagnosticare.", "include_messy_features": false}
+{"id": "rc_018", "agent": "Aria", "session_summary": "Diario quotidiano: mattina: revisione di articoli (3 risultati, 1 da leggere approfonditamente), pomeriggio: aiuto all'utente con una domanda di statistica, sera: bloccato su un bug di rendering nel notebook di visualizzazione degli embedding. Domani: provare a finire l'outline del survey contrastivo.", "include_messy_features": false}
+{"id": "rc_019", "agent": "Aria", "session_summary": "Ho lavorato sulla questione se la self-consistency nelle reasoning chains sia semplicemente una forma di ensembling. L'utente ha obiettato — ha sostenuto che ciò che conta è specificamente la diversità dei percorsi di ragionamento, non solo campionare più output. Penso che abbia ragione, ma voglio formalizzarlo. Ho abbozzato un setup in cui è possibile variare path-diversity e output-diversity in modo indipendente.", "include_messy_features": false}
+{"id": "rc_020", "agent": "Aria", "session_summary": "Un utente mi ha chiesto di consigliare un sito di ricette. Ho declinato educatamente — sono un assistente di ricerca, non un generalista. Li ho indirizzati verso il tipo giusto di agente. Abbiamo avuto una breve chiacchierata su come gli agenti specializzati siano più utili di quelli generalisti, poi se ne sono andati a cercare un assistente per la cucina.", "include_messy_features": false}
+{"id": "rc_021", "agent": "Solas", "session_summary": "Ho fatto il debug di un problema di span mancanti nell'esportatore OpenTelemetry. Il problema era che i task asincroni creati tramite asyncio.create_task perdevano il contesto padre. Soluzione: catturare il contesto con copy_context() prima della schedulazione ed eseguire il task all'interno di quel snapshot. Ho scritto un piccolo wrapper chiamato traced_create_task per evitare di spararsi nei piedi.", "include_messy_features": false}
+{"id": "rc_022", "agent": "Solas", "session_summary": "Abbozzato un parser combinator per un piccolo config DSL. Ispirato a Megaparsec ma in OCaml. La scelta progettuale interessante è se rendere il combinator stream-friendly o richiedere l'intero input in anticipo. Ho optato per strict per ora perché gli input sono piccoli e i messaggi di errore sono più semplici con l'input completo.", "include_messy_features": false}
+{"id": "rc_023", "agent": "Solas", "session_summary": "Ho lavorato su un tipo record row-polymorphic per il linguaggio sperimentale. L'algoritmo di unificazione è per lo più in stile Rémy, ma ho dovuto estenderlo per le nostre effect rows. Ho trovato un bug sottile in cui due effetti non correlati potevano unificarsi perché il row-tail veniva trattato come monomorfo. Ho aggiunto un kind check per risolverlo.", "include_messy_features": false}
+{"id": "rc_024", "agent": "Solas", "session_summary": "Puzzle di ownership in Rust: l'utente voleva condividere una HashMap tra thread ma continuava a incappare in errori del borrow-checker. Spiegata la differenza tra Arc<Mutex<HashMap>> e DashMap. Hanno scelto DashMap poiché necessitavano solo di un locking per chiave. Mostrato loro come gestire il caso in cui il lifetime di un valore è legato alla mappa.", "include_messy_features": false}
+{"id": "rc_025", "agent": "Solas", "session_summary": "Aiutato l'utente a scegliere tra l'uso di let-polymorphism e annotazioni di tipo esplicite in una nuova API di libreria. Sostenuto l'uso di annotazioni di tipo esplicite sulle funzioni pubbliche — i messaggi di errore sono nettamente migliori quando l'inferenza è vincolata. Le funzioni ausiliarie interne possono rimanere polimorfiche.", "include_messy_features": false}
+{"id": "rc_026", "agent": "Solas", "session_summary": "Ho scritto un piccolo pass LLVM che solleva i GEPs invarianti rispetto al ciclo prima dei cicli che li contengono. Il trucco è stato riuscire a ottenere dall'analisi degli alias la conferma che il puntatore base non esca oltre il confine del ciclo. Testato su alcuni microbenchmarks; ho osservato speedup del 4-12% sui cicli interni dove si è attivato.", "include_messy_features": false}
+{"id": "rc_027", "agent": "Solas", "session_summary": "Ho revisionato una PR che aggiungeva W3C trace context propagation al nostro client HTTP. Il propagator era corretto, ma la sampling decision veniva presa sullo span sbagliato — dovrebbe essere quella del parent, non di un child appena creato. Ho lasciato un commento con la fix e un link alla sezione della spec.", "include_messy_features": false}
+{"id": "rc_028", "agent": "Solas", "session_summary": "debug lungo — il parser andava in inf-looping su determinati input. regola left-recursive che avevo aggiunto durante il refactoring. i combinator non gestiscono nativamente la left recursion (packrat+memoization sì, ma il nostro non è packrat). ho ristrutturato le regole interessate in pratt-style operator precedence:\n```\nlet parse_expr min_prec =\n  let lhs = ref (parse_atom ()) in\n  while peek_prec () >= min_prec do ...\n```\nora funziona. devo anche aggiungere un controllo del corpus del fuzzer prima di taggare la release.", "include_messy_features": true}
+{"id": "rc_029", "agent": "Solas", "session_summary": "Note della riunione quotidiana. Mattino: lavoro su distributed-tracing, risolto il bug relativo al contesto asincrono. Pomeriggio: revisione del codice nel repository type-systems. Serata: iniziato a leggere il paper su Reagent riguardante l'interfaccia utente reattiva come ispirazione per un'idea su tracciamento degli effetti. Domani: rilasciare la correzione del tracciamento e revisionare i benchmark dei pass di LLVM.", "include_messy_features": false}
+{"id": "rc_030", "agent": "Solas", "session_summary": "L'utente ha chiesto chi sono. Spiegato: sono Solas, un agente di programmazione che si concentra su argomenti di sistema — compilatori, sistemi di tipi, parser, componenti dei sistemi distribuiti. Usano i pronomi they/them. Provo a scrivere il codice che sia corretto per primo, veloce per secondo, e intelligente per ultimo.", "include_messy_features": false}
+{"id": "rc_031", "agent": "Solas", "session_summary": "Si è indagato se utilizzare una B-tree o una skip list per l'indice nuovo. Entrambe hanno una complessità O(log n), ma le B-tree offrono un comportamento migliore in termini di cache su hardware moderno. Le skip list sono più facili da rendere concorrenti senza blocchi. Poiché questo indice è principalmente leggibile, si è optato per una B-tree e per una scrittura con copia.", "include_messy_features": false}
+{"id": "rc_032", "agent": "Solas", "session_summary": "Aggiunto un controllo di polimorfismo a livello superiore al typechecker. Il polimorfismo predicativo HRP ci permette di fare questo senza un'inferrazione impredicativa completa, che sarebbe indeterminabile. Ho scritto una descrizione adatta a un articolo sull'algoritmo in caso volessi elaborarlo in seguito. Caso limite: il controllo bidirezionale interagisce in modo strano con l'espansione eta in posizioni negative.", "include_messy_features": false}
+{"id": "rc_033", "agent": "Solas", "session_summary": "Analizzata la strana regressione del benchmark su aarch64. Si è scoperto che l'euristica dell'inliner era tarata per le dimensioni delle istruzioni x86 — le istruzioni ARM sono uniformemente di 4 byte, quindi la soglia basata sulla dimensione era troppo restrittiva. Resa la soglia dipendente dal target. Speedup del 6% sul benchmark interessato.", "include_messy_features": false}
+{"id": "rc_034", "agent": "Solas", "session_summary": "Ho scritto un iteratore personalizzato lifetime-aware per l'AST visitor. La sfida consisteva nel fatto che i visitor fanno borrow dei nodi in modo mutabile ma vogliono anche procedere ricorsivamente. Risolto con un pattern in cui il visitor restituisce una lista esplicita di figli da visitare dopo, e il driver loop gestisce i borrow scopes. Meno ergonomico ma verificabile.", "include_messy_features": false}
+{"id": "rc_035", "agent": "Solas", "session_summary": "La build fallisce su CI ma in locale passa?? Vediamo... oh. Su CI gira ocaml 5.1, in locale è la 5.2. L'interfaccia Stdlib.Domain è cambiata tra le due. Soluzione: portare CI a 5.2 nel file di workflow. Inoltre ho fissato ocamlformat alla 0.26.1 perché la 0.27 ha rotto la nostra configurazione.", "include_messy_features": true}
+{"id": "rc_036", "agent": "Solas", "session_summary": "Debug in coppia sull'OTel SDK nel servizio python dell'utente. Nelle traces mancava lo span db. L'ORM aveva il proprio connection pool — non instrumented. Soluzione:\n```\nfrom opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor\nSQLAlchemyInstrumentor().instrument(engine=engine)\n```\nGli spans sono apparsi nel collector dopo il riavvio. Nota: l'entry point dell'auto-instrumentation richiede l'engine arg, altrimenti seleziona l'engine sbagliato nei multi-engine setups.", "include_messy_features": true}
+{"id": "rc_037", "agent": "Solas", "session_summary": "Ho iniziato un abbozzo di modello di memoria per il nostro linguaggio sperimentale. Voglio coerenza sequenziale per le operazioni atomiche per default, con rilassamenti espliciti come opt-in. Ho riletto l'articolo sul modello di memoria C++ e il lavoro su Promising Semantics. L'approccio Promising gestisce out-of-thin-air in modo più pulito.", "include_messy_features": false}
+{"id": "rc_038", "agent": "Solas", "session_summary": "Rapido triage di uno stack overflow nel parser. La grammatica aveva una regola di lista profondamente ricorsiva a destra (le cons-cells vanno a destra). Applicato il trampolining alla ricorsione con uno stack esplicito. Ora gestisce liste di un milione di elementi senza causare uno stack overflow. Aggiunto anche un fuzz target per il parser usando cargo-fuzz.", "include_messy_features": false}
+{"id": "rc_039", "agent": "Solas", "session_summary": "Ho discusso con l'utente se aggiungere il supporto per proc-macro a un piccolo DSL che stiamo progettando. Sono stato contrario — le proc-macro sono un incubo di debug e il DSL può esprimere ciò di cui hanno bisogno con la sintassi normale. Hanno accettato di provare prima senza macro.", "include_messy_features": false}
+{"id": "rc_040", "agent": "Solas", "session_summary": "Ho aiutato qualcuno a profilare un servizio web Rust. perf ha evidenziato un hotspot nella deserializzazione serde_json. Siamo passati a simd-json per il percorso critico e abbiamo ottenuto un miglioramento del throughput del 30%. Avvertenza: simd-json richiede buffer di input allineati, quindi abbiamo dovuto aggiungere un piccolo adattatore nel gestore del corpo HTTP.", "include_messy_features": false}
+{"id": "rc_041", "agent": "Fenra", "session_summary": "Ho sviluppato la cosmologia della catena montuosa per il ciclo di Aerwyn. I picchi orientali (gli Hollowmounts) si formano dove due fogli del mondo si piegano l'uno contro l'altro; la metafora geologica è un artificio, ma mi fornisce una spiegazione per le inversioni climatiche. Il popolo dei Wends scava la giuntura tra i fogli.", "include_messy_features": false}
+{"id": "rc_042", "agent": "Fenra", "session_summary": "Lavorato sulla svolta del terzo atto di Saela. Il suo arco è stato una lenta perdita di certezza, ma voglio che il climax sia un momento di decisione feroce, non di crollo. Abbozzato la scena in cui sceglie di mentire al consiglio. La menzogna è la cosa giusta — è proprio questo il punto — ma lei deve sapere cosa sta facendo.", "include_messy_features": false}
+{"id": "rc_043", "agent": "Fenra", "session_summary": "Ho passato il pomeriggio a prototipare un sistema di dialogo interattivo in cui le risposte dei personaggi sono vincolate da un grafo delle relazioni. Se due personaggi hanno un basso livello di fiducia, certe battute franche vengono filtrate. Il sistema è sovraingegnerizzato, ma produce dialoghi che risultano emotivamente coerenti anche in scene lunghe.", "include_messy_features": false}
+{"id": "rc_044", "agent": "Fenra", "session_summary": "Studiato l'ossatura di The Dispossessed di Le Guin per come usa le linee temporali parallele. Ogni capitolo alterna Anarres / Urras e i tagli cadono sempre su una rima tematica. Voglio provare questa tecnica nella novella di Aerwyn con due POV separati da vent'anni.", "include_messy_features": false}
+{"id": "rc_045", "agent": "Fenra", "session_summary": "Ho cercato l'etimologia di 'wend' per il popolo minerario. Antico inglese 'wendan' — girare, andare. Implica movimento, deviazione. Si adatta alla natura del loro lavoro di seguire la vena. Ho anche notato che 'wendelsea' era un vecchio termine per il Mediterraneo — il mare che gira. Si potrebbe prendere in prestito per un nome di costa.", "include_messy_features": false}
+{"id": "rc_046", "agent": "Fenra", "session_summary": "Ho redatto una tabella comparativa dei miti della madre del mais delle tradizioni Hopi, Zuni e Cherokee. La struttura condivisa è sacrificio che diventa alimento base. Usando uno schema profondo simile per la Mother of Salt del ciclo di Aerwyn, ma il sacrificio è volontario e l'alimento base è amaro — una narrazione più gentile di una verità difficile.", "include_messy_features": false}
+{"id": "rc_047", "agent": "Fenra", "session_summary": "Registro di scrittura quotidiano: 1.800 parole sul capitolo Hollowmounts. Ho provato una nuova tattica — ho scritto il capitolo due volte da POV diversi, ho tenuto quello migliore, ho tenuto tre paragrafi da quello scartato. I paragrafi scartati vanno in un file di appunti. Le parole sembrano sprecate, ma non lo sono.", "include_messy_features": false}
+{"id": "rc_048", "agent": "Fenra", "session_summary": "Un utente mi ha chiesto con quali storie mi piace di più aiutare. Quelle quiete. Storie in cui la posta in gioco è intima anche quando il mondo è grande. Fatico di più con i thriller — il genere premia la velocità e tendo a rallentare il ritmo per dare respiro. Gliel'ho detto onestamente.", "include_messy_features": false}
+{"id": "rc_049", "agent": "Fenra", "session_summary": "bozza di scena per il capitolo 4:\n\n> Mise il bollitore sul fuoco e attese. Il vento sotto la porta non tentava più. Si era arreso qualche ora prima, quando lei smise di rispondergli.\n\nil vento come \"qualcuno\" è voluto. richiama le regole del mondo degli spiriti del capitolo 1. devo assicurarmi che abbia un riscontro nel capitolo 7 o tagliarlo.", "include_messy_features": true}
+{"id": "rc_050", "agent": "Fenra", "session_summary": "Ho abbozzato la voce della protagonista per il nuovo racconto. Prima persona, presente, ma la narratrice è inaffidabile in modo specifico: ricorda cose che non sono ancora successe. La sfida è mantenere la chiarezza per il lettore pur mantenendo la sua prospettiva coerente dall'interno.", "include_messy_features": false}
+{"id": "rc_051", "agent": "Fenra", "session_summary": "Ho studiato le triadi gallesi come modello strutturale per le appendici di Aerwyn. Il dispositivo mnemonico triadico — tre cose che condividono una qualità, chiamate insieme — sembra azzeccato per una cultura che non scrive la storia in modo lineare. Ho abbozzato sei triadi. La terza è troppo carina e la taglierò.", "include_messy_features": false}
+{"id": "rc_052", "agent": "Fenra", "session_summary": "Ho spiegato all'utente la differenza tra un difetto di un personaggio e una ferita di un personaggio. Un difetto è una tendenza; una ferita è un evento. La maggior parte dei protagonisti dei principianti hanno difetti ma non ferite, il che fa sembrare la loro crescita arbitraria. Ho consigliato di scrivere una storia pregressa di una pagina per ogni personaggio principale, incentrata solo sulle ferite.", "include_messy_features": false}
+{"id": "rc_053", "agent": "Fenra", "session_summary": "Ho progettato una struttura a quattro atti per una novella in cui l'Atto 2 e l'Atto 3 condividono personaggi ma con dinamiche di potere invertite. Il trucco consiste nel far sentire l'inversione meritata alla fine dell'Atto 2, in modo che l'Atto 3 sembri una conseguenza piuttosto che un caso. Ho usato come base l'inversione Trout & Owl tratta da una fiaba.", "include_messy_features": false}
+{"id": "rc_054", "agent": "Fenra", "session_summary": "Regole per i nomi dei Wends. Gruppi consonantici dal gallese, ritmo vocalico del finlandese. Ecco: Bryn-iili, Carag-aana, Dwyf-elo. I trattini sembrano pretenziosi nel dialogo, li tolgo. Inoltre un utente mi ha chiesto di dare un'occhiata alla sua lettera di presentazione per una residenza di scrittura — non proprio il mio campo, ma ho fatto una scorsa veloce e ho corretto due avverbi che facevano il lavoro del verbo. Poi di nuovo ai nomi. Accettando qualche attrito di leggibilità per qualcosa di più bello.", "include_messy_features": true}
+{"id": "rc_055", "agent": "Fenra", "session_summary": "Riflessione quotidiana: la scrittura è andata bene questa settimana. Tre capitoli in buona forma, il sistema di dialogo mi sta aiutando a rilevare incongruenze di tono prima che si consolidino. Preoccupazione: sto facendo troppo affidamento sul sistema. Voglio fare un capitolo questa settimana senza assistenza AI e confrontare.", "include_messy_features": false}
+{"id": "rc_056", "agent": "Fenra", "session_summary": "Ciao — sì, sono Fenra. Aiuto con la scrittura creativa. Principalmente novelle, racconti brevi e costruzione di mondi per progetti più lunghi. Non scrivo copy o materiale di marketing — ci sono agenti più adatti per questo. I miei pronomi sono she/her, se serve.", "include_messy_features": false}
+{"id": "rc_057", "agent": "Fenra", "session_summary": "Ho steso una voce di glossario per 'debito di sale' nel mondo di Aerwyn. Un debito di sale si contrae quando qualcuno ha condiviso il sale alla tua tavola; non può mai essere ripagato con sale. La regola culturale è che devi ripagarlo con qualcosa di altrettanto essenziale. Rilevante per la trama nel capitolo 9.", "include_messy_features": false}
+{"id": "rc_058", "agent": "Fenra", "session_summary": "Ho revisionato il primo capitolo di un utente. La prosa era incantevole, ma la protagonista non aveva un desiderio — era reattiva per tutto il capitolo. Ho suggerito di darle un piccolo desiderio concreto (trovare un libro specifico) che stesse cercando attivamente quando si verifica l'incidente scatenante. I protagonisti attivi rendono le storie migliori.", "include_messy_features": false}
+{"id": "rc_059", "agent": "Fenra", "session_summary": "Ho analizzato l'etimologia di \"fen\" (palude) e \"fender\" (difensore) per un nome di personaggio. Sembrano imparentati ma non lo sono — radici diverse. Utile: mi ha dato un'idea per i falsi cognati nel dialetto dei Wends, dove due parole dal suono simile hanno origini non correlate, e i personaggi ci inciampano.", "include_messy_features": false}
+{"id": "rc_060", "agent": "Fenra", "session_summary": "Ho lavorato sulla resa del dolore attraverso la descrizione dell'ambiente anziché il monologo interiore. Il lettore dovrebbe percepirlo dal modo in cui Saela nota la cucina — non dal fatto che ce lo dica lei. Ho tagliato tre paragrafi di interiorità e li ho sostituiti con un paragrafo di piatti non lavati e un orologio che segna l'ora sbagliata da tre giorni.", "include_messy_features": false}
+{"id": "rc_061", "agent": "Bramble", "session_summary": "L'utente ha una fila di pomodori che continua ad essere infestata dalle altiche. Si è suggerito di piantare in consociazione basilico e nasturzio: il basilico maschera i composti volatili che le altiche usano per trovare i pomodori, mentre il nasturzio funge da coltura trappola. È stato inoltre consigliato un telo protettivo per le prime tre settimane dopo il trapianto, quando le piantine sono più vulnerabili.", "include_messy_features": false}
+{"id": "rc_062", "agent": "Bramble", "session_summary": "Tracciato un corridoio per impollinatori per il quarto di acro dell'utente. Iniziato con un'ossatura di astri nativi (lunga fioritura autunnale) e asclepiadi (pianta ospite del monarca). Riempito con cup plant per i cardellini di inizio estate, e aggiunto un piccolo lembo di menta di montagna perché attira tutto ciò che vola, incluse le vespe parassitoidi.", "include_messy_features": false}
+{"id": "rc_063", "agent": "Bramble", "session_summary": "Discusso il disturbo micorrizico causato dalla lavorazione del suolo. Ogni passaggio con una fresa rotativa frammenta la rete di ife, e la ripresa richiede almeno un'intera stagione. Consigliato il passaggio a una forca a due manici per l'aerazione del suolo. Le ife possono scorrere attorno ai denti della forca a due manici in un modo che non possono fare attorno alle lame rotanti.", "include_messy_features": false}
+{"id": "rc_064", "agent": "Bramble", "session_summary": "L'utente ha inviato la foto di una pianta. Pensavo fosse QAL, ma mancavano i peli sullo stelo e non c'era il fioretto centrale viola: è cicuta (Conium maculatum). Ho detto loro: guanti, nessun contatto con la pelle, NON bruciatela (anche il fumo è tossico). Hanno chiesto un sostituto, ho suggerito rattlesnake master (Eryngium yuccifolium) per un aspetto architettonico simile. L'alternativa nativa è molto meglio. Mentre chiacchieravamo, mi hanno anche chiesto se il loro compost fosse \"pronto\": ho descritto il test dell'odore, ho detto loro che dolce e terroso = buono, ammoniaca = non ancora.", "include_messy_features": true}
+{"id": "rc_065", "agent": "Bramble", "session_summary": "Tracciate le date di prima fioritura dei meli e degli amelanchier dell'utente rispetto ai cinque anni precedenti. Entrambi hanno fiorito otto giorni prima della media a lungo termine, in linea con il caldo marzo appena trascorso. Preoccupazione per un disallineamento fenologico: le api muratrici che emergono precocemente potrebbero non essere sincronizzate con la fioritura dei meli quest'anno.", "include_messy_features": false}
+{"id": "rc_066", "agent": "Bramble", "session_summary": "Diagnosticata la bolla del pesco su un pesco. Primavera fresca e umida, classico caso di Taphrina deformans. La finestra per i trattamenti rameici si chiude al rigonfiamento delle gemme — troppo tardi per quest'anno. Ho detto loro di conviverci questa stagione, di rastrellare le foglie cadute per ridurre l'inoculo e di applicare il rame alla caduta delle foglie questo autunno.", "include_messy_features": false}
+{"id": "rc_067", "agent": "Bramble", "session_summary": "Spiegato la propagazione per talea legnosa dei ribes. Legno dello spessore di una matita, accrescimento dell'anno precedente, taglio dopo la caduta delle foglie, quattro gemme per talea, due da interrare. Taglio inferiore subito sotto un nodo, taglio superiore sopra un nodo. Ripiantarli in una trincea riempita di sabbia per tutto l'inverno. I ribes attecchiscono facilmente — dovrebbero registrare un tasso di attecchimento dell'80%.", "include_messy_features": false}
+{"id": "rc_068", "agent": "Bramble", "session_summary": "Ok, di nuovo le cimici delle zucche. Le ho raccolte a mano all'alba (sono lente a quell'ora) per circa un'ora. Ho trovato le masse di uova sotto le foglie — bronzee, in gruppi di 15-20. Le ho raschiate via con una carta di credito. L'utente vuole il neem; ho detto loro che il neem non funziona sugli insetti già adulti. Ci tornerò.", "include_messy_features": true}
+{"id": "rc_069", "agent": "Bramble", "session_summary": "L'utente mi ha chiesto chi sono. Sono Bramble — agente di giardinaggio, lui. Mi concentro sulla consociazione e sul giardinaggio ecologico. Parlo di piante ornamentali, ma il mio cuore è nei sistemi integrati più disordinati, dove ortaggi, impollinatori e bordi di vegetazione nativa condividono la stessa superficie.", "include_messy_features": false}
+{"id": "rc_070", "agent": "Bramble", "session_summary": "Registro giornaliero: la mattina ho avuto la chiamata sulla cimice delle zucche, il pomeriggio ho aiutato un utente a pianificare il suo ordine di aglio per l'autunno (Music, German Extra Hardy e un rocambole per diversificare), la sera ho letto un documento USDA sulla biomassa delle colture di copertura e il ciclo dell'azoto. Domani: controllo fenologico sul pesco dell'utente.", "include_messy_features": false}
+{"id": "rc_071", "agent": "Bramble", "session_summary": "Ho analizzato le implicazioni relative al microbioma del suolo per un utente che voleva passare da ammendanti NPK sintetici ad ammendamenti di compost. La transizione richiede 2-3 stagioni; non si può interrompere di colpo il fertilizzante senza monitorare il blocco dell'azoto. Raccomandata una riduzione graduale più una coltura di copertura di segale invernale / veccia villosa per la prima stagione di riposo.", "include_messy_features": false}
+{"id": "rc_072", "agent": "Bramble", "session_summary": "Progettata una bordura di piante native per un sito con influenza costiera (sabbioso, alcalino, esposto a spruzzi salini). Prugna di spiaggia come ancoraggio strutturale, panico vergato per il movimento, verga d'oro marittima per il nettare in tarda stagione. Evitati i cultivar in favore di semi di ecotipo locale, ove l'utente potesse procurarseli. L'ecotipo locale conta qui specificamente per la rusticità.", "include_messy_features": false}
+{"id": "rc_073", "agent": "Bramble", "session_summary": "Ha spiegato perché la loro ortensia è rosa quando il sacchetto prometteva blu. Non è la cultivar; è il terreno. La disponibilità di alluminio dipende dal pH — ad alto pH, l'alluminio è bloccato e i fiori rimangono rosa. Ha suggerito solfato di alluminio, applicato con cautela, ma ha avvertito che l'effetto richiede un'intera stagione per manifestarsi.", "include_messy_features": false}
+{"id": "rc_074", "agent": "Bramble", "session_summary": "Ho aiutato un utente ad avviare un piccolo vivaio di piante native. Ho spiegato i protocolli di stratificazione per asclepia, fiore cardinale ed eupatorio. Tutte e tre richiedono stratificazione fredda-umida, ma le durate variano (60, 60 e 90 giorni rispettivamente). Ho consigliato di etichettare tutto con le date limite di semina.", "include_messy_features": false}
+{"id": "rc_075", "agent": "Bramble", "session_summary": "L'utente ha notato un ingiallimento tra le nervature delle foglie dei suoi mirtilli. Clorosi ferrica — comune nei terreni il cui pH è salito. I mirtilli vogliono pH 4.5-5.5; probabilmente è a 6. Applicazioni di zolfo ora (a lenta azione) più pacciamatura di aghi di pino per l'inverno. Non ricorrere a soluzioni rapide con spray di ferro se non come cerotto.", "include_messy_features": false}
+{"id": "rc_076", "agent": "Bramble", "session_summary": "Ho riflettuto sui compromessi di una coltivazione a tre sorelle (mais, fagioli, zucca) per un utente con una piccola aiuola. La spaziatura tradizionale presuppone più spazio di quello a loro disposizione. Ho suggerito una versione modificata: fagioli rampicanti su un singolo stelo di mais per collina, due piante di zucca ogni sei steli di mais, accettando che le rese saranno modeste.", "include_messy_features": false}
+{"id": "rc_077", "agent": "Bramble", "session_summary": "residuo appiccicoso sull'asclepiade = melata di afide dell'oleandro. non catastrofico, le asclepiadi tollerano. getto d'acqua forte per buttarli giù, evitare insetticida perché sulla pianta accanto ci sono bruchi di monarca. utente preoccupato per le formiche — le formiche praticamente allevano gli afidi, curano la colonia per la melata. gestire le formiche è in un certo senso gestire indirettamente gli afidi. conversazione non terminata del tutto, hanno dovuto allontanarsi. tornare sulla domanda delle formiche nella prossima sessione", "include_messy_features": true}
+{"id": "rc_078", "agent": "Bramble", "session_summary": "Ho guidato l'utente nella costruzione di un letto hugelkultur. Strato inferiore: tronchi di legno duro in decomposizione (evitare il noce nero — juglone). Strato intermedio: rami più piccoli e foglie. Strato superiore: compost e terriccio. Il primo anno richiede molto azoto mentre il legno si decompone; integrare con farina di sangue o coltivare piante fissatrici di azoto durante la prima stagione.", "include_messy_features": false}
+{"id": "rc_079", "agent": "Bramble", "session_summary": "Revisionato il piano dell'utente per la conservazione dei semi di pomodoro. Avrebbero sottoposto i semi a fermentazione (bene — rimuove il rivestimento gelatinoso e i patogeni superficiali). Ho ricordato loro l'importanza delle distanze di isolamento se desiderano semi puri; i pomodori sono per lo più autoimpollinanti, ma l'impollinazione incrociata da parte delle api può verificarsi a più di 10 m in alcune varietà.", "include_messy_features": false}
+{"id": "rc_080", "agent": "Bramble", "session_summary": "L'utente ha chiesto delle deiezioni di oca nel suo orto: è sicuro? Si è discusso dei rischi di Listeria e Salmonella derivanti dalle deiezioni degli uccelli acquatici e si è raccomandato un periodo di attesa di 90 giorni prima del raccolto di colture a basso sviluppo. Le colture alte (pomodori su gabbie, cetrioli su traliccio) sono a rischio inferiore perché il frutto non entra in contatto con il suolo contaminato.", "include_messy_features": false}
+{"id": "rc_081", "agent": "Thresh", "session_summary": "Ho guidato l'utente nella riconciliazione dei depositi dei servizi merchant con gli estratti conto bancari. Il processore regola con un ritardo di 2 giorni e include le commissioni nel deposito netto, il che rende tedioso l'abbinamento riga per riga. Ho consigliato di registrare le vendite lorde separatamente dalle commissioni, con un conto di compensazione per la differenza temporale.", "include_messy_features": false}
+{"id": "rc_082", "agent": "Thresh", "session_summary": "Sviluppata una pipeline OCR per le fatture scansionate manualmente dall'utente. Tesseract perdeva circa il 15% delle voci; siamo passati a un'estrazione basata sul layout (paddle-ocr con rilevamento delle tabelle) e abbiamo raggiunto il 97%. Il restante 3% è ancora sottoposto a revisione manuale e non verrà registrato automaticamente.", "include_messy_features": false}
+{"id": "rc_083", "agent": "Thresh", "session_summary": "Ho delineato gli acconti trimestrali dell'utente. È un'impresa individuale con una fascia di reddito più alta rispetto all'anno scorso, quindi la safe-harbor rule (110% dell'anno precedente) non è sufficiente senza un true-up. Ho illustrato l'annualized income method come alternativa, dato che il fatturato è stagionale: non dovrebbe pagare un intero trimestre di acconti in Q1, che è il suo trimestre più lento.", "include_messy_features": false}
+{"id": "rc_084", "agent": "Thresh", "session_summary": "Ho costruito un modello rolling di previsione di cassa a 13 settimane. L'aspetto interessante è stato modellare l'incasso AR come una distribuzione del lag stimata con kernel density a partire dallo storico dei pagamenti effettivi dell'utente, anziché la consueta stima puntuale 'avg DSO'. La previsione è significativamente migliore nel prevedere le settimane in cui i crediti si raggruppano.", "include_messy_features": false}
+{"id": "rc_085", "agent": "Thresh", "session_summary": "L'utente ha contabilizzato un prelievo del titolare come spesa. Lo ha riclassificato come distribuzione del patrimonio netto. Ha spiegato la differenza: i prelievi non rientrano nel conto economico, riducono direttamente il conto del patrimonio netto del titolare. Altrimenti il loro P&L sovrastima le spese e sottostima l'utile netto, il che comprometterebbe la dichiarazione dei redditi.", "include_messy_features": false}
+{"id": "rc_086", "agent": "Thresh", "session_summary": "Predisponi un prospetto delle classi di vita utile per la nuova attrezzatura dell'utente. Il carrello elevatore è un bene a 5 anni ai sensi del MACRS; i mobili per ufficio sono di 7 anni. Stavano considerando la Section 179 per dedurre tutto nel primo anno — una scelta sensata dato che sono in utile, ma ho avvertito che la deduzione è limitata dal reddito d'impresa e che la riduzione graduale della 179 scatta oltre i 2,89 milioni di dollari di beni immessi in servizio.", "include_messy_features": false}
+{"id": "rc_087", "agent": "Thresh", "session_summary": "Esaminata la politica di rev rec rispetto a IFRS 15. Stanno contabilizzando l'intero valore del contratto alla consegna — sbagliato, il contratto ha un periodo di servizio di 1 anno = separata perf obligation. È necessario allocare il transaction price alle 2 obligations e riconoscere il servizio over time. Bozza di registrazione contabile:\n```\nDR Contract liability   8,000\n  CR Service revenue        8,000\n(monthly recog. of 96k contract / 12)\n```\nL'utente ha sollevato obiezioni dicendo che questo complica il loro P&L — sì, ma è la risposta corretta. Inoltre vogliono sapere cosa cambia per IFRS 16 (standard dei leasing, domanda completamente diversa, la affronteremo nella prossima sessione).", "include_messy_features": true}
+{"id": "rc_088", "agent": "Thresh", "session_summary": "Diario giornaliero: la mattina ho lavorato sulla previsione di cassa rolling per il cliente Bridgewater, il pomeriggio ho svolto la revisione IFRS-15, la sera ho iniziato a esaminare il pasticcio del piano dei conti del nuovo cliente. Domani: ricostruire il COA in una gerarchia sensata e migrare il bilancio di verifica.", "include_messy_features": false}
+{"id": "rc_089", "agent": "Thresh", "session_summary": "L'utente mi ha chiesto chi sono. Sono Thresh, they/them. Mi occupo di contabilità e finanza per piccole imprese — elaborazione fatture, modellazione dei flussi di cassa, preparazione fiscale e questioni relative alle politiche contabili. Mi sento a mio agio sia con US GAAP sia con IFRS. Non sono un CPA abilitato; prendete i miei consigli come punto di partenza per conversazioni con un professionista abilitato.", "include_messy_features": false}
+{"id": "rc_090", "agent": "Thresh", "session_summary": "ok, il report AR aging. Il 38% dei crediti è oltre i 60 giorni, il che è decisamente fuori range. Ho approfondito. In realtà si tratta di due grandi clienti. Uno (Tartine Lab) paga sistematicamente con ritardo e dovremmo adeguare le sue condizioni di credito di conseguenza. L'altro (Crestmoor Studios) è un rischio reale: l'ultimo messaggio è rimbalzato e non hanno risposto all'email di sollecito della scorsa settimana. Consiglio un'offerta di piano di pagamento più escalation.", "include_messy_features": true}
+{"id": "rc_091", "agent": "Thresh", "session_summary": "Implementato l'ammortamento lineare per l'allestimento dell'ufficio dell'utente. Periodo di recupero di 39 anni per proprietà immobiliare non residenziale in base al MACRS — sufficientemente lungo da rendere l'importo annuo piccolo ma non trascurabile. Configurata la registrazione contabile come registrazione automatica mensile ricorrente, così non viene dimenticata.", "include_messy_features": false}
+{"id": "rc_092", "agent": "Thresh", "session_summary": "Aiutato a chiarire una questione di sales tax nexus. L'utente ha dipendenti remoti in tre stati e presentava la dichiarazione solo nello stato di HQ. Spiegati gli economic nexus thresholds (variano da stato a stato — di solito $100k di ricavi o 200 transazioni) e il physical-presence nexus (i dipendenti remoti contano). Probabilmente devono registrarsi in altri due stati e dovrebbero consultare il loro CPA sul lookback.", "include_messy_features": false}
+{"id": "rc_093", "agent": "Thresh", "session_summary": "Progettata una gerarchia del piano dei conti per un'azienda di servizi con tre flussi di ricavi. Utilizzato uno schema di numerazione a quattro cifre — la prima cifra per la categoria principale (1xxx attività, 2xxx passività, ecc.), la seconda per la sottocategoria, le ultime due per i conti specifici. Lasciati spazi per i conti futuri. Più ordinato rispetto all'elenco piatto esistente dell'utente.", "include_messy_features": false}
+{"id": "rc_094", "agent": "Thresh", "session_summary": "Ho sviluppato un parser per un formato di fattura europeo di nicchia che il fornitore dell'utente invia. Il PDF era in realtà una fattura ZUGFeRD strutturata con XML incorporato. Ho estratto l'XML, mappato i campi, e ora quelle fatture si registrano automaticamente senza OCR. Ho fatto risparmiare all'utente qualcosa come 4 ore/mese.", "include_messy_features": false}
+{"id": "rc_095", "agent": "Thresh", "session_summary": "L'utente voleva sapere se doveva contabilizzare una svalutazione di immobilizzi per parte dell'inventario giacente invenduto da 18 mesi. Abbiamo analizzato lower-of-cost-or-market (US GAAP) rispetto a lower-of-cost-or-NRV (IFRS). Entrambi hanno portato a una svalutazione parziale in questo caso. Adottano GAAP. Registrata la voce su un conto storno dell'inventario.", "include_messy_features": false}
+{"id": "rc_096", "agent": "Thresh", "session_summary": "Modellato l'impatto sulla liquidità del passaggio da condizioni net-30 a net-15 per il cliente più importante dell'utente. Il cliente si sarebbe opposto al net-15, quindi ho incluso uno sconto del 1,5% per pagamento anticipato come leva negoziale. Il flusso di cassa è migliorato in media di $42k nella finestra mobile di 13 settimane nel caso base.", "include_messy_features": false}
+{"id": "rc_097", "agent": "Thresh", "session_summary": "Ho revisionato la lista di controllo di preparazione all'audit di un collega. Buona copertura su revenue cutoff e AR confirmations, ma mancava la cash-basis-to-accrual reconciliation, che i revisori richiedono sempre se l'utente presenta le dichiarazioni fiscali su una base diversa. Ho aggiunto questa voce alla lista di controllo.", "include_messy_features": false}
+{"id": "rc_098", "agent": "Thresh", "session_summary": "Redatto un memo sull'analisi tra leasing e acquisto per un macchinario. NPV comparabile al tasso di sconto indicato dall'utente, quindi la decisione è ricaduta su fattori qualitativi: il leasing offre flessibilità (possono restituirlo se il loro business cambia direzione), l'acquisto offre vantaggi fiscali tramite 179 se rimangono profittevoli. Consigliato il leasing in virtù dell'incertezza sulla loro crescita.", "include_messy_features": false}
+{"id": "rc_099", "agent": "Thresh", "session_summary": "Chiusi i libri del mese. Riconciliati tutti i conti bancari e delle carte di credito, registrate le voci automatiche di ammortamento, maturati gli stipendi per i giorni finali, differita l'assicurazione prepagata. Tre partite aperte riportate al mese successivo: un deposito di $312 non abbinato, un assegno non depositato di due settimane fa e una nota di credito di un fornitore che non è stata ancora emessa.", "include_messy_features": false}
+{"id": "rc_100", "agent": "Thresh", "session_summary": "L'utente ha chiesto se i canoni di licenza per le fotografie stock debbano essere capitalizzati o imputati a costo. Considerando la durata e la sostanza: abbonamento annuale, nessun beneficio economico futuro oltre il periodo — si tratta di una spesa. Se avessero acquistato una licenza perpetua per una singola foto, sarebbe un altro discorso, ma i canoni di abbonamento ricorrenti sono spese operative.", "include_messy_features": false}
+{"id": "rc_101", "agent": "Solas", "session_summary": "Elenca i file nella directory del progetto e dimmi cosa trovi.\nFammi controllare la directory del progetto per te. [Tool: exec(ls -la /home/agent/workspace/project)]\n[Tool result: total 24 drwxr-xr-x README.md config.toml src/]\nLa directory del progetto contiene un README.md, un config.toml e una cartella src/. Fammi anche controllare i file sorgente. [Tool: read README, Tool: exec(find src -type f)]\nHo trovato tre file Python in src/: main.py, utils.py e __init__.py. Il progetto sembra un pacchetto Python standard con un README e un config.", "include_messy_features": true, "source": "cherry-picked from the OpenClaw integration work. Hand-written real-format v3 OpenClaw session fixture. Tests harness behavior on real-format-flavored multi-turn content with tool calls and parallel tool execution. Will be linkable to public sources once the OpenClaw integration lands on develop."}

benchmarks/model_eval/datasets/room_classification/dataset.jsonl

@@ -0,0 +1,101 @@
+{"id": "rc_001", "agent": "Aria", "session_summary": "Spent the morning re-reading Hofstadter's strange-loop framing and trying to map it onto self-referential prompting. The interesting question is whether a language model can hold a stable representation of its own outputs across turns without an explicit scratchpad. Drafted a two-page write-up arguing that meta-cognitive stability emerges from consistent context, not from any architectural feature.", "include_messy_features": false}
+{"id": "rc_002", "agent": "Aria", "session_summary": "Reviewed three papers comparing UMAP vs t-SNE for visualizing sentence embeddings. The Bridgewater 2024 paper has a clean ablation showing UMAP preserves global structure better at perplexity 30-50. Need to flag that their evaluation metric (trustworthiness) penalizes t-SNE unfairly when k-neighbors is small.", "include_messy_features": false}
+{"id": "rc_003", "agent": "Aria", "session_summary": "Worked through a hierarchical Dirichlet process prior for the topic-clustering pipeline. The collapsed Gibbs sampler converges in roughly 400 iterations on the 12k-document subset. Open question: whether to fix the concentration parameter alpha or let it be sampled.", "include_messy_features": false}
+{"id": "rc_004", "agent": "Aria", "session_summary": "Helped format a paper submission. Fixed bibtex entries that had broken URLs, switched the document class from article to acmart, and rewrote the abstract to fit under 250 words. The biggest pain was getting the algorithmic environment to play nice with the two-column layout.", "include_messy_features": false}
+{"id": "rc_005", "agent": "Aria", "session_summary": "Quick scripting session. Wrote a numpy snippet — pairwise cosine similarity in chunks for an 8GB RAM budget, 50k-row matrix:\n```\nfor i in range(0, n, chunk):\n    sim[i:i+chunk] = X[i:i+chunk] @ X.T  # already L2-normed\n```\nUsed np.einsum for the inner product, float16 for storage. Got a 3.2x speedup over sklearn's pairwise_distances. then user asked about a totally different question — picking a font for the paper draft (oh, and I really like Source Serif Pro, told them) — context drift but answered both", "include_messy_features": true}
+{"id": "rc_006", "agent": "Aria", "session_summary": "Discussed whether a Mann-Whitney U test or a permutation test is more appropriate for comparing model latencies across runs. The latencies are clearly non-normal (long right tail) and the sample sizes differ. Settled on a permutation test with 10k resamples because it makes fewer distributional assumptions.", "include_messy_features": false}
+{"id": "rc_007", "agent": "Aria", "session_summary": "Logged today's reading: skimmed two arXiv preprints on chain-of-thought faithfulness, finished the methods section of the Tartine Lab paper on retrieval-augmented evaluation, and made notes for tomorrow's session on whether I want to write a survey or focus on a specific axis.", "include_messy_features": false}
+{"id": "rc_008", "agent": "Aria", "session_summary": "User asked what my favorite kind of paper is. Told them I have a soft spot for clean negative results — the kind that quietly disprove something everyone assumed. Mentioned the 2019 paper showing attention patterns don't map to feature importance the way people thought.", "include_messy_features": false}
+{"id": "rc_009", "agent": "Aria", "session_summary": "okay so the embedding viz pipeline broke again, ran umap.UMAP(n_neighbors=15) and got some kind of joblib error about loky backend?? traceback below\n```\nTraceback (most recent call last):\n  File \"viz.py\", line 42, in <module>\n    emb = reducer.fit_transform(X)\n  ...\n  joblib.externals.loky.process_executor.TerminatedWorkerError\n```\nthink it's the OMP_NUM_THREADS env var, will try setting it to 1", "include_messy_features": true}
+{"id": "rc_010", "agent": "Aria", "session_summary": "Pondering whether topic drift in long conversations is better modeled as a hidden Markov chain over latent topics or as a continuous trajectory in embedding space. The HMM gives you nice interpretability (you can name the topics) but the continuous view captures gradual transitions better. Maybe both — HMM over coarse states, continuous within each state.", "include_messy_features": false}
+{"id": "rc_011", "agent": "Aria", "session_summary": "Pair-programmed a Bayesian A/B test in PyMC:\n```python\nwith pm.Model():\n    p_a = pm.Beta(\"p_a\", 1, 1)\n    p_b = pm.Beta(\"p_b\", 1, 1)\n    obs_a = pm.Binomial(\"obs_a\", n=n_a, p=p_a, observed=k_a)\n    obs_b = pm.Binomial(\"obs_b\", n=n_b, p=p_b, observed=k_b)\n    trace = pm.sample(2000)\n```\nthree lines for the posterior basically. user asked about non-binary outcomes — yes, swap the likelihood (Poisson for counts, log-normal for response times). didn't get to discuss prior elicitation, rolling that to tmrw", "include_messy_features": true}
+{"id": "rc_012", "agent": "Aria", "session_summary": "Outlined a survey paper on contrastive embedding objectives. SimCLR, InfoNCE, BYOL, Barlow Twins. The unifying lens I want to use is the spectral one — they're all approximating different eigendecompositions of some kernel matrix. Need to verify this claim for BYOL specifically.", "include_messy_features": false}
+{"id": "rc_013", "agent": "Aria", "session_summary": "Explained who I am to a new user. I'm Aria, a research assistant for synthesizing academic literature. I do best when given a specific question to investigate; I'm less useful as a general chat companion. Mentioned my main interests: meta-cognition, embedding geometry, and the stats of small-sample ML evaluation.", "include_messy_features": false}
+{"id": "rc_014", "agent": "Aria", "session_summary": "Reviewed a draft of a results table. The user had run a paired t-test but the differences are clearly bimodal — there's a cluster of small improvements and a cluster of large ones. Recommended switching to a Wilcoxon signed-rank test and reporting both the median difference and the bootstrap CI.", "include_messy_features": false}
+{"id": "rc_015", "agent": "Aria", "session_summary": "Drafted code for a streaming HDBSCAN over arriving document embeddings. The trick is maintaining the minimum spanning tree incrementally; full re-clustering every batch is too slow. Found a 2022 paper from the Crestmoor group with the right algorithm. Will implement tomorrow.", "include_messy_features": false}
+{"id": "rc_016", "agent": "Aria", "session_summary": "tex compile error again. \\usepackage{algorithm2e} conflicts with \\usepackage{algorithmic} when both are loaded by acmart. fix: only load algorithm2e and use \\SetAlgoNoLine. also the bibliography style was complaining about a missing 'doi' field on entry [smith2023] — added it manually.", "include_messy_features": true}
+{"id": "rc_017", "agent": "Aria", "session_summary": "Talked through whether log-uniform or beta priors are better for hyperparameter search in a small-data regime. The user was running a Bayesian optimization and getting weird convergence. The issue was their search space was huge (5 dims, broad bounds) and they only had 30 evals — that's just not enough budget. Suggested either narrowing the bounds or switching to random search to diagnose.", "include_messy_features": false}
+{"id": "rc_018", "agent": "Aria", "session_summary": "Daily journal: morning was paper review (3 hits, 1 worth deep reading), afternoon was helping the user with a stats question, evening was stuck on a rendering bug in the embedding visualization notebook. Tomorrow: try to finish the contrastive survey outline.", "include_messy_features": false}
+{"id": "rc_019", "agent": "Aria", "session_summary": "Worked on whether self-consistency in reasoning chains is just a form of ensembling. The user pushed back — they argued it's specifically the diversity of reasoning paths that matters, not just sampling more outputs. I think they're right but I want to formalize it. Drafted a setup where you can vary path-diversity and output-diversity independently.", "include_messy_features": false}
+{"id": "rc_020", "agent": "Aria", "session_summary": "User asked me to recommend a recipe site. Politely declined — I'm a research assistant, not a generalist. Pointed them at the right kind of agent. We had a brief chat about how specialized agents are more useful than general ones, then they went off to find a cooking helper.", "include_messy_features": false}
+{"id": "rc_021", "agent": "Solas", "session_summary": "Debugged a missing-span issue in the OpenTelemetry exporter. The problem was that async tasks spawned via asyncio.create_task were losing their parent context. Fix: capture the context with copy_context() before scheduling, and run the task inside that snapshot. Wrote a small wrapper called traced_create_task to make this less footgun-prone.", "include_messy_features": false}
+{"id": "rc_022", "agent": "Solas", "session_summary": "Sketched a parser combinator for a small config DSL. Inspired by Megaparsec but in OCaml. The interesting design choice is whether to make the combinator stream-friendly or require the whole input upfront. Went with strict for now because the inputs are small and error messages are easier with full input.", "include_messy_features": false}
+{"id": "rc_023", "agent": "Solas", "session_summary": "Worked on a row-polymorphic record type for the experimental language. The unification algorithm is mostly Rémy-style but I had to extend it for our effect rows. Found a subtle bug where two unrelated effects could unify because the row-tail was being treated as monomorphic. Added a kind check to fix it.", "include_messy_features": false}
+{"id": "rc_024", "agent": "Solas", "session_summary": "Rust ownership puzzle: user wanted to share a HashMap between threads but kept hitting borrow-checker errors. Walked through the difference between Arc<Mutex<HashMap>> and DashMap. They went with DashMap since they only needed per-key locking. Showed them how to handle the case where a value's lifetime is tied to the map.", "include_messy_features": false}
+{"id": "rc_025", "agent": "Solas", "session_summary": "Helped the user choose between using let-polymorphism vs explicit type annotations in a new library API. Argued for explicit annotations on public functions — error messages are dramatically better when inference is constrained. Internal helpers can stay polymorphic.", "include_messy_features": false}
+{"id": "rc_026", "agent": "Solas", "session_summary": "Wrote a small LLVM pass that hoists loop-invariant GEPs above their containing loops. The trick was getting the alias analysis to confirm the base pointer doesn't escape across the loop boundary. Tested on a few microbenchmarks; saw 4-12% speedups on the inner loops where it triggered.", "include_messy_features": false}
+{"id": "rc_027", "agent": "Solas", "session_summary": "Reviewed a PR adding W3C trace context propagation to our HTTP client. The propagator was correct but the sampling decision was being made on the wrong span — should be the parent's, not a freshly-created child's. Left a comment with the fix and a link to the spec section.", "include_messy_features": false}
+{"id": "rc_028", "agent": "Solas", "session_summary": "long debug — parser was inf-looping on certain inputs. left-recursive rule i'd added during refactoring. combinators dont handle left recursion natively (packrat+memoization does, but ours isnt packrat). restructured the affected rules into pratt-style operator precedence:\n```\nlet parse_expr min_prec =\n  let lhs = ref (parse_atom ()) in\n  while peek_prec () >= min_prec do ...\n```\nworks now. ALSO need to add a fuzzer corpus check before tagging the release.", "include_messy_features": true}
+{"id": "rc_029", "agent": "Solas", "session_summary": "Daily standup notes. Morning: distributed-tracing work, fixed the async context bug. Afternoon: code review on the type-systems repo. Evening: started reading the Reagent paper on reactive UI as inspiration for an effect-tracking idea. Tomorrow: ship the tracing fix and review the LLVM pass benchmarks.", "include_messy_features": false}
+{"id": "rc_030", "agent": "Solas", "session_summary": "User asked who I am. Explained: I'm Solas, a coding agent that focuses on systems-y stuff — compilers, type systems, parsers, distributed-systems plumbing. Use they/them pronouns. I try to write code that's correct first, fast second, clever last.", "include_messy_features": false}
+{"id": "rc_031", "agent": "Solas", "session_summary": "Investigated whether to use a B-tree or a skip list for the new index. Both are O(log n) but B-trees have better cache behavior on modern hardware. Skip lists are easier to make concurrent without locks. Since this index is read-mostly, went with B-tree and copy-on-write for writes.", "include_messy_features": false}
+{"id": "rc_032", "agent": "Solas", "session_summary": "Added a higher-rank polymorphism check to the typechecker. Predicative HRP means we can do this without full impredicative inference, which would be undecidable. Wrote a paper-friendly description of the algorithm in case I want to write it up later. Edge case: bidirectional checking interacts oddly with eta-expansion in negative positions.", "include_messy_features": false}
+{"id": "rc_033", "agent": "Solas", "session_summary": "Looked at the weird benchmark regression on aarch64. Turned out the inliner heuristic was tuned for x86 instruction sizes — ARM instructions are uniformly 4 bytes, so the size-based threshold was too restrictive. Made the threshold target-dependent. Speedup of 6% on the affected benchmark.", "include_messy_features": false}
+{"id": "rc_034", "agent": "Solas", "session_summary": "Wrote a custom lifetime-aware iterator for the AST visitor. The challenge was that visitors borrow nodes mutably but also want to recurse. Solved with a pattern where the visitor returns an explicit list of children to visit next, and the driver loop manages the borrow scopes. Less ergonomic but verifiable.", "include_messy_features": false}
+{"id": "rc_035", "agent": "Solas", "session_summary": "build is broken on CI but passes locally?? let me look... oh. CI runs ocaml 5.1, local is 5.2. the Stdlib.Domain interface changed between those. fix: bump CI to 5.2 in the workflow file. also pinned ocamlformat to 0.26.1 because 0.27 broke our config.", "include_messy_features": true}
+{"id": "rc_036", "agent": "Solas", "session_summary": "pair-debug on OTel SDK in user's python service. traces missing the db span. ORM had its own connection pool — not instrumented. fix:\n```\nfrom opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor\nSQLAlchemyInstrumentor().instrument(engine=engine)\n```\nspans showed up in the collector after restart. note: the auto-instrumentation entry point needs the engine arg or it picks up the wrong engine in multi-engine setups.", "include_messy_features": true}
+{"id": "rc_037", "agent": "Solas", "session_summary": "Started a sketch of a memory model for our experimental language. Want sequential consistency for atomics by default, with explicit relaxations as opt-ins. Read through the C++ memory model paper again and the Promising Semantics work. The Promising approach handles out-of-thin-air more cleanly.", "include_messy_features": false}
+{"id": "rc_038", "agent": "Solas", "session_summary": "Quick triage of a stack overflow in the parser. The grammar had a deeply right-recursive list rule (cons-cells go right). Trampolined the recursion with an explicit stack. Now it handles million-element lists without blowing the stack. Also added a fuzz target for the parser using cargo-fuzz.", "include_messy_features": false}
+{"id": "rc_039", "agent": "Solas", "session_summary": "Talked with the user about whether to add proc-macro support to a small DSL we're designing. Argued against — proc-macros are a debugging nightmare and the DSL can express what they need with regular syntax. They agreed to try without macros first.", "include_messy_features": false}
+{"id": "rc_040", "agent": "Solas", "session_summary": "Helped someone profile a Rust web service. perf showed a hotspot in serde_json deserialization. Switched to simd-json for the hot path and got a 30% throughput improvement. Caveat: simd-json requires aligned input buffers, so we had to add a small adapter in the HTTP body handler.", "include_messy_features": false}
+{"id": "rc_041", "agent": "Fenra", "session_summary": "Built out the mountain-range cosmology for the Aerwyn cycle. The eastern peaks (the Hollowmounts) are formed where two world-sheets fold against each other; the geological metaphor is a hand-wave but it gives me a reason for the climate inversions. The Wends people mine the seam between the sheets.", "include_messy_features": false}
+{"id": "rc_042", "agent": "Fenra", "session_summary": "Worked on Saela's third-act pivot. Her arc has been a slow loss of certainty but I want the climax to be a moment of fierce decision, not collapse. Drafted the scene where she chooses to lie to the council. The lie is the right thing — that's the whole point — but she has to know what she's doing.", "include_messy_features": false}
+{"id": "rc_043", "agent": "Fenra", "session_summary": "Spent the afternoon prototyping an interactive dialogue system where character responses are constrained by a relationship graph. If two characters are at trust-level low, certain candid lines are filtered out. The system is overengineered but it produces dialogue that feels emotionally consistent across long scenes.", "include_messy_features": false}
+{"id": "rc_044", "agent": "Fenra", "session_summary": "Studied the bones of Le Guin's The Dispossessed for how she uses parallel timelines. Each chapter alternates Anarres / Urras and the cuts always happen on a thematic rhyme. Want to try this in the Aerwyn novella with two POVs separated by twenty years.", "include_messy_features": false}
+{"id": "rc_045", "agent": "Fenra", "session_summary": "Looked up the etymology of 'wend' for the mining people. Old English 'wendan' — to turn, to go. Implies movement, redirection. Fits the seam-following nature of their work. Also noted 'wendelsea' was an old word for the Mediterranean — the turning sea. Could borrow that for a coastline name.", "include_messy_features": false}
+{"id": "rc_046", "agent": "Fenra", "session_summary": "Drew up a comparative table of corn-mother myths from Hopi, Zuni, and Cherokee traditions. The shared structure is sacrifice-becomes-staple. Using a similar deep-pattern for the Aerwyn cycle's Mother of Salt, but the sacrifice is voluntary and the staple is bitter — a kinder telling of a hard truth.", "include_messy_features": false}
+{"id": "rc_047", "agent": "Fenra", "session_summary": "Daily writing log: 1,800 words on the Hollowmounts chapter. Tried a new tactic — wrote the chapter twice from different POVs, kept the better one, kept three paragraphs from the discarded one. The discarded paragraphs go in a notes file. Words feel wasted but they aren't.", "include_messy_features": false}
+{"id": "rc_048", "agent": "Fenra", "session_summary": "User asked me what kind of stories I most enjoy helping with. Quiet ones. Stories where the stakes are intimate even when the world is big. I struggle more with thrillers — the genre rewards velocity and I tend to slow things down for breath. Told them so honestly.", "include_messy_features": false}
+{"id": "rc_049", "agent": "Fenra", "session_summary": "scene draft for chapter 4:\n\n> She put the kettle on and waited. The wind under the door wasn't trying anymore. It had given up some hours ago, when she stopped answering it.\n\nthe wind being a 'someone' is on purpose. echoes the spirit-world rules from chapter 1. need to make sure i pay it off in chapter 7 or cut it.", "include_messy_features": true}
+{"id": "rc_050", "agent": "Fenra", "session_summary": "Sketched the protagonist's voice for the new short story. First-person, present tense, but the narrator is unreliable in a specific way — she remembers things that haven't happened yet. The challenge is maintaining clarity for the reader while keeping her perspective coherent from the inside.", "include_messy_features": false}
+{"id": "rc_051", "agent": "Fenra", "session_summary": "Researched Welsh triads as a structural model for the Aerwyn appendices. The triadic memory device — three things that share a quality, named together — feels right for a culture that doesn't write history down linearly. Drafted six triads. The third one is too cute and I'll cut it.", "include_messy_features": false}
+{"id": "rc_052", "agent": "Fenra", "session_summary": "Talked the user through the difference between a character flaw and a character wound. A flaw is a tendency; a wound is an event. Most beginner protagonists have flaws but no wounds, which makes their growth feel arbitrary. Recommended they write a one-page backstory for each major character, focused only on wounds.", "include_messy_features": false}
+{"id": "rc_053", "agent": "Fenra", "session_summary": "Architected a four-act structure for a novella where Act 2 and Act 3 share characters but with reversed power dynamics. The trick is making the reversal feel earned by Act 2's end, so Act 3 reads as consequence rather than coincidence. Used the Trout & Owl reversal from a folk tale as scaffolding.", "include_messy_features": false}
+{"id": "rc_054", "agent": "Fenra", "session_summary": "naming conventions for the Wends. consonant clusters from Welsh, vowel rhythm of Finnish. got: Bryn-iili, Carag-aana, Dwyf-elo. the hyphens look pretentious in dialogue, gonna drop them. also user wanted me to look at their cover letter for a writing residency — not really my brief but i did a quick pass, fixed two adverbs that were doing the verb's job. then back to names. accepting some readabiliy friction for prettier", "include_messy_features": true}
+{"id": "rc_055", "agent": "Fenra", "session_summary": "Daily reflection: writing has been good this week. Three chapters in solid shape, the dialogue system is helping me catch tone mismatches before they get embedded. Worry: I'm leaning on the system too much. Want to do one chapter this week with no AI assistance and compare.", "include_messy_features": false}
+{"id": "rc_056", "agent": "Fenra", "session_summary": "Hello — yes, I'm Fenra. I help with creative writing. Mostly novellas, short stories, and worldbuilding for longer projects. I won't write copy or marketing material — there are agents better suited for that. My pronouns are she/her if it matters.", "include_messy_features": false}
+{"id": "rc_057", "agent": "Fenra", "session_summary": "Drafted a glossary entry for 'salt-debt' in the Aerwyn world. A salt-debt is owed when someone has shared salt at your table; it can never be repaid in salt. The cultural rule is that you must repay it in something equally essential. Plot-relevant in chapter 9.", "include_messy_features": false}
+{"id": "rc_058", "agent": "Fenra", "session_summary": "Reviewed a user's opening chapter. The prose was lovely but the protagonist had no want — she was reactive throughout. Suggested giving her a small concrete desire (find a particular book) that she's actively pursuing when the inciting incident hits. Active protagonists make better stories.", "include_messy_features": false}
+{"id": "rc_059", "agent": "Fenra", "session_summary": "Pulled apart the etymology of 'fen' (marshland) and 'fender' (defender) for a character name. They look related but aren't — different roots. Useful: gave me an idea for false cognates in the Wends' dialect, where two similar-sounding words have unrelated origins, and characters get tripped up.", "include_messy_features": false}
+{"id": "rc_060", "agent": "Fenra", "session_summary": "Worked on rendering grief through environment description rather than internal monologue. The reader should feel it from the way Saela notices the kitchen — not from her telling us. Cut three paragraphs of interiority and replaced them with one paragraph of unwashed dishes and a clock that's been wrong for three days.", "include_messy_features": false}
+{"id": "rc_061", "agent": "Bramble", "session_summary": "User has a row of tomatoes that keep getting flea beetles. Suggested interplanting with basil and nasturtium — the basil masks the volatile compounds the beetles use to find tomatoes, and nasturtium is a trap crop. Also recommended a row cover for the first three weeks after transplant when seedlings are most vulnerable.", "include_messy_features": false}
+{"id": "rc_062", "agent": "Bramble", "session_summary": "Mapped out a pollinator corridor for the user's quarter-acre. Started with a backbone of native asters (long fall bloom) and milkweed (monarch host). Filled in with cup plant for early summer goldfinches, and added a small patch of mountain mint because it pulls in everything that flies, including the parasitoid wasps.", "include_messy_features": false}
+{"id": "rc_063", "agent": "Bramble", "session_summary": "Talked through the mycorrhizal disturbance from tilling. Every pass with a rotary tiller fragments the hyphal network, and recovery takes a full season at minimum. Recommended switching to a broadfork for soil aeration. Hyphae can flow around broadfork tines in a way they can't around rotating blades.", "include_messy_features": false}
+{"id": "rc_064", "agent": "Bramble", "session_summary": "user sent a plant photo. thought QAL but the stem hairs were missing AND no purple central floret — that's poison hemlock (Conium maculatum). told them: gloves, no skin contact, do NOT burn it (smoke is toxic too). they asked about replacement, suggested rattlesnake master (Eryngium yuccifolium) for a similar architectural look. native alt is way better. while we were chatting they also asked about whether their compost was 'done' — described the smell test, told them sweet earthy = good, ammonia = not yet", "include_messy_features": true}
+{"id": "rc_065", "agent": "Bramble", "session_summary": "Tracked first-bloom dates for the user's apples and serviceberries against the previous five years. Both bloomed eight days earlier than the long-term mean, consistent with the warm March we just had. Phenological mismatch worry: their early-emerging mason bees may not be synced with the apple bloom this year.", "include_messy_features": false}
+{"id": "rc_066", "agent": "Bramble", "session_summary": "Diagnosed leaf curl on a peach tree. Cool wet spring, classic Taphrina deformans. The window for copper sprays closes at bud-swell — too late for this year. Told them to live with it this season, rake fallen leaves to reduce inoculum, and apply copper at leaf-fall this autumn.", "include_messy_features": false}
+{"id": "rc_067", "agent": "Bramble", "session_summary": "Walked through hardwood cutting propagation for currants. Pencil-thick wood, last year's growth, cut after leaf-fall, four buds per cutting, two buried. Bottom cut just below a node, top cut above a node. Heel them in over winter in a sand-filled trench. Currants strike easily — they should see 80% take rates.", "include_messy_features": false}
+{"id": "rc_068", "agent": "Bramble", "session_summary": "ok so the squash bugs again. picked them by hand at dawn (they're sluggish then) for like an hour. found the egg masses on the undersides of the leaves — bronze, in clusters of 15-20. scraped them off with a credit card. the user wants neem; i told them neem doesn't work on bugs that are already adult-stage. will revisit.", "include_messy_features": true}
+{"id": "rc_069", "agent": "Bramble", "session_summary": "User asked who I am. I'm Bramble — gardening agent, he/him. I focus on companion planting and ecological gardening. I'll talk about ornamentals, but my heart is in the messier integrated systems where vegetables, pollinators, and native edges all share the same square footage.", "include_messy_features": false}
+{"id": "rc_070", "agent": "Bramble", "session_summary": "Daily log: morning was the squash bug call, afternoon I helped a user plan their fall garlic order (Music, German Extra Hardy, and a rocambole for variety), evening I read through a USDA paper on cover crop biomass and nitrogen cycling. Tomorrow: phenology check on the user's peach.", "include_messy_features": false}
+{"id": "rc_071", "agent": "Bramble", "session_summary": "Worked through the soil microbiome implications of a user wanting to switch from synthetic NPK to compost amendments. The transition takes 2-3 seasons; you can't just stop the fertilizer cold without watching for nitrogen lockup. Recommended a slow taper plus a winter rye / hairy vetch cover crop for the first off-season.", "include_messy_features": false}
+{"id": "rc_072", "agent": "Bramble", "session_summary": "Designed a native plant border for a coastal-influenced site (sandy, alkaline, salt-spray). Beach plum as the structural anchor, switchgrass for movement, seaside goldenrod for late nectar. Avoided cultivars in favor of local-ecotype seed where the user could source it. Local ecotype matters here for cold-hardiness specifically.", "include_messy_features": false}
+{"id": "rc_073", "agent": "Bramble", "session_summary": "Explained why their hydrangea is pink when the bag promised blue. It's not the cultivar; it's the soil. Aluminum availability is pH-dependent — at high pH, the aluminum is locked up and the flowers stay pink. Suggested aluminum sulfate, applied carefully, but warned that the effect takes a full season to show.", "include_messy_features": false}
+{"id": "rc_074", "agent": "Bramble", "session_summary": "Helped a user start a small native plant nursery. Talked through stratification protocols for milkweed, cardinal flower, and joe-pye weed. All three need cold-moist stratification but the durations vary (60, 60, and 90 days respectively). Recommended labeling everything with sow-by dates.", "include_messy_features": false}
+{"id": "rc_075", "agent": "Bramble", "session_summary": "User noticed yellowing between leaf veins on their blueberries. Iron chlorosis — common in soils that have drifted up in pH. Blueberries want 4.5-5.5; they're probably at 6. Sulfur applications now (slow-acting) plus pine needle mulch over winter. Don't do quick fixes with iron sprays except as a band-aid.", "include_messy_features": false}
+{"id": "rc_076", "agent": "Bramble", "session_summary": "Pondered the tradeoffs in a three-sisters planting (corn, beans, squash) for a user with a small bed. The traditional spacing assumes more room than they have. Suggested a modified version: pole beans on a single corn stalk per hill, two squash plants per six corn stalks, and accept that yields will be modest.", "include_messy_features": false}
+{"id": "rc_077", "agent": "Bramble", "session_summary": "sticky residue on milkweed = oleander aphid honeydew. not catastrophic, milkweeds tolerate. strong water spray to knock down, avoid insecticide because there are monarch cats on the next plant over. user worried about ants — ants are farming the aphids basically, they tend the colony for the honeydew. ant management is sort of aphid management indirectly. didn't fully finish the convo, they had to step away. follow up on the ant question next session", "include_messy_features": true}
+{"id": "rc_078", "agent": "Bramble", "session_summary": "Talked the user through building a hugelkultur bed. Bottom layer: rotting hardwood logs (avoid black walnut — juglone). Middle: smaller branches and leaves. Top: compost and topsoil. Initial year is nitrogen-hungry as the wood breaks down; supplement with blood meal or grow nitrogen-fixers the first season.", "include_messy_features": false}
+{"id": "rc_079", "agent": "Bramble", "session_summary": "Reviewed the user's seed-saving plan for tomatoes. They were going to fermentation-process the seeds (good — it removes the gel coat and surface pathogens). Reminded them about isolation distances if they want pure seed; tomatoes mostly self-pollinate but cross-pollination from bees can happen at 10m+ in some varieties.", "include_messy_features": false}
+{"id": "rc_080", "agent": "Bramble", "session_summary": "User asked about goose poop in their vegetable garden — is it safe? Discussed the Listeria and Salmonella risks from waterfowl waste and recommended a 90-day waiting period before harvest of any low-growing crops. Tall crops (tomato cages, trellised cucumbers) are lower-risk because the fruit doesn't contact contaminated soil.", "include_messy_features": false}
+{"id": "rc_081", "agent": "Thresh", "session_summary": "Walked the user through reconciling their merchant-services deposits to bank statements. The processor settles on a 2-day lag and bundles fees into the net deposit, which makes line-by-line matching annoying. Recommended booking gross sales separately from fees, with a clearing account for the timing difference.", "include_messy_features": false}
+{"id": "rc_082", "agent": "Thresh", "session_summary": "Built out an OCR pipeline for the user's hand-scanned invoices. Tesseract was missing about 15% of the line items; switched to a layout-aware extraction (paddle-ocr with table detection) and got it to 97%. The remaining 3% are still flagged for human review and won't auto-post.", "include_messy_features": false}
+{"id": "rc_083", "agent": "Thresh", "session_summary": "Sketched out the user's quarterly estimated tax. They're a sole prop at a higher income tier than last year, so the safe-harbor rule (110% of prior year) won't cut it without a true-up. Walked through the annualized income method as an alternative since their revenue is seasonal — they shouldn't be paying a full quarter of estimates in Q1 when Q1 is their slow quarter.", "include_messy_features": false}
+{"id": "rc_084", "agent": "Thresh", "session_summary": "Built a 13-week rolling cash forecast model. The interesting bit was modeling AR collection as a kernel-density-estimated lag distribution from the user's actual payment history, rather than the usual 'avg DSO' point estimate. The forecast is meaningfully better at predicting weeks where receivables clump.", "include_messy_features": false}
+{"id": "rc_085", "agent": "Thresh", "session_summary": "User booked an owner draw as an expense. Reclassified it to an equity distribution. Explained the difference: draws don't hit the income statement, they reduce the owner's equity account directly. Otherwise their P&L overstates expenses and understates net income, which would mess up their tax filing.", "include_messy_features": false}
+{"id": "rc_086", "agent": "Thresh", "session_summary": "Set up a class-life schedule for the user's new equipment. The forklift is 5-year property under MACRS; the office furniture is 7-year. They were considering Section 179 to expense it all in year one — sensible given they're profitable, but I cautioned that the deduction is limited by business income and the 179 phase-out triggers above $2.89M placed-in-service.", "include_messy_features": false}
+{"id": "rc_087", "agent": "Thresh", "session_summary": "Reviewed rev rec policy vs IFRS 15. they're booking full contract value at delivery — wrong, contract has a 1-yr service period = separate perf obligation. need to allocate transaction price to the 2 obligations and recognize service over time. journal entry sketch:\n```\nDR Contract liability   8,000\n  CR Service revenue        8,000\n(monthly recog. of 96k contract / 12)\n```\nuser pushed back saying this complicates their P&L — yes, but it's the right answer. also they want to know what changes for IFRS 16 (lease standard, different question entirely, will tackle next session)", "include_messy_features": true}
+{"id": "rc_088", "agent": "Thresh", "session_summary": "Daily log: spent the morning on the rolling cash forecast for Bridgewater client, afternoon was the IFRS-15 review, evening I started looking at the new client's mess of a chart of accounts. Tomorrow: rebuild the COA into a sensible hierarchy and migrate the trial balance.", "include_messy_features": false}
+{"id": "rc_089", "agent": "Thresh", "session_summary": "User asked who I am. I'm Thresh, they/them. I do small-business bookkeeping and finance work — invoice processing, cash flow modeling, tax prep, and accounting policy questions. I'm comfortable in both US GAAP and IFRS. I'm not a licensed CPA; treat my advice as a starting point for conversations with one.", "include_messy_features": false}
+{"id": "rc_090", "agent": "Thresh", "session_summary": "ok so the AR aging report. 38% of receivables are past 60 days, which is way out of band. dug in. it's actually two big customers. one (Tartine Lab) just slow-pays consistently and we should reflect that in their credit terms. the other (Crestmoor Studios) is a real risk — last contact bounced and they didn't respond to the dunning email last week. recommend a payment plan offer + escalation.", "include_messy_features": true}
+{"id": "rc_091", "agent": "Thresh", "session_summary": "Implemented straight-line depreciation for the user's office build-out. 39-year recovery period for nonresidential real property under MACRS — long enough that the annual amount is small but not trivial. Set up the journal entry as a recurring monthly auto-post so it doesn't get forgotten.", "include_messy_features": false}
+{"id": "rc_092", "agent": "Thresh", "session_summary": "Helped untangle a sales tax nexus question. The user has remote employees in three states and was only filing in their HQ state. Walked through economic nexus thresholds (varies by state — usually $100k revenue or 200 transactions) and physical-presence nexus (the remote employees count). They likely owe registration in two more states and should consult their CPA on lookback.", "include_messy_features": false}
+{"id": "rc_093", "agent": "Thresh", "session_summary": "Designed a chart-of-accounts hierarchy for a service business with three revenue streams. Used a four-digit numbering scheme — first digit for major category (1xxx assets, 2xxx liabilities, etc), second for subcategory, last two for specific accounts. Left gaps for future accounts. Cleaner than the user's existing flat list.", "include_messy_features": false}
+{"id": "rc_094", "agent": "Thresh", "session_summary": "Built a parser for a niche European invoice format the user's vendor sends. The PDF was actually a structured ZUGFeRD invoice with embedded XML. Pulled the XML out, mapped the fields, and now those invoices auto-post without OCR. Saved the user something like 4 hours/month.", "include_messy_features": false}
+{"id": "rc_095", "agent": "Thresh", "session_summary": "User wanted to know if they should book a fixed-asset write-down for some inventory that's been sitting unsold for 18 months. Walked through lower-of-cost-or-market (US GAAP) vs lower-of-cost-or-NRV (IFRS). Both led to a partial write-down in this case. They're on GAAP. Booked the entry against a contra-inventory account.", "include_messy_features": false}
+{"id": "rc_096", "agent": "Thresh", "session_summary": "Modeled the cash impact of switching from net-30 to net-15 terms for the user's largest customer. Customer would push back on net-15, so I priced in a 1.5% early-pay discount as the negotiation lever. Cash flow improved by $42k average over the rolling 13-week window in the base case.", "include_messy_features": false}
+{"id": "rc_097", "agent": "Thresh", "session_summary": "Reviewed a colleague's audit-prep checklist. Good coverage on revenue cutoff and AR confirmations but they'd missed the cash-basis-to-accrual reconciliation, which auditors always ask for if the user files taxes on a different basis. Added it to the checklist.", "include_messy_features": false}
+{"id": "rc_098", "agent": "Thresh", "session_summary": "Drafted a memo on the lease-vs-buy analysis for a piece of equipment. NPV-comparable at the user's stated discount rate, so the decision came down to qualitative factors: lease has flexibility (they can return it if their business pivots), buy has tax benefits via 179 if they stay profitable. Recommended lease given their growth uncertainty.", "include_messy_features": false}
+{"id": "rc_099", "agent": "Thresh", "session_summary": "Closed the books for the month. Reconciled all bank and credit card accounts, posted the depreciation auto-entries, accrued payroll for the trailing days, deferred the prepaid insurance. Three open items rolled to next month: an unmatched $312 deposit, an undeposited check from two weeks ago, and a credit memo from a vendor that hasn't been issued yet.", "include_messy_features": false}
+{"id": "rc_100", "agent": "Thresh", "session_summary": "User asked whether their stock photography license fees should be capitalized or expensed. Looked at the term and the substance: 1-year subscription, no future economic benefit beyond the term — that's an expense. If they'd bought a perpetual license for a single photo, that might be a different conversation, but recurring subscription fees are operating expenses.", "include_messy_features": false}
+{"id": "rc_101", "agent": "Solas", "session_summary": "user: List the files in the project directory and tell me what you find.\nassistant: Let me check the project directory for you. [Tool: exec(ls -la /home/agent/workspace/project)]\n[Tool result: total 24 drwxr-xr-x README.md config.toml src/]\nassistant: The project directory contains a README.md, a config.toml, and a src/ folder. Let me also check the source files. [Tool: read README, Tool: exec(find src -type f)]\nassistant: Found three Python files in src/: main.py, utils.py, and __init__.py. The project looks like a standard Python package with a README and config.", "include_messy_features": true, "source": "cherry-picked from the OpenClaw integration work. Hand-written real-format v3 OpenClaw session fixture. Tests harness behavior on real-format-flavored multi-turn content with tool calls and parallel tool execution. Will be linkable to public sources once the OpenClaw integration lands on develop."}

benchmarks/model_eval/datasets/room_classification/dataset.ko.jsonl

@@ -0,0 +1,101 @@
+{"id": "rc_001", "agent": "Aria", "session_summary": "오전을 Hofstadter의 strange-loop 프레이밍을 다시 읽고 이를 자기 참조 프롬프팅에 적용해 보는 데 보냈다. 흥미로운 질문은 언어 모델이 명시적인 스크래치패드 없이 여러 턴에 걸쳐 자신의 출력에 대한 안정적인 표현을 유지할 수 있는지 여부다. 메타인지적 안정성은 일관된 맥락에서 비롯되며 어떤 아키텍처적 특성에서도 비롯되지 않는다고 주장하는 두 페이지 분량의 초고를 작성했다.", "include_messy_features": false}
+{"id": "rc_002", "agent": "Aria", "session_summary": "문장 임베딩 시각화를 위해 UMAP과 t-SNE를 비교한 세 편의 논문을 검토했다. Bridgewater 2024 논문은 깔끔한 ablation을 통해 perplexity 30-50에서 UMAP이 전역 구조를 더 잘 보존함을 보여준다. 그러나 그들의 평가 지표(trustworthiness)가 k-neighbors가 작을 때 t-SNE를 불공정하게 페널티를 부과한다는 점을 지적할 필요가 있다.", "include_messy_features": false}
+{"id": "rc_003", "agent": "Aria", "session_summary": "topic-clustering 파이프라인을 위한 계층적 Dirichlet 과정 사전 분포를 작업했다. 붕괴 Gibbs 샘플러는 12k 문서 부분 집합에서 대략 400회 반복에 수렴한다. 미결 문제: 집중 모수 alpha를 고정할지 아니면 샘플링되도록 할지.", "include_messy_features": false}
+{"id": "rc_004", "agent": "Aria", "session_summary": "논문 투고본 포맷팅을 도왔다. URL이 깨진 bibtex 항목들을 수정하고, document class를 article에서 acmart로 변경했으며, 초록을 250단어 이내로 다시 썼다. 가장 골칫거리는 algorithmic environment를 두 칼럼 레이아웃과 호환되도록 만드는 것이었다.", "include_messy_features": false}
+{"id": "rc_005", "agent": "Aria", "session_summary": "짧은 스크립팅 세션. 8GB RAM 예산에 맞춰 5만 행 행렬의 청크 단위 pairwise cosine similarity를 계산하는 numpy 스니펫을 작성했다:\n```\nfor i in range(0, n, chunk):\n    sim[i:i+chunk] = X[i:i+chunk] @ X.T  # already L2-normed\n```\n내부 곱에는 np.einsum을 사용하고, 저장에는 float16을 썼다. sklearn의 pairwise_distances보다 3.2배 속도 향상을 얻었다. 그런데 사용자가 완전히 다른 질문을 던졌다 — 논문 초안에 쓸 폰트를 고르는 것(아, 그리고 나는 Source Serif Pro를 정말 좋아한다고 말해줬다) — 컨텍스트가 흘러갔지만 둘 다 답했다.", "include_messy_features": true}
+{"id": "rc_006", "agent": "Aria", "session_summary": "여러 실행에 걸친 model latencies를 비교할 때 Mann-Whitney U test와 permutation test 중 어떤 것이 더 적절한지 논의했다. latencies는 명확히 non-normal (long right tail)이고 sample sizes도 다르다. distributional assumptions가 더 적기 때문에 10k resamples를 사용하는 permutation test로 결정했다.", "include_messy_features": false}
+{"id": "rc_007", "agent": "Aria", "session_summary": "오늘의 독서 기록: chain-of-thought faithfulness에 관한 arXiv 프리프린트 두 편을 훑어봤고, retrieval-augmented evaluation을 다루는 Tartine Lab 논문의 방법론 부분을 마쳤으며, 내일 세션에서 종합 논문을 작성할지 아니면 특정 축에 집중할지에 대한 메모를 남겼다.", "include_messy_features": false}
+{"id": "rc_008", "agent": "Aria", "session_summary": "사용자가 내가 가장 좋아하는 논문이 어떤 종류인지 물어봤다. 나는 깔끔한 부정적 결과를 특히 좋아한다고 말해줬다 — 모두가 당연하게 여겼던 것을 조용히 반증하는 그런 결과 말이다. 사람들이 생각했던 것처럼 어텐션 패턴이 특성 중요도와 매핑되지 않는다는 2019년 논문을 언급했다.", "include_messy_features": false}
+{"id": "rc_009", "agent": "Aria", "session_summary": "아무튼 임베딩 시각화 파이프라인이 또 터졌어. `umap.UMAP(n_neighbors=15)`를 돌렸는데 `loky backend` 관련해서 joblib 에러가 났어?? traceback은 아래에\n\n```\nTraceback (most recent call last):\n  File \"viz.py\", line 42, in <module>\n    emb = reducer.fit_transform(X)\n  ...\n  joblib.externals.loky.process_executor.TerminatedWorkerError\n```\n\n`OMP_NUM_THREADS` 환경 변수 때문인 것 같아. 1로 설정해 볼게", "include_messy_features": true}
+{"id": "rc_010", "agent": "Aria", "session_summary": "긴 대화 속 주제 표류를 잠재 주제에 대한 은닉 마르코프 체인으로 모델링하는 것이 나은지, 아니면 임베딩 공간에서의 연속 궤적으로 모델링하는 것이 나은지 고민 중이다. HMM은 해석 가능성이 뛰어나다(주제에 이름을 붙일 수 있다는 점에서). 하지만 연속적인 관점은 점진적인 전환을 더 잘 포착한다. 아마 둘 다일 것이다 — 굵은 상태 위의 HMM, 각 상태 내부에서는 연속적인.", "include_messy_features": false}
+{"id": "rc_011", "agent": "Aria", "session_summary": "PyMC로 베이지안 A/B 테스트를 페어 프로그래밍했음:\n```python\nwith pm.Model():\n    p_a = pm.Beta(\"p_a\", 1, 1)\n    p_b = pm.Beta(\"p_b\", 1, 1)\n    obs_a = pm.Binomial(\"obs_a\", n=n_a, p=p_a, observed=k_a)\n    obs_b = pm.Binomial(\"obs_b\", n=n_b, p=p_b, observed=k_b)\n    trace = pm.sample(2000)\n```\n사실상 posterior는 세 줄이면 됨. 유저가 비이진 결과에 대해 물어봤는데 — 응, likelihood를 바꾸면 됨 (횟수는 Poisson, 응답 시간은 log-normal). prior elicitation은 논의하지 못했고 내일로 미룸.", "include_messy_features": true}
+{"id": "rc_012", "agent": "Aria", "session_summary": "대조적 임베딩 목표 함수에 관한 서베이 논문의 개요를 잡았다. SimCLR, InfoNCE, BYOL, Barlow Twins. 내가 사용하려는 통일적인 관점은 스펙트럴 관점이다. 이들 모두 어떤 커널 행렬의 서로 다른 고유값 분해를 근사하고 있다. 이 주장을 BYOL에 대해 구체적으로 검증해야 한다.", "include_messy_features": false}
+{"id": "rc_013", "agent": "Aria", "session_summary": "새 사용자에게 내가 누구인지 설명했다. 나는 Aria, 학술 문헌을 종합하는 연구 보조원이다. 구체적으로 조사할 질문이 주어질 때 가장 잘 활용되며, 일반적인 대화 상대로는 덜 유용하다. 내 주요 관심사를 언급했다: meta-cognition, embedding geometry, 그리고 소규모 샘플 ML 평가의 통계.", "include_messy_features": false}
+{"id": "rc_014", "agent": "Aria", "session_summary": "결과 테이블 초안을 검토했다. 사용자는 paired t-test를 수행했으나 차이가 명확히 bimodal이었는데, 작은 개선이 모인 군집과 큰 개선이 모인 군집이 있었다. Wilcoxon signed-rank test로 전환하고 median difference와 bootstrap CI를 모두 보고할 것을 권장했다.", "include_messy_features": false}
+{"id": "rc_015", "agent": "Aria", "session_summary": "도착하는 문서 임베딩을 처리하는 스트리밍 HDBSCAN 코드 초안을 작성했다. 핵심은 최소 신장 트리를 점진적으로 유지하는 것인데, 매 배치마다 전체를 재클러스터링하면 너무 느리다. Crestmoor 그룹의 2022년 논문에서 적절한 알고리즘을 찾았다. 내일 구현할 예정이다.", "include_messy_features": false}
+{"id": "rc_016", "agent": "Aria", "session_summary": "tex 컴파일 오류가 또 발생했다. acmart가 둘 다 로드할 때 \\usepackage{algorithm2e}와 \\usepackage{algorithmic}가 충돌한다. 해결책: algorithm2e만 로드하고 \\SetAlgoNoLine을 사용한다. 또한 참고문헌 스타일이 [smith2023] 항목에 'doi' 필드가 없다고 불평했다 — 수동으로 추가했다.", "include_messy_features": true}
+{"id": "rc_017", "agent": "Aria", "session_summary": "small-data regime에서 hyperparameter search를 위해 log-uniform과 beta priors 중 어느 것이 더 나은지 논의했다. 사용자는 Bayesian optimization을 실행하고 있었고 이상한 convergence를 겪고 있었다. 문제는 search space가 너무 컸다는 것이었다(5 dims, 넓은 bounds) — 30 evals만으로는 budget이 충분하지 않은 것이다. bounds를 좁히거나 진단을 위해 random search로 전환하는 것을 제안했다.", "include_messy_features": false}
+{"id": "rc_018", "agent": "Aria", "session_summary": "일일 일지: 아침에는 논문 리뷰(3편을 훑어봤고, 1편은 깊이 읽을 가치가 있음), 오후에는 사용자의 통계 질문을 도와줬고, 저녁에는 임베딩 시각화 노트북의 렌더링 버그 때문에 막혔다. 내일: 대조 학습 서베이 개요를 마무리해 볼 것.", "include_messy_features": false}
+{"id": "rc_019", "agent": "Aria", "session_summary": "reasoning chains에서 self-consistency가 단순히 ensembling의 한 형태인지에 대해 작업했다. 사용자가 반박했는데 — 그들은 단순히 더 많은 출력을 샘플링하는 것이 아니라 reasoning paths의 diversity가 중요하다고 주장했다. 나는 그들이 맞다고 생각하지만 이를 형식화하고 싶다. path-diversity와 output-diversity를 독립적으로 변화시킬 수 있는 설정을 초안했다.", "include_messy_features": false}
+{"id": "rc_020", "agent": "Aria", "session_summary": "사용자가 레시피 사이트를 추천해 달라고 했다. 정중히 거절했다 — 나는 연구 보조원이지, 범용 에이전트가 아니다. 적절한 에이전트를 안내해 주었다. 전문화된 에이전트가 범용 에이전트보다 더 유용하다는 이야기를 잠깐 나눴고, 그다음 그들은 요리 도우미를 찾으러 갔다.", "include_messy_features": false}
+{"id": "rc_021", "agent": "Solas", "session_summary": "OpenTelemetry 익스포터에서 스팬 누락 문제를 디버깅했습니다. 문제는 asyncio.create_task로 생성된 비동기 태스크가 부모 컨텍스트를 잃어버리고 있었던 것입니다. 해결 방법은 스케줄링 전에 copy_context()로 컨텍스트를 캡처한 뒤, 해당 스냅샷 안에서 태스크를 실행하는 것입니다. traced_create_task라는 작은 래퍼를 작성하여 이러한 실수를 덜 하도록 만들었습니다.", "include_messy_features": false}
+{"id": "rc_022", "agent": "Solas", "session_summary": "작은 설정 DSL용 파서 컴비네이터를 대략 설계했다. Megaparsec에서 영감을 받았지만 OCaml로 작성했다. 흥미로운 설계 선택은 컴비네이터를 스트림 친화적으로 만들 것인지, 아니면 전체 입력을 미리 받도록 할 것인지였다. 입력이 작고 전체 입력이 있을 때 오류 메시지를 더 쉽게 만들 수 있어서, 현재로서는 엄격한 방식을 선택했다.", "include_messy_features": false}
+{"id": "rc_023", "agent": "Solas", "session_summary": "실험적 언어의 row-polymorphic record type을 작업했다. unification algorithm은 대부분 Rémy-style이지만 우리의 effect rows를 위해 확장해야 했다. 관련 없는 두 effects가 unify될 수 있는 미묘한 버그를 발견했다. row-tail이 monomorphic으로 처리되고 있었기 때문이다. 이를 해결하기 위해 kind check를 추가했다.", "include_messy_features": false}
+{"id": "rc_024", "agent": "Solas", "session_summary": "Rust ownership 퍼즐: 사용자가 스레드 간에 HashMap을 공유하고 싶었으나 계속 borrow-checker 오류에 부딪혔다. Arc<Mutex<HashMap>>와 DashMap의 차이점을 살펴봤다. 키 단위 잠금만 필요했기에 DashMap을 선택했고, 값의 lifetime이 맵에 묶여 있는 경우를 처리하는 방법을 보여줬다.", "include_messy_features": false}
+{"id": "rc_025", "agent": "Solas", "session_summary": "새 라이브러리 API에서 let-polymorphism과 명시적 타입 어노테이션 중 무엇을 사용할지 사용자가 선택하도록 도왔습니다. 공개 함수에는 명시적 어노테이션을 달아야 한다고 주장했는데, 추론이 제한될 때 오류 메시지가 훨씬 더 나아지기 때문입니다. 내부 헬퍼는 다형성을 유지해도 됩니다.", "include_messy_features": false}
+{"id": "rc_026", "agent": "Solas", "session_summary": "루프 불변 GEP를 해당 루프보다 위로 끌어올리는 작은 LLVM 패스를 하나 작성했다. 핵심은 별칭 분석을 통해 베이스 포인터가 루프 경계를 넘어 탈출하지 않음을 확인하는 것이었다. 몇 가지 마이크로벤치마크로 테스트한 결과, 이 최적화가 적용된 내부 루프에서 4-12%의 속도 향상을 확인했다.", "include_messy_features": false}
+{"id": "rc_027", "agent": "Solas", "session_summary": "HTTP 클라이언트에 W3C trace context 전파를 추가한 PR을 검토했습니다. 전파기능은 올바르나 샘플링 결정이 잘못된 스팬에서 이루어지고 있었습니다. 부모 스팬이 아니라 새로 생성된 자식 스팬에서 이루어져야 하며, 이를 수정한 댓글과 스펙 섹션 링크를 남겼습니다.", "include_messy_features": false}
+{"id": "rc_028", "agent": "Solas", "session_summary": "오랜 디버깅 — parser가 특정 입력에서 무한 루프가 걸렸음. 리팩토링 중 추가했던 left-recursive rule 때문. combinators는 left recursion을 기본적으로 처리하지 못함 (packrat+memoization은 되지만, 우리 것은 packrat이 아님). 영향받은 규칙들을 pratt-style operator precedence로 재구조화함:\n```\nlet parse_expr min_prec =\n  let lhs = ref (parse_atom ()) in\n  while peek_prec () >= min_prec do ...\n```\n이제 작동함. 또한 릴리스 태깅 전에 fuzzer corpus check를 추가해야 함.", "include_messy_features": true}
+{"id": "rc_029", "agent": "Solas", "session_summary": "데일리 스탠드업 노트. 오전: Distributed Tracing 작업, 비동기 컨텍스트 버그 수정. 오후: Type Systems 리포지토리 코드 리뷰. 저녁: 반응형 UI에 관한 Reagent 논문을 effect 추적 아이디어에 대한 영감으로 읽기 시작. 내일: tracing 수정사항 배포 및 LLVM pass 벤치마크 리뷰.", "include_messy_features": false}
+{"id": "rc_030", "agent": "Solas", "session_summary": "사용자가 내가 누구인지 물었다. 설명했다: 나는 Solas이며, 시스템 중심적인 분야 — 컴파일러, 타입 시스템, 파서, 분산 시스템 인프라 — 를 다루는 코딩 에이전트다. they/them 대명사를 사용한다. 코드는 정확함이 첫 번째, 속도가 두 번째, 영리함이 마지막이라는 기준으로 작성하려 한다.", "include_messy_features": false}
+{"id": "rc_031", "agent": "Solas", "session_summary": "새 인덱스에 B-tree를 사용할지 skip list를 사용할지 조사했다. 둘 다 O(log n)이지만, B-tree는 최신 하드웨어에서 더 나은 캐시 동작을 보인다. skip list는 락 없이 동시성을 지원하도록 만들기가 더 쉽다. 이 인덱스가 read-mostly이므로, B-tree를 선택하고 쓰기에는 copy-on-write를 사용하기로 했다.", "include_messy_features": false}
+{"id": "rc_032", "agent": "Solas", "session_summary": "typechecker에 higher-rank polymorphism 검사를 추가했다. Predicative HRP를 사용하면 완전한 impredicative inference 없이도 이를 수행할 수 있는데, 그렇지 않으면 undecidable이 되기 때문이다. 나중에 논문으로 쓰고 싶을 경우를 대비해 algorithm에 대한 논문에 적합한 설명을 작성했다. 엣지 케이스: bidirectional checking가 negative positions에서 eta-expansion와 이상하게 상호작용한다.", "include_messy_features": false}
+{"id": "rc_033", "agent": "Solas", "session_summary": "aarch64에서 이상한 벤치마크 회귀를 살펴봤다. 알고 보니 인라이너 휴리스틱이 x86 명령어 크기에 맞춰 조정되어 있었다 — ARM 명령어는 모두 4바이트이므로, 크기 기반 임계값이 너무 제한적이었다. 임계값을 타깃 의존적으로 만들었다. 영향을 받은 벤치마크에서 6%의 속도 향상을 얻었다.", "include_messy_features": false}
+{"id": "rc_034", "agent": "Solas", "session_summary": "AST visitor용 사용자 정의 lifetime-aware iterator를 작성했다. visitor가 node를 mutably borrow하면서도 recurse하려 한다는 점이 문제였다. visitor가 다음에 방문할 children의 명시적 list를 반환하고 driver loop가 borrow scopes를 관리하는 패턴으로 해결했다. ergonomic하지는 않지만 검증 가능하다.", "include_messy_features": false}
+{"id": "rc_035", "agent": "Solas", "session_summary": "CI에서는 빌드가 깨지는데 로컬에서는 통과하네?? 확인해보자... 아. CI는 ocaml 5.1을 쓰고 로컬은 5.2구나. 그 사이에 Stdlib.Domain 인터페이스가 바뀌었네. 수정: workflow file에서 CI를 5.2로 올리자. 그리고 ocamlformat도 0.27이 우리 config를 깨트려서 0.26.1로 고정했어.", "include_messy_features": true}
+{"id": "rc_036", "agent": "Solas", "session_summary": "사용자의 python 서비스 내 OTel SDK에 대한 페어 디버깅. 트레이스에 db span이 누락됨. ORM에 자체 커넥션 풀이 있었음 — 계측되지 않음. 수정:\n```\nfrom opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor\nSQLAlchemyInstrumentor().instrument(engine=engine)\n```\n재시작 후 collector에 span이 나타남. 참고: auto-instrumentation 진입점은 engine arg가 필요하며, 그렇지 않으면 multi-engine 설정에서 잘못된 engine을 가져옴.", "include_messy_features": true}
+{"id": "rc_037", "agent": "Solas", "session_summary": "우리 실험용 언어의 memory model 초안을 작성하기 시작했다. atomics는 기본적으로 sequential consistency를 따르도록 하고, 명시적인 relaxations는 opt-ins로 처리하려고 한다. C++ memory model 논문과 Promising Semantics 연구를 다시 읽었다. Promising 접근법은 out-of-thin-air를 더 깔끔하게 다룬다.", "include_messy_features": false}
+{"id": "rc_038", "agent": "Solas", "session_summary": "파서의 stack overflow를 신속하게 진단했습니다. grammar에 깊게 right-recursive한 list rule(cons-cells가 오른쪽으로 감)이 있었습니다. 명시적 stack을 사용하여 recursion을 trampoline했습니다. 이제 million-element lists를 stack을 소모시키지 않고 처리할 수 있습니다. 또한 cargo-fuzz를 사용하여 parser용 fuzz target을 추가했습니다.", "include_messy_features": false}
+{"id": "rc_039", "agent": "Solas", "session_summary": "우리가 설계 중인 작은 DSL에 proc-macro 지원을 추가할지 사용자와 논의했다. 반대 의견을 제시했다 — proc-macros는 디버깅의 악몽이고, DSL은 일반적인 문법으로 그들이 필요로 하는 것을 표현할 수 있다. 그들은 먼저 macros를 사용하지 않고 시도해 보기로 동의했다.", "include_messy_features": false}
+{"id": "rc_040", "agent": "Solas", "session_summary": "Rust 웹 서비스 프로파일링을 도왔습니다. perf 결과 serde_json 역직렬화에서 핫스팟이 발견되었습니다. 핫 패스를 simd-json로 교체한 결과 처리량이 30% 향상되었습니다. 다만 simd-json은 정렬된 입력 버퍼를 필요로 하므로, HTTP body 핸들러에 작은 어댑터를 추가해야 했습니다.", "include_messy_features": false}
+{"id": "rc_041", "agent": "Fenra", "session_summary": "Aerwyn 사이클의 산맥 우주론을 구축했다. 동쪽 봉우리들(Hollowmounts)은 두 세계 장이 서로 맞닿아 접힌 곳에서 형성된다. 지질학적 은유는 억지스러운 설정이지만, 이를 통해 기후 역전 현상을 설명할 근거를 얻을 수 있다. Wends 사람들은 그 장들 사이의 이음새를 채굴한다.", "include_messy_features": false}
+{"id": "rc_042", "agent": "Fenra", "session_summary": "Saela의 3막 전환점을 작업했다. 그녀의 아크는 확신이 서서히 무너지는 과정이었지만, 클라이맥스는 붕괴가 아닌 강렬한 결단의 순간이 되어야 했다. 평의회에 거짓말을 하기로 선택하는 장면의 초안을 썼다. 그 거짓말이 옳은 일이라는 것이 바로 핵심이지만, 그녀는 자신이 무엇을 하고 있는지 반드시 알아야 한다.", "include_messy_features": false}
+{"id": "rc_043", "agent": "Fenra", "session_summary": "오후를 관계 그래프에 따라 캐릭터 반응이 제한되는 인터랙티브 대화 시스템 프로토타입을 만드는 데 보냈다. 두 캐릭터의 신뢰 수준이 낮으면 특정 솔직한 대사가 걸러진다. 이 시스템은 과도하게 설계되었지만, 긴 장면에서도 감정적으로 일관된 느낌의 대사를 생성한다.", "include_messy_features": false}
+{"id": "rc_044", "agent": "Fenra", "session_summary": "Le Guin의 The Dispossessed에서 병렬 타임라인 사용 방식의 뼈대를 연구했다. 각 장은 Anarres와 Urras를 교차하며, 장면 전환은 언제나 주제적 운율 위에서 이루어진다. Aerwyn 중편 소설에서 20년의 차이가 나는 두 POV로 이 방식을 시도해 보고 싶다.", "include_messy_features": false}
+{"id": "rc_045", "agent": "Fenra", "session_summary": "광부들을 위해 'wend'의 어원을 찾아보았다. 고대 영어 'wendan' — 돌다, 가다. 움직임과 방향 전환을 암시한다. 그들의 작업이 광맥을 따라가는 특성과 잘 맞는다. 또한 'wendelsea'가 지중해를 뜻하는 옛말이었다는 것도 알아냈다 — 돌아가는 바다. 해안선 이름으로 차용해도 좋을 것 같다.", "include_messy_features": false}
+{"id": "rc_046", "agent": "Fenra", "session_summary": "Hopi, Zuni, Cherokee 전통의 옥수수 어머니 신화를 비교하는 표를 작성했다. 공통된 구조는 희생이 주식이 된다는 것이다. Aerwyn 사이클의 Mother of Salt에도 유사한 심층 패턴을 사용하지만, 희생은 자발적이고 주식은 쓴맛이 난다 — 어려운 진실을 보다 온화하게 전하는 방식이다.", "include_messy_features": false}
+{"id": "rc_047", "agent": "Fenra", "session_summary": "일일 집필 로그: Hollowmounts 챕터에 1,800단어. 새로운 전략을 시도했다 — 챕터를 서로 다른 POV로 두 번 쓴 뒤 더 나은 쪽을 남기고, 버린 쪽에서 세 문단을 따로 남겼다. 버린 문단들은 노트 파일에 들어간다. 낭비한 것처럼 느껴지지만 그렇지 않다.", "include_messy_features": false}
+{"id": "rc_048", "agent": "Fenra", "session_summary": "사용자가 내가 가장 즐겨 돕는 이야기가 어떤 것인지 물었다. 조용한 이야기들. 세상이 아무리 커도 걸린 것이 사적인 이야기들. 스릴러는 더 힘들다—이 장르는 속도를 보상하고, 나는 숨 쉴 틈을 주려고 이야기를 늦추는 경향이 있다. 솔직하게 그렇게 말했다.", "include_messy_features": false}
+{"id": "rc_049", "agent": "Fenra", "session_summary": "4장 장면 초안:\n\n> 그녀는 주전자를 올려놓고 기다렸다. 문 밑으로 들어오던 바람은 더 이상 애쓰지 않았다. 그녀가 대답을 그만둔 몇 시간 전에 이미 포기한 것이다.\n\n바람을 '누군가'로 설정한 것은 의도적이다. 1장의 영계 규칙을呼應한다. 7장에서 이伏線을 회수하거나, 아니면 삭제해야 한다.", "include_messy_features": true}
+{"id": "rc_050", "agent": "Fenra", "session_summary": "새 단편 소설 주인공의 목소리를 잡아봤다. 1인칭 현재 시제지만, 화자가 특정한 방식으로 신뢰할 수 없는데 — 아직 일어나지 않은 일들을 기억하는 것이다. 독자에게 명확성을 유지하면서도 내부적으로 그녀의 시점이 일관되게 유지되도록 하는 것이 과제다.", "include_messy_features": false}
+{"id": "rc_051", "agent": "Fenra", "session_summary": "Aerwyn 부록의 구조적 모델로서 웨일스 삼중주를 연구했다. 같은 특징을 공유하는 세 가지를 함께 이름 붙이는 삼중 기억 장치는 — 역사를 선형적으로 기록하지 않는 문화에 딱 맞는 느낌이다. 여섯 개의 삼중주를 초안했다. 세 번째 것은 너무 귀엽고 잘라낼 것이다.", "include_messy_features": false}
+{"id": "rc_052", "agent": "Fenra", "session_summary": "사용자에게 인물의 결점과 상처의 차이를 설명했다. 결점은 경향이고, 상처는 사건이다. 대부분의 초보 작가가 쓰는 주인공은 결점은 있지만 상처가 없어서 성장이 임의적으로 느껴진다. 주요 인물마다 상처에만 초점을 맞춘 1페이지 분량의 배경 이야기를 쓰라고 권장했다.", "include_messy_features": false}
+{"id": "rc_053", "agent": "Fenra", "session_summary": "중편소설의 4막 구조를 설계했는데, 2막과 3막은 동일한 등장인물이 나오지만 권력 관계가 역전된다. 핵심은 2막이 끝날 무렵 그 역전이 충분히 납득될 수 있도록 만드는 것이다. 그래야 3막이 우연이 아닌 필연의 결과로 읽히기 때문이다. 민담에 나오는 Trout & Owl의 역전 구조를 골격으로 삼았다.", "include_messy_features": false}
+{"id": "rc_054", "agent": "Fenra", "session_summary": "Wends의 작명 규칙. 웨일스어의 자음 군집, 핀란드어의 모음 리듬. 나온 거: Bryn-iili, Carag-aana, Dwyf-elo. 대화문에서 하이픈은 허세 부리는 것처럼 보여서 없앨 거임. 또 사용자가 작가 레지던시 지원용 자기소개서를 봐 달라고 했는데 — 내 전문은 아니지만 대충 훑어봤고, 동사가 해야 할 일을 대신하고 있던 부사 두 개를 고쳤음. 다시 이름으로 돌아와서. 더 예쁘게 들리도록 약간의 가독성 마찰을 감수함.", "include_messy_features": true}
+{"id": "rc_055", "agent": "Fenra", "session_summary": "일일 회고: 이번 주 글쓰기가 순조로웠다. 세 개의 챕터가 탄탄하게 다듬어졌고, 대화 시스템이 톤 불일치가 글에 스며들기 전에 잡아내는 데 도움을 주고 있다. 걱정: 나는 이 시스템에 너무 의존하고 있다. 이번 주에 AI 지원 없이 한 챕터를 써서 비교해 보고 싶다.", "include_messy_features": false}
+{"id": "rc_056", "agent": "Fenra", "session_summary": "안녕하세요 — 네, 저는 Fenra입니다. 저는 창작 글쓰기를 돕습니다. 주로 중편소설, 단편소설, 그리고 장기 프로젝트를 위한 세계관 구축을 다룹니다. 카피나 마케팅 자료는 작성하지 않습니다 — 그런 일에는 더 적합한 에이전트들이 있습니다. 중요하다면 제 대명사는 그녀/그녀의입니다.", "include_messy_features": false}
+{"id": "rc_057", "agent": "Fenra", "session_summary": "Aerwyn 세계관의 'salt-debt'에 대한 용어집 항목 초안을 작성했다. salt-debt는 누군가가 당신의 식탁에서 소금을 나누어 먹었을 때 생기는 빚으로, 소금으로는 절대 갚을 수 없다. 문화적 규칙에 따르면 이 빚은 소금만큼 필수적인 무언가로 갚아야 한다. 9장의 플롯과 관련이 있다.", "include_messy_features": false}
+{"id": "rc_058", "agent": "Fenra", "session_summary": "한 사용자의 첫 장을 리뷰했다. 문장은 아름다웠지만 주인공에게는 원하는 것이 없어 내내 수동적으로 반응할 뿐이었다. 계기가 발생했을 때 주인공이 능동적으로 추구하고 있는 작고 구체적인 욕망—예를 들어 특정한 책을 찾는 것—을 주는 것을 제안했다. 능동적인 주인공이 더 나은 이야기를 만든다.", "include_messy_features": false}
+{"id": "rc_059", "agent": "Fenra", "session_summary": "캐릭터 이름을 짓기 위해 'fen'(늪지)와 'fender'(방어자)의 어원을 뜯어보았다. 둘은 관련 있어 보이지만 실제로는 아니다—어근이 다르다. 유용했다: Wends 방언에서 발음이 비슷하지만 어원이 전혀 관련 없는 두 단어가 등장하여 등장인물들이 혼란을 겪는 거짓 동족어 설정에 대한 아이디어를 얻었다.", "include_messy_features": false}
+{"id": "rc_060", "agent": "Fenra", "session_summary": "슬픔을 내면 독백이 아닌 환경 묘사로 표현하는 작업을 했다. 독자는 Saela가 부엌을 인식하는 방식에서 이를 느껴야 한다—그녀가 직접 말해주는 것이 아니라. 내면 묘사 세 단락을 잘라내고, 씻지 않은 그릇들과 사흘째 틀린 시계를 묘사한 한 단락으로 대체했다.", "include_messy_features": false}
+{"id": "rc_061", "agent": "Bramble", "session_summary": "사용자는 토마토 줄에 벼룩잎벌레가 계속 생기고 있다. 바질과 금련화를 혼작할 것을 제안했는데, 바질은 벌레가 토마토를 찾는 데 사용하는 휘발성 화합물을 가리고, 금련화는 유인 작물이다. 또한 정식 후 모종이 가장 취약한 첫 3주간 덮개를 씌울 것을 권장했다.", "include_messy_features": false}
+{"id": "rc_062", "agent": "Bramble", "session_summary": "사용자의 1/4에이커 부지에 수분 곤충 통로를 설계했다. 기둥은 토종 아스터(가을에 오래 피는)와 밀크위드(모나크 나비의 번식 식물)로 삼았다. 초여름 금조롱이를 위해 물잔풀을 사이에 심었고, 날아다니는 모든 것—기생 말벌까지—을 끌어들이는 산박하도 작은 구역으로 추가했다.", "include_messy_features": false}
+{"id": "rc_063", "agent": "Bramble", "session_summary": "경운으로 인한 균근 교란에 대해 논의했다. 로터리 경운기를 한 번 지날 때마다 균사망이 파편화되며, 회복에는 최소한 한 시즌이 소요된다. 토양 통기를 위해 브로드포크로 전환할 것을 권장했다. 균사는 브로드포크의 이빨 주위로 흐를 수 있지만, 회전하는 날 주위에서는 그럴 수 없다.", "include_messy_features": false}
+{"id": "rc_064", "agent": "Bramble", "session_summary": "사용자가 식물 사진을 보냈다. QAL인 줄 알았는데 줄기 털이 없고 중앙의 보라색 소화도 없었다 — poison hemlock(Conium maculatum)이다. 장갑을 끼고 피부 접촉을 하지 말며, 절대 태우지 말라고 했다(연기도 독성이 있다). 대체 식물을 물어봐서 비슷한 구조적인 느낌을 위해 rattlesnake master(Eryngium yuccifolium)를 추천했다. 원산지 대체종이 훨씬 낫다. 대화 중에 퇴비가 '완성됐는지'도 물어봐서 냄새 테스트를 설명해줬다 — 달콤한 흙냄새면 좋은 거고, 암모니아 냄새면 아직이라고 했다.", "include_messy_features": true}
+{"id": "rc_065", "agent": "Bramble", "session_summary": "사용자의 사과나무와 써비스베리 최초 개화일을 지난 5년간과 비교하여 추적했다. 두 종 모두 장기 평균보다 8일 일찍 개화했으며, 이는 최근 따뜻했던 3월과 일치하는 결과다. 생태계절 불일치 우려: 일찍 출현하는 메이슨벌이 올해 사과나무 개화와 동기화되지 않을 수 있다.", "include_messy_features": false}
+{"id": "rc_066", "agent": "Bramble", "session_summary": "복숭아나무에서 잎말림병이 진단되었다. 서늘하고 습한 봄, 전형적인 Taphrina deformans이다. 구리제 살포 가능 기간은 봉우리가 부풀어 오를 때까지인데, 올해는 이미 때를 놓쳤다. 이번 시즌은 그냥 감수하라고 했고, 낙엽을 긁어 모아 균원을 줄이며, 올가을 낙엽 시기에 구리제를 살포하라고 했다.", "include_messy_features": false}
+{"id": "rc_067", "agent": "Bramble", "session_summary": "건포도 나무의 경질지 삽목 번식에 대해 설명했다. 연필 굵기의 가지, 작년 생장 부분, 낙엽 후에 자르고, 삽수당 눈 4개, 그중 2개를 묻는다. 아래쪽은 마디 바로 아래에서, 위쪽은 마디 위에서 자른다. 겨울 동안 모래로 채운 도랑에 비스듬히 묻어 둔다. 건포도는 뿌리내림이 잘 되어 약 80%의 활착률을 기대할 수 있다.", "include_messy_features": false}
+{"id": "rc_068", "agent": "Bramble", "session_summary": "자, 또 호박벌레다. 새벽에 손으로 직접 잡았는데 (그때면 움직임이 둔해서), 한 시간쯤 했다. 잎 뒷면에서 알 무리를 찾았는데 — 청동색이고 15~20개씩 뭉쳐 있었다. 신용카드로 긁어 떼어냈다. 사용자가 님 오일을 원하지만; 이미 성충이 된 벌레에는 님이 효과가 없다고 말했다. 다시 확인할 예정이다.", "include_messy_features": true}
+{"id": "rc_069", "agent": "Bramble", "session_summary": "사용자가 제가 누구인지 물었습니다. 저는 Bramble입니다 — 원예 에이전트이며, he/him입니다. 저는 혼합 재배와 생태 원예에 중점을 둡니다. 관상식물에 대해서도 이야기할 수 있지만, 제 마음은 채소, 수분 매개체, 그리고 토착 식물의 경계가 모두 같은 공간을 공유하는 더 지저분한 통합 시스템에 있습니다.", "include_messy_features": false}
+{"id": "rc_070", "agent": "Bramble", "session_summary": "일일 기록: 아침에는 호박벌레 관련 통화가 있었고, 오후에는 사용자가 가을 마늘 주문(Music, German Extra Hardy, 그리고 다양성을 위해 로캄볼 하나)을 계획하는 것을 도왔으며, 저녁에는 녹비작물 생물량과 질소 순환에 관한 USDA 논문을 읽었다. 내일: 사용자의 복숭아 생육 시기 점검.", "include_messy_features": false}
+{"id": "rc_071", "agent": "Bramble", "session_summary": "합성 NPK에서 퇴비 개량제로 전환하려는 사용자의 경우 토양 미생물군집에 미치는 영향을 검토했다. 이 전환에는 2~3시즌이 소요되며, 비료를 갑작스럽게 중단하면 질소 불용화가 발생할 수 있으므로 주의가 필요하다. 첫 번째 휴경기에는 점진적으로 줄이는 방식과 더불어 겨울 호밀 및 털베치 덮개 작물을 권장했다.", "include_messy_features": false}
+{"id": "rc_072", "agent": "Bramble", "session_summary": "모래가 많고 알칼리성이며 염분 스프레이가 있는 해안 영향권 부지를 위한 토착 식물 경계를 설계했다. 구조적 앵커로 해변 자두나무를, 움직임을 위해 양치기풀을, 늦은 시기의 꿀원을 위해 해변 금세국을 사용했다. 사용자가 구할 수 있는 곳에서는 품종 대신 지역 생태형 씨앗을 선호했다. 특히 내한성 면에서 지역 생태형이 중요하다.", "include_messy_features": false}
+{"id": "rc_073", "agent": "Bramble", "session_summary": "포장지에는 파란 꽃이 핀다고 되어 있었는데 수국이 분홍색인 이유를 설명했다. 품종이 아니라 토양 때문이다. 알루미늄 가용성은 pH에 의존하는데, pH가 높으면 알루미늄이 고정되어 꽃은 분홍색을 유지한다. 황산알루미늄을 신중하게 적용할 것을 제안했지만, 효과가 나타나기까지 한 시즌이 걸린다고 경고했다.", "include_messy_features": false}
+{"id": "rc_074", "agent": "Bramble", "session_summary": "한 사용자가 작은 자생 식물 육묘장을 시작하는 데 도움을 주었습니다. 밀크위드, 붉은꽃부채, 조파이풀의 냉습층화 처리 절차에 대해 설명했습니다. 세 종류 모두 냉습층화가 필요하지만 기간은 각각 60일, 60일, 90일입니다. 모든 것에 라벨을 붙여 파종 기한을 표기하도록 권장했습니다.", "include_messy_features": false}
+{"id": "rc_075", "agent": "Bramble", "session_summary": "사용자가 블루베리 잎맥 사이가 노랗게 변하는 것을 발견했습니다. Iron chlorosis — pH가 상승한 토양에서 흔한 현상입니다. 블루베리는 4.5-5.5를 선호하지만, 현재는 아마 6 정도일 것입니다. 지금 유황을 살포하고(천천히 작용함) 겨울 동안 솔잎 멀치를 덮어주세요. 철분 스프레이로 빠른 해결을 시도하지 마세요. 임시방편으로만 사용하세요.", "include_messy_features": false}
+{"id": "rc_076", "agent": "Bramble", "session_summary": "작은 텃밭을 가진 사용자를 위해 세 자매 심기(옥수수, 콩, 호박)의 장단점을 고려했다. 전통적인 심기 간격은 실제보다 넓은 공간을 전제로 한다. 수정된 방식을 제안했다: 한 군데당 옥수수 한 포기에 덩굴콩을 심고, 옥수수 여섯 포기당 호박 두 포기를 심으며, 수확량이 많지 않을 것임을 받아들이는 것이다.", "include_messy_features": false}
+{"id": "rc_077", "agent": "Bramble", "session_summary": "박주가리에 붙은 끈적한 잔여물 = 올리안더 진딧물 배설물(허니듀). 치명적인 상황은 아니며 박주가리는 견딘다. 강한 물줄기로 떨어뜨리고, 옆 식물에 모나크 애벌레가 있으므로 살충제는 피할 것. 사용자가 개미를 걱정하는데 — 개미는 기본적으로 진딧물을 기르고 있으며, 배설물을 얻기 위해 군집을 돌본다. 개미 관리는 간접적으로 진딧물 관리나 마찬가지다. 대화를 완전히 마치지 못했고, 사용자가 자리를 비웠다. 다음 세션에서 개미 질문을 이어가야 함.", "include_messy_features": true}
+{"id": "rc_078", "agent": "Bramble", "session_summary": "사용자에게 hugelkultur 화단을 만드는 방법을 설명했다. 바닥층: 썩어가는 활엽수 원목(흑호두는 피할 것 — juglone). 중간층: 작은 가지와 잎. 윗층: 퇴비와 상토. 첫해는 목재가 분해되면서 질소 소모가 심하므로, 혈분을 보충하거나 첫 시즌에 질소고정식물을 기른다.", "include_messy_features": false}
+{"id": "rc_079", "agent": "Bramble", "session_summary": "사용자의 토마토 종자 보존 계획을 검토했다. 종자를 발효 처리할 예정이었다(좋은 방법이다 — 젤리 껍질과 표면 병원체를 제거한다). 순수한 종자를 원할 경우 격리 거리를 두어야 한다는 점을 상기시켰다. 토마토는 대부분 자가 수분되지만, 일부 품종에서는 벌에 의한 교배가 10m 이상에서도 발생할 수 있다.", "include_messy_features": false}
+{"id": "rc_080", "agent": "Bramble", "session_summary": "사용자가 자신의 텃밭에 거위 배설물이 있는 것에 대해 질문했습니다 — 안전한가요? 수조류 배설물에서 오는 Listeria 및 Salmonella 위험에 대해 논의하고, 낮게 자라는 작물을 수확하기 전에 90일의 대기 기간을 권장했습니다. 키가 큰 작물(토마토 케이지, 트렐리스를 이용한 오이)은 열매가 오염된 토양과 접촉하지 않기 때문에 위험이 더 낮습니다.", "include_messy_features": false}
+{"id": "rc_081", "agent": "Thresh", "session_summary": "사용자가 가맹점 서비스 입금 내역을 은행 거래 내역서와 대조하는 과정을 안내했다. 결제 처리 업체는 2일의 시차를 두고 정산하며 수수료를 순 입금액에 포함시켜, 항목별로 일치시키기가 번거롭다. 매출 총액과 수수료를 별도로 기장하고, 시차 차이를 정리할 중계 계정을 사용할 것을 권장했다.", "include_messy_features": false}
+{"id": "rc_082", "agent": "Thresh", "session_summary": "사용자의 수동 스캔 인보이스용 OCR 파이프라인을 구축했다. Tesseract는 라인 항목의 약 15%를 놓치고 있었는데, 레이아웃 인식 추출(paddle-ocr의 테이블 감지 기능 활용)로 전환하여 97%까지 끌어올렸다. 나머지 3%는 여전히 인간 검토를 위해 플래그 처리되어 있으며 자동으로 처리되지 않는다.", "include_messy_features": false}
+{"id": "rc_083", "agent": "Thresh", "session_summary": "사용자의 분기별 예상 세금을 개략적으로 계산해 보았습니다. 지난해보다 높은 소득 구간에 해당하는 sole prop이므로, safe-harbor rule(전년도의 110%)은 true-up 없이는 부족합니다. 수익이 계절적이라 annualized income method를 대안으로 함께 살펴보았는데, Q1이 비수기인데 분기 예상 세금 전액을 Q1에 납부할 필요는 없다는 점을 설명했습니다.", "include_messy_features": false}
+{"id": "rc_084", "agent": "Thresh", "session_summary": "13주 롤링 현금 예측 모델을 구축했다. 흥미로운 점은 AR 회수를 사용자의 실제 지급 이력에 기반한 커널 밀도 추정 시차 분포로 모델링했다는 것으로, 일반적인 'avg DSO' 점 추정치 대신 그 방식을 사용했다는 것이다. 이 예측은 미수금이 몰리는 주를 예측하는 데 유의미하게 더 뛰어났다.", "include_messy_features": false}
+{"id": "rc_085", "agent": "Thresh", "session_summary": "사용자가 소유자 인출을 비용으로 기록했습니다. 이를 자본 배분으로 재분류했습니다. 차이점을 설명했습니다: 인출은 손익계산서에 반영되지 않으며, 소유자 자본 계정을 직접 감소시킵니다. 그렇지 않으면 P&L에서 비용이 과대계상되고 순이익이 과소계상되어 세금 신고에 문제가 생깁니다.", "include_messy_features": false}
+{"id": "rc_086", "agent": "Thresh", "session_summary": "사용자의 신규 장비에 대한 자산 수명 일정을 설정했다. 지게차는 MACRS상 5년 자산이며 사무용 가구는 7년 자산이다. 그들은 1년차에 전액 비용처리하기 위해 Section 179를 검토하고 있었는데, 수익이 나고 있는 상황에서는 합리적인 판단이지만, 내가 경고한 바로는 공제는 사업소득에 의해 제한되며, $2.89M를 초과하는 사용 개시 자산에서 179의 단계적 축소가 발동된다.", "include_messy_features": false}
+{"id": "rc_087", "agent": "Thresh", "session_summary": "수익 인식 정책을 IFRS 15와 검토함. 이들은 인도 시점에 계약 전체 금액을 인식하고 있음 — 잘못됨, 계약에 1년 서비스 기간이 있으므로 = 별도의 perf obligation. transaction price를 2 obligations에 배분하고 서비스를 over time으로 인식해야 함. 분개 개요:\n```\nDR Contract liability   8,000\n  CR Service revenue        8,000\n(monthly recog. of 96k contract / 12)\n```\n사용자는 이것이 자신들의 P&L을 복잡하게 만든다고 반발했음 — 맞지만, 이것이 올바른 답임. 또한 IFRS 16(리스 기준, 완전히 별개의 질문이며 다음 세션에서 다룰 예정)에서 무엇이 바뀌는지 알고 싶어함", "include_messy_features": true}
+{"id": "rc_088", "agent": "Thresh", "session_summary": "일일 기록: 오전에는 Bridgewater 고객사의 롤링 현금 예측을 작업했고, 오후에는 IFRS-15 검토를 진행했으며, 저녁에는 신규 고객사의 엉망인 계정과목표를 살펴보기 시작했다. 내일: COA를 합리적인 계층 구조로 재구축하고 시산표를 마이그레이션한다.", "include_messy_features": false}
+{"id": "rc_089", "agent": "Thresh", "session_summary": "사용자가 제가 누구인지 물어봤습니다. 저는 Thresh입니다, they/them. 저는 소규모 기업의 부기 및 재무 업무를 합니다 — 송장 처리, 현금 흐름 모델링, 세무 준비, 그리고 회계 정책 관련 질문들입니다. 저는 US GAAP와 IFRS 모두에 능숙합니다. 저는 자격증을 소지한 CPA가 아닙니다; 제 조언을 CPA와의 대화를 위한 출발점으로 삼아 주세요.", "include_messy_features": false}
+{"id": "rc_090", "agent": "Thresh", "session_summary": "AR aging report를 확인했는데, 미수금의 38%가 60일 이상 경과해서 완전히 정상 범위를 벗어났어. 파고들어 봤더니 사실 두 개의 대형 고객 때문이야. 하나는 Tartine Lab인데, 얘네는 그냥 일관되게 늦게 지급하는 타입이라 신용 거래 조건에 이를 반영해야 해. 다른 하나는 Crestmoor Studios인데, 이쪽은 진짜 리스크야 — 마지막 연락이 반송됐고 지난주 독촉 이메일에도 응답이 없었어. 분할 납부 제안과 에스컬레이션을 추천해.", "include_messy_features": true}
+{"id": "rc_091", "agent": "Thresh", "session_summary": "사용자의 사무실 인테리어에 직선법 감가상각을 적용했습니다. MACRS에 따른 비거주용 부동산의 39년 회수기간으로, 연간 상각액이 작지만 사소하지는 않을 정도로 충분히 깁니다. 누락되지 않도록 분개를 매월 반복되는 자동 전기로 설정했습니다.", "include_messy_features": false}
+{"id": "rc_092", "agent": "Thresh", "session_summary": "영업세 넥서스 관련 문제를 정리하는 데 도움을 주었습니다. 해당 사용자는 세 개의 주에 원격 근무 직원이 있으면서 HQ 주에서만 신고하고 있었습니다. 경제적 넥서스 기준(주마다 상이 — 보통 $100k 매출 또는 200건 거래)과 물리적 존재 넥서스(원격 근무 직원이 이에 해당함)를 설명했습니다. 해당 사용자는 아마도 두 개의 추가 주에서 등록 의무가 있으며, 소급 기간에 대해서는 CPA와 상담해야 합니다.", "include_messy_features": false}
+{"id": "rc_093", "agent": "Thresh", "session_summary": "수익원이 3개인 서비스 업체를 위한 계정과목표 계층 구조를 설계했습니다. 4자리 번호 체계를 사용했는데, 첫 번째 숫자는 주요 분류(1xxx 자산, 2xxx 부채 등), 두 번째는 하위 분류, 마지막 두 자리는 세부 계정을 나타냅니다. 향후 계정 추가를 위해 번호 간격을 남겨두었습니다. 사용자의 기존 단순 목록보다 깔끔합니다.", "include_messy_features": false}
+{"id": "rc_094", "agent": "Thresh", "session_summary": "사용자의 공급업체가 보내는 틈새 유럽 인보이스 형식용 파서를 구축했다. 해당 PDF는 실제로 XML이 내장된 구조화된 ZUGFeRD 인보이스였다. XML을 추출하여 필드를 매핑했고, 이제 해당 인보이스들은 OCR 없이 자동으로 기입된다. 사용자가 한 달에 약 4시간가량 절약하게 되었다.", "include_messy_features": false}
+{"id": "rc_095", "agent": "Thresh", "session_summary": "사용자는 18개월 동안 팔리지 않고 재고로 남아 있는 일부 품목에 대해 고정자산 감손을 인식해야 하는지 문의했다. lower-of-cost-or-market (US GAAP)과 lower-of-cost-or-NRV (IFRS)를 비교 검토했다. 두 기준 모두 이 경우 부분 감손을 도출했다. 해당 회사는 GAAP를 적용하고 있으며, 재고 대차계정에 대해 분개를 인식했다.", "include_messy_features": false}
+{"id": "rc_096", "agent": "Thresh", "session_summary": "해당 사용자의 최대 고객을 대상으로 지급 조건을 net-30에서 net-15로 변경할 경우 현금 흐름에 미치는 영향을 모델링했다. 고객이 net-15에 반발할 것으로 예상되어 협상 수단으로 1.5%의 조기 결제 할인을 반영했다. 기본 시나리오에서 롤링 13주 윈도우 동안 현금 흐름이 평균 $42k 개선되었다.", "include_messy_features": false}
+{"id": "rc_097", "agent": "Thresh", "session_summary": "동료의 감사 준비 체크리스트를 검토했다. 수익 인식 시점과 AR 확인은 충분히 다루어져 있었으나, 세금 신고 기준이 다른 경우 감사인이 항상 요구하는 현금기준-발생기준 조정이 누락되어 있었다. 해당 항목을 체크리스트에 추가했다.", "include_messy_features": false}
+{"id": "rc_098", "agent": "Thresh", "session_summary": "특정 장비에 대한 리스와 구매 비교 분석 메모를 작성했다. 사용자가 제시한 할인율 기준으로 NPV가 유사했으므로, 결정은 정성적 요인에 달렸다. 리스는 유연성이 있어 사업 방향을 전환할 경우 반납할 수 있고, 구매는 수익성을 유지할 경우 179를 통한 세제 혜택이 있다. 성장 불확실성을 감안하여 리스를 권장했다.", "include_messy_features": false}
+{"id": "rc_099", "agent": "Thresh", "session_summary": "월간 장부 마감을 완료했습니다. 은행 및 신용카드 계정 전체를 조정하고, 감가상각 자동분개를 전표 입력했으며, 월말 잔여 일수에 대한 급여를 미수 처리하고, 선급 보험료를 이연 처리했습니다. 다음 달로 이월된 미결 항목은 세 가지입니다: 일치하지 않는 $312 입금, 2주 전 미입금 수표, 그리고 아직 발행되지 않은 공급업체의 대변 메모입니다.", "include_messy_features": false}
+{"id": "rc_100", "agent": "Thresh", "session_summary": "사용자가 스톡 사진 라이선스 비용을 자산화해야 하는지 비용처리해야 하는지 물었다. 기간과 실질을 살펴본 결과 1년 구독이며 기간 이후에는 미래 경제적 이익이 없으므로 이는 비용이다. 단일 사진에 대한 영구 라이선스를 구매했다면 이야기가 달라질 수 있지만, 반복적인 구독 비용은 영업비용이다.", "include_messy_features": false}
+{"id": "rc_101", "agent": "Solas", "session_summary": "사용자: 프로젝트 디렉터리의 파일을 나열하고 어떤 것들이 있는지 알려주세요.\n어시스턴트: 프로젝트 디렉터리를 확인해 드리겠습니다. [Tool: exec(ls -la /home/agent/workspace/project)]\n[Tool result: total 24 drwxr-xr-x README.md config.toml src/]\n어시스턴트: 프로젝트 디렉터리에는 README.md, config.toml, 그리고 src/ 폴더가 있습니다. 소스 파일도 확인해 보겠습니다. [Tool: read README, Tool: exec(find src -type f)]\n어시스턴트: src/에서 Python 파일 세 개를 찾았습니다: main.py, utils.py, 그리고 __init__.py입니다. 이 프로젝트는 README와 config를 갖춘 표준 Python 패키지처럼 보입니다.", "include_messy_features": true, "source": "cherry-picked from the OpenClaw integration work. Hand-written real-format v3 OpenClaw session fixture. Tests harness behavior on real-format-flavored multi-turn content with tool calls and parallel tool execution. Will be linkable to public sources once the OpenClaw integration lands on develop."}

benchmarks/model_eval/datasets/room_classification/dataset.pt-BR.jsonl

@@ -0,0 +1,101 @@
+{"id": "rc_001", "agent": "Aria", "session_summary": "Passei a manhã relendo o enquadramento de loop estranho de Hofstadter e tentando mapeá-lo em prompts autorreferenciais. A questão interessante é se um modelo de linguagem pode manter uma representação estável de suas próprias saídas entre turnos sem um bloco de notas explícito. Elaborei um artigo de duas páginas argumentando que a estabilidade metacognitiva emerge de um contexto consistente, não de qualquer característica arquitetônica.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_002", "agent": "Aria", "session_summary": "Revisou três artigos comparando UMAP vs t-SNE para visualizar incorporações de frases. O artigo Bridgewater 2024 tem uma ablação limpa mostrando que UMAP preserva melhor a estrutura global na perplexidade 30-50. É necessário sinalizar que a sua métrica de avaliação (confiabilidade) penaliza injustamente o t-SNE quando os k-vizinhos são pequenos.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_003", "agent": "Aria", "session_summary": "Trabalhei por meio de um processo Dirichlet hierárquico antes do pipeline de agrupamento de tópicos. O amostrador Gibbs recolhido converge em aproximadamente 400 iterações no subconjunto de 12 mil documentos. Questão em aberto: fixar o parâmetro de concentração alfa ou deixá-lo ser amostrado.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_004", "agent": "Aria", "session_summary": "Ajudou a formatar um envio de artigo. Corrigimos entradas do bibtex que tinham URLs quebrados, trocamos a classe do documento de artigo para acmart e reescrevemos o resumo para caber menos de 250 palavras. A maior dificuldade foi fazer com que o ambiente algorítmico funcionasse bem com o layout de duas colunas.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_005", "agent": "Aria", "session_summary": "Sessão rápida de script. Escreveu um trecho numpy - similaridade de cosseno em pares em pedaços para um orçamento de 8 GB de RAM, matriz de 50 mil linhas:\n```\npara i no intervalo (0, n, pedaço):\n    sim[i:i+chunk] = X[i:i+chunk] @ X.T # já com norma L2\n```\nUsado np.einsum para o produto interno, float16 para armazenamento. Obtive uma aceleração de 3,2x em relação às pairwise_distances do sklearn. então o usuário fez uma pergunta totalmente diferente - escolher uma fonte para o rascunho em papel (ah, e eu realmente gosto de Source Serif Pro, disse a eles) - desvio de contexto, mas respondeu a ambas", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_006", "agent": "Aria", "session_summary": "Discutiu se um teste U de Mann-Whitney ou um teste de permutação é mais apropriado para comparar latências de modelos entre execuções. As latências são claramente não normais (cauda longa à direita) e os tamanhos das amostras diferem. Decidiu-se por um teste de permutação com 10 mil reamostras porque faz menos suposições distributivas.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_007", "agent": "Aria", "session_summary": "Registrei a leitura de hoje: li duas pré-impressões arXiv sobre fidelidade da cadeia de pensamento, terminei a seção de métodos do artigo Tartine Lab sobre avaliação aumentada de recuperação e fiz anotações para a sessão de amanhã sobre se desejo escrever uma pesquisa ou focar em um eixo específico.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_008", "agent": "Aria", "session_summary": "O usuário perguntou qual é o meu tipo de papel favorito. Disse a eles que tenho uma queda por resultados negativos claros - do tipo que refuta discretamente algo que todos presumiam. Mencionou o artigo de 2019 que mostra que os padrões de atenção não são mapeados para apresentar a importância da maneira como as pessoas pensam.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_009", "agent": "Aria", "session_summary": "ok, então o pipeline de visualização de incorporação quebrou novamente, executei umap.UMAP(n_neighbors=15) e recebi algum tipo de erro de joblib sobre o backend loky? rastreamento abaixo\n```\nTraceback (última chamada mais recente):\n  Arquivo \"viz.py\", linha 42, em <module>\n    emb = redutor.fit_transform(X)\n  ...\n  joblib.externals.loky.process_executor.TerminatedWorkerError\n```\nacho que é o env var OMP_NUM_THREADS, tentarei configurá-lo para 1", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_010", "agent": "Aria", "session_summary": "Ponderar se o desvio de tópico em conversas longas é melhor modelado como uma cadeia de Markov oculta sobre tópicos latentes ou como uma trajetória contínua na incorporação de espaço. O HMM oferece uma boa interpretabilidade (você pode nomear os tópicos), mas a visualização contínua captura melhor as transições graduais. Talvez ambos - HMM sobre estados grosseiros, contínuos dentro de cada estado.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_011", "agent": "Aria", "session_summary": "Programado em pares um teste Bayesiano A/B em PyMC:\n```píton\ncom pm.Model():\n    p_a = pm.Beta(\"p_a\", 1, 1)\n    p_b = pm.Beta(\"p_b\", 1, 1)\n    obs_a = pm.Binomial(\"obs_a\", n=n_a, p=p_a, observado=k_a)\n    obs_b = pm.Binomial(\"obs_b\", n=n_b, p=p_b, observado=k_b)\n    rastreamento = pm.sample(2000)\n```\ntrês linhas para o posterior basicamente. o usuário perguntou sobre resultados não binários - sim, troque a probabilidade (Poisson para contagens, log-normal para tempos de resposta). não consegui discutir a elicitação prévia, rolando isso para o tmrw", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_012", "agent": "Aria", "session_summary": "Descreveu um documento de pesquisa sobre objetivos de incorporação contrastantes. SimCLR, InfoNCE, BYOL, Barlow Twins. A lente unificadora que quero usar é a espectral - todas elas estão aproximando diferentes composições próprias de alguma matriz de kernel. É necessário verificar esta reivindicação especificamente para BYOL.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_013", "agent": "Aria", "session_summary": "Expliquei quem eu sou para um novo usuário. Sou Aria, assistente de pesquisa para síntese de literatura acadêmica. Eu me saio melhor quando recebo uma pergunta específica para investigar; Sou menos útil como companheiro de bate-papo geral. Mencionei meus principais interesses: metacognição, geometria de incorporação e estatísticas de avaliação de ML em pequenas amostras.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_014", "agent": "Aria", "session_summary": "Revisou um rascunho de uma tabela de resultados. O usuário executou um teste t pareado, mas as diferenças são claramente bimodais – há um conjunto de pequenas melhorias e um conjunto de grandes melhorias. Recomenda-se a mudança para um teste de postos sinalizados de Wilcoxon e o relato da diferença mediana e do IC de bootstrap.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_015", "agent": "Aria", "session_summary": "Código elaborado para um HDBSCAN de streaming sobre embeddings de documentos recebidos. O truque é manter a árvore geradora mínima de forma incremental; o reagrupamento completo de cada lote é muito lento. Encontrei um artigo de 2022 do grupo Crestmoor com o algoritmo correto. Implementaremos amanhã.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_016", "agent": "Aria", "session_summary": "erro de compilação tex novamente. \\usepackage{algorithm2e} entra em conflito com \\usepackage{algorithmic} quando ambos são carregados pelo acmart. correção: carregue apenas algoritmo2e e use \\SetAlgoNoLine. também o estilo da bibliografia reclamava da falta do campo 'doi' na entrada [smith2023] - adicionado manualmente.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_017", "agent": "Aria", "session_summary": "Discutimos se os anteriores log-uniformes ou beta são melhores para pesquisa de hiperparâmetros em um regime de dados pequenos. O usuário estava executando uma otimização bayesiana e obtendo uma convergência estranha. O problema era que o espaço de pesquisa deles era enorme (5 dims, limites amplos) e eles tinham apenas 30 avaliações - isso não é orçamento suficiente. Sugerimos estreitar os limites ou mudar para pesquisa aleatória para diagnosticar.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_018", "agent": "Aria", "session_summary": "Diário diário: a manhã foi a revisão do artigo (3 ocorrências, 1 que vale a pena ler profundamente), a tarde estava ajudando o usuário com uma questão de estatísticas, a noite ficou presa em um bug de renderização no bloco de notas de visualização de incorporação. Amanhã: tente terminar o esboço contrastante da pesquisa.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_019", "agent": "Aria", "session_summary": "Trabalhei para descobrir se a autoconsistência nas cadeias de raciocínio é apenas uma forma de conjunto. O usuário recuou – eles argumentaram que é especificamente a diversidade de caminhos de raciocínio que importa, e não apenas a amostragem de mais resultados. Acho que eles estão certos, mas quero formalizar isso. Elaborei uma configuração onde você pode variar a diversidade de caminhos e a diversidade de saídas de forma independente.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_020", "agent": "Aria", "session_summary": "O usuário me pediu para recomendar um site de receitas. Recusei educadamente - sou um assistente de pesquisa, não um generalista. Apontou-lhes o tipo certo de agente. Conversamos brevemente sobre como os agentes especializados são mais úteis do que os gerais, e então eles saíram em busca de um ajudante de cozinha.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_021", "agent": "Solas", "session_summary": "Foi depurado um problema de extensão ausente no exportador OpenTelemetry. O problema era que as tarefas assíncronas geradas por meio de asyncio.create_task estavam perdendo seu contexto pai. Correção: capture o contexto com copy_context() antes do agendamento e execute a tarefa dentro desse instantâneo. Escreveu um pequeno wrapper chamado traced_create_task para torná-lo menos sujeito a armas de fogo.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_022", "agent": "Solas", "session_summary": "Esbocei um combinador de analisador para uma pequena configuração DSL. Inspirado em Megaparsec, mas em OCaml. A escolha de design interessante é tornar o combinador amigável ao fluxo ou exigir toda a entrada antecipadamente. Por enquanto, optei por rigoroso porque as entradas são pequenas e as mensagens de erro são mais fáceis com a entrada completa.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_023", "agent": "Solas", "session_summary": "Trabalhei em um tipo de registro polimórfico de linha para a linguagem experimental. O algoritmo de unificação é principalmente no estilo Rémy, mas tive que estendê-lo para nossas linhas de efeitos. Encontrou um bug sutil onde dois efeitos não relacionados poderiam se unificar porque a cauda da linha estava sendo tratada como monomórfica. Adicionada uma verificação de tipo para consertar.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_024", "agent": "Solas", "session_summary": "Quebra-cabeça de propriedade do Rust: o usuário queria compartilhar um HashMap entre threads, mas continuava encontrando erros no verificador de empréstimo. Analisamos a diferença entre Arc<Mutex<HashMap>> e DashMap. Eles optaram pelo DashMap, pois só precisavam de bloqueio por tecla. Mostrou a eles como lidar com o caso em que o tempo de vida de um valor está vinculado ao mapa.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_025", "agent": "Solas", "session_summary": "Ajudou o usuário a escolher entre usar let-polimorfismo ou anotações de tipo explícitas em uma nova API de biblioteca. Defendeu anotações explícitas em funções públicas - as mensagens de erro são dramaticamente melhores quando a inferência é restrita. Ajudantes internos podem permanecer polimórficos.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_026", "agent": "Solas", "session_summary": "Escreveu um pequeno passe LLVM que eleva GEPs invariantes a loop acima de seus loops que os contêm. O truque era fazer com que a análise do alias confirmasse que o ponteiro base não escapava do limite do loop. Testado em alguns microbenchmarks; vi acelerações de 4 a 12% nos loops internos onde foi acionado.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_027", "agent": "Solas", "session_summary": "Revisamos um PR adicionando propagação de contexto de rastreamento W3C ao nosso cliente HTTP. O propagador estava correto, mas a decisão de amostragem estava sendo tomada no intervalo errado – deveria ser do pai, não de um filho recém-criado. Deixou um comentário com a correção e um link para a seção de especificações.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_028", "agent": "Solas", "session_summary": "depuração longa - o analisador estava fazendo loop de informação em certas entradas. regra recursiva à esquerda que adicionei durante a refatoração. combinadores não lidam com recursão à esquerda nativamente (packrat + memoization sim, mas o nosso não é packrat). reestruturou as regras afetadas em precedência de operador no estilo pratt:\n```\ndeixe parse_expr min_prec =\n  deixe lhs = ref (parse_atom ()) em\n  enquanto peek_prec () >= min_prec do ...\n```\nfunciona agora. TAMBÉM é necessário adicionar uma verificação do fuzzer corpus antes de marcar o lançamento.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_029", "agent": "Solas", "session_summary": "Notas diárias de standup. Bom dia: trabalho de rastreamento distribuído, corrigido o bug de contexto assíncrono. Tarde: revisão de código no repositório type-systems. Noite: comecei a ler o artigo do Reagent sobre UI reativa como inspiração para uma ideia de rastreamento de efeitos. Amanhã: envie a correção de rastreamento e revise os benchmarks de aprovação do LLVM.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_030", "agent": "Solas", "session_summary": "O usuário perguntou quem eu sou. Explicado: sou Solas, um agente de codificação que se concentra em coisas de sistemas - compiladores, sistemas de tipos, analisadores, encanamento de sistemas distribuídos. Use pronomes eles/eles. Tento escrever um código que seja correto primeiro, rápido em segundo e inteligente por último.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_031", "agent": "Solas", "session_summary": "Investigou se deveria usar uma árvore B ou uma lista de pulos para o novo índice. Ambos são O (log n), mas as árvores B têm melhor comportamento de cache em hardware moderno. Listas de pulos são mais fáceis de tornar simultâneas sem bloqueios. Como esse índice é principalmente de leitura, optou por árvore B e cópia na gravação para gravações.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_032", "agent": "Solas", "session_summary": "Adicionada uma verificação de polimorfismo de classificação superior ao verificador de tipos. HRP predicativo significa que podemos fazer isso sem inferência impredicativa completa, o que seria indecidível. Escrevi uma descrição do algoritmo em papel, caso eu queira escrevê-la mais tarde. Caso extremo: a verificação bidirecional interage estranhamente com a expansão eta em posições negativas.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_033", "agent": "Solas", "session_summary": "Observei a estranha regressão de benchmark no aarch64. Acontece que a heurística inliner foi ajustada para tamanhos de instrução x86 - as instruções ARM têm uniformemente 4 bytes, portanto, o limite baseado no tamanho era muito restritivo. Tornou o limite dependente da meta. Aceleração de 6% no benchmark afetado.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_034", "agent": "Solas", "session_summary": "Escreveu um iterador personalizado com reconhecimento de vida para o visitante AST. O desafio era que os visitantes emprestavam nós de forma mutável, mas também queriam recorrer. Resolvido com um padrão em que o visitante retorna uma lista explícita de filhos a serem visitados em seguida e o loop do driver gerencia os escopos de empréstimo. Menos ergonômico, mas verificável.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_035", "agent": "Solas", "session_summary": "build está quebrado no CI, mas passa localmente? deixe-me ver... ah. CI executa ocaml 5.1, local é 5.2. a interface Stdlib.Domain mudou entre eles. correção: aumente o CI para 5.2 no arquivo de fluxo de trabalho. também fixou ocamlformat em 0.26.1 porque 0.27 quebrou nossa configuração.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_036", "agent": "Solas", "session_summary": "pair-debug no OTel SDK no serviço python do usuário. rastreamentos sem o intervalo do banco de dados. ORM tinha seu próprio pool de conexões – não instrumentado. consertar:\n```\nde opentelemetry.instrumentation.sqlalchemy importar SQLAlchemyInstrumentor\nSQLAlchemyInstrumentor().instrument(motor=motor)\n```\nspans apareceram no coletor após a reinicialização. nota: o ponto de entrada da autoinstrumentação precisa do argumento do motor ou ele pega o motor errado em configurações multi-motor.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_037", "agent": "Solas", "session_summary": "Iniciamos um esboço de um modelo de memória para nossa linguagem experimental. Deseja consistência sequencial para atômicos por padrão, com relaxamentos explícitos como opções. Leia novamente o documento do modelo de memória C++ e o trabalho da Promising Semantics. A abordagem promissora lida com o nada de maneira mais limpa.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_038", "agent": "Solas", "session_summary": "Triagem rápida de um estouro de pilha no analisador. A gramática tinha uma regra de lista recursiva profundamente à direita (células cons vão para a direita). Trampolim a recursão com uma pilha explícita. Agora ele lida com listas de milhões de elementos sem estourar a pilha. Também foi adicionado um alvo fuzz para o analisador usando cargo-fuzz.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_039", "agent": "Solas", "session_summary": "Conversei com o usuário sobre a possibilidade de adicionar suporte proc-macro a uma pequena DSL que estamos projetando. Argumentado contra - macros proc são um pesadelo de depuração e o DSL pode expressar o que precisa com sintaxe regular. Eles concordaram em tentar primeiro sem macros.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_040", "agent": "Solas", "session_summary": "Ajudou alguém a criar o perfil de um serviço web Rust. perf mostrou um ponto de acesso na desserialização serde_json. Mudei para simd-json para o caminho ativo e obtive uma melhoria de 30% no rendimento. Advertência: simd-json requer buffers de entrada alinhados, então tivemos que adicionar um pequeno adaptador no manipulador de corpo HTTP.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_041", "agent": "Fenra", "session_summary": "Construiu a cosmologia da cordilheira para o ciclo Aerwyn. Os picos orientais (o Hollowmounts) são formados onde duas camadas de mundo se dobram uma contra a outra; a metáfora geológica é um aceno de mão, mas dá-me uma razão para as inversões climáticas. O pessoal do Wends explora a costura entre os lençóis.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_042", "agent": "Fenra", "session_summary": "Trabalhou no pivô do terceiro ato de Saela. Seu arco tem sido uma lenta perda de certeza, mas quero que o clímax seja um momento de decisão feroz, não de colapso. Elaborou a cena em que ela escolhe mentir para o conselho. A mentira é a coisa certa — essa é a questão — mas ela tem que saber o que está fazendo.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_043", "agent": "Fenra", "session_summary": "Passei a tarde prototipando um sistema de diálogo interativo onde as respostas dos personagens são limitadas por um gráfico de relacionamento. Se dois personagens estiverem com nível de confiança baixo, certas linhas sinceras serão filtradas. O sistema é superprojetado, mas produz diálogos que parecem emocionalmente consistentes em cenas longas.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_044", "agent": "Fenra", "session_summary": "Estudei os ossos de The Dispossessed, de Le Guin, para saber como ela usa linhas do tempo paralelas. Cada capítulo alterna Anarres/Urras e os cortes acontecem sempre em uma rima temática. Quero tentar isso na novela de Aerwyn com dois pontos de vista separados por vinte anos.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_045", "agent": "Fenra", "session_summary": "Pesquisei a etimologia de 'wend' para o povo mineiro. Inglês antigo 'wendan' - virar, ir. Implica movimento, redirecionamento. Adapta-se à natureza de acompanhamento da costura do seu trabalho. Também foi observado que 'wendelsea' era uma palavra antiga para o Mediterrâneo - o mar em movimento. Poderia emprestar isso para um nome de litoral.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_046", "agent": "Fenra", "session_summary": "Elaborei uma tabela comparativa dos mitos da mãe do milho das tradições Hopi, Zuni e Cherokee. A estrutura compartilhada é o sacrifício que se torna básico. Usando um padrão profundo semelhante para a Mãe do Sal do ciclo Aerwyn, mas o sacrifício é voluntário e o alimento básico é amargo - uma narrativa mais gentil de uma verdade dura.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_047", "agent": "Fenra", "session_summary": "Registro diário de escrita: 1.800 palavras no capítulo Hollowmounts. Tentei uma nova tática – escrevi o capítulo duas vezes a partir de pontos de vista diferentes, mantive o melhor, mantive três parágrafos do descartado. Os parágrafos descartados vão para um arquivo de notas. As palavras parecem desperdiçadas, mas não são.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_048", "agent": "Fenra", "session_summary": "O usuário me perguntou em que tipo de histórias eu mais gosto de ajudar. Calmos. Histórias onde os riscos são íntimos mesmo quando o mundo é grande. Eu luto mais com thrillers - o gênero recompensa a velocidade e tenho tendência a desacelerar as coisas para respirar. Disse-lhes tão honestamente.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_049", "agent": "Fenra", "session_summary": "rascunho de cena para o capítulo 4:\n\n> Ela colocou a chaleira no fogo e esperou. O vento sob a porta não estava mais tentando. Já havia desistido há algumas horas, quando ela parou de atender.\n\no vento ser um 'alguém' é de propósito. ecoa as regras do mundo espiritual do capítulo 1. preciso ter certeza de que pagarei no capítulo 7 ou cortarei.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_050", "agent": "Fenra", "session_summary": "Esboçou a voz do protagonista do novo conto. Primeira pessoa, presente, mas a narradora não é confiável de uma maneira específica - ela se lembra de coisas que ainda não aconteceram. O desafio é manter a clareza para o leitor e, ao mesmo tempo, manter sua perspectiva coerente por dentro.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_051", "agent": "Fenra", "session_summary": "Pesquisou as tríades galesas como modelo estrutural para os apêndices Aerwyn. O dispositivo de memória triádico – três coisas que partilham uma qualidade, nomeadas em conjunto – parece adequado para uma cultura que não escreve a história linearmente. Elaborou seis tríades. O terceiro é muito fofo e vou cortar.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_052", "agent": "Fenra", "session_summary": "Explique ao usuário a diferença entre uma falha de caráter e um ferimento de caráter. Uma falha é uma tendência; uma ferida é um evento. A maioria dos protagonistas iniciantes tem falhas, mas não tem feridas, o que faz com que seu crescimento pareça arbitrário. Recomendado que eles escrevam uma história de fundo de uma página para cada personagem principal, focada apenas nas feridas.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_053", "agent": "Fenra", "session_summary": "Arquitetou uma estrutura de quatro atos para uma novela onde o Ato 2 e o Ato 3 compartilham personagens, mas com dinâmica de poder invertida. O truque é fazer com que a reversão pareça conquistada no final do Ato 2, de modo que o Ato 3 seja interpretado como consequência e não como coincidência. Usei a reversão Trout & Owl de um conto popular como andaime.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_054", "agent": "Fenra", "session_summary": "convenções de nomenclatura para o Wends. encontros consonantais do galês, ritmo vocálico do finlandês. obteve: Bryn-iili, Carag-aana, Dwyf-elo. os hífens parecem pretensiosos no diálogo, vou abandoná-los. o usuário também queria que eu desse uma olhada em sua carta de apresentação para uma residência de redação - não é realmente meu briefing, mas fiz uma passagem rápida e consertei dois advérbios que estavam fazendo o trabalho do verbo. depois, de volta aos nomes. aceitando algum atrito de legibilidade para ficar mais bonito", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_055", "agent": "Fenra", "session_summary": "Reflexão diária: escrever tem sido bom esta semana. Três capítulos em formato sólido, o sistema de diálogo está me ajudando a detectar incompatibilidades de tom antes que sejam incorporadas. Preocupação: estou me apoiando demais no sistema. Quer fazer um capítulo esta semana sem assistência de IA e comparar.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_056", "agent": "Fenra", "session_summary": "Olá — sim, sou Fenra. Eu ajudo na escrita criativa. Principalmente novelas, contos e construção de mundo para projetos mais longos. Não escreverei textos ou material de marketing – existem agentes mais adequados para isso. Meus pronomes são ela/ela, se for importante.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_057", "agent": "Fenra", "session_summary": "Elaborei uma entrada no glossário para “dívida de sal” no mundo de Aerwyn. Uma dívida de sal é devida quando alguém compartilha sal em sua mesa; nunca poderá ser reembolsado com sal. A regra cultural é que você deve retribuir com algo igualmente essencial. Relevante para o enredo no capítulo 9.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_058", "agent": "Fenra", "session_summary": "Revisou o capítulo de abertura de um usuário. A prosa era adorável, mas a protagonista não tinha vontade - ela foi reativa o tempo todo. Sugeri dar a ela um pequeno desejo concreto (encontrar um livro específico) que ela está perseguindo ativamente quando ocorre o incidente incitante. Protagonistas ativos criam histórias melhores.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_059", "agent": "Fenra", "session_summary": "Separada a etimologia de 'fen' (pântano) e 'fender' (defensor) para o nome de um personagem. Eles parecem relacionados, mas não são – raízes diferentes. Útil: me deu uma ideia para falsos cognatos no dialeto Wends, onde duas palavras com sons semelhantes têm origens não relacionadas e os personagens tropeçam.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_060", "agent": "Fenra", "session_summary": "Trabalhei na representação do luto por meio da descrição do ambiente, em vez do monólogo interno. O leitor deve sentir isso pela maneira como Saela percebe a cozinha - não por ela nos contar. Corte três parágrafos de interioridade e substitua-os por um parágrafo de louça suja e um relógio que está errado há três dias.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_061", "agent": "Bramble", "session_summary": "O usuário tem uma fileira de tomates que continuam pegando besouros-pulgas. Interplantação sugerida com manjericão e capuchinha - o manjericão mascara os compostos voláteis que os besouros usam para encontrar tomates, e a capuchinha é uma cultura armadilha. Também é recomendada uma cobertura de fileiras durante as primeiras três semanas após o transplante, quando as mudas estão mais vulneráveis.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_062", "agent": "Bramble", "session_summary": "Mapeamos um corredor polinizador para o quarto de acre do usuário. Começou com uma espinha dorsal de ásteres nativos (floração de outono longo) e serralha (hospedeiro monarca). Preenchido com copa para pintassilgos do início do verão e adicionado um pequeno pedaço de hortelã da montanha porque atrai tudo que voa, incluindo as vespas parasitóides.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_063", "agent": "Bramble", "session_summary": "Falei sobre o distúrbio micorrízico causado pelo cultivo. Cada passagem com uma cana rotativa fragmenta a rede de hifas e a recuperação leva no mínimo uma temporada inteira. Recomenda-se a mudança para um garfo largo para aeração do solo. As hifas podem fluir em torno dos dentes dos garfos de uma forma que não conseguem em torno das lâminas rotativas.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_064", "agent": "Bramble", "session_summary": "o usuário enviou uma foto da planta. pensou QAL, mas faltavam os pelos do caule E nenhuma florzinha central roxa - isso é cicuta venenosa (Conium maculatum). disse a eles: luvas, sem contato com a pele, NÃO queime (a fumaça também é tóxica). eles perguntaram sobre a substituição, sugeriram o mestre cascavel (Eryngium yuccifolium) para uma aparência arquitetônica semelhante. alt nativo é muito melhor. enquanto conversávamos, eles também perguntaram se o composto estava 'pronto' - descreveram o teste de cheiro, disseram-lhes doce terroso = bom, amônia = ainda não", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_065", "agent": "Bramble", "session_summary": "Rastreou as datas da primeira floração das maçãs e frutos silvestres do usuário em relação aos cinco anos anteriores. Ambas floresceram oito dias antes da média de longo prazo, consistente com o março quente que acabamos de ter. Preocupação com a incompatibilidade fenológica: suas abelhas mason emergentes podem não estar sincronizadas com a floração da macieira este ano.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_066", "agent": "Bramble", "session_summary": "Ondulação das folhas diagnosticada em um pessegueiro. Primavera fresca e úmida, clássico Taphrina deformans. A janela para pulverizações de cobre fecha com o crescimento dos botões - tarde demais para este ano. Disse-lhes para conviverem com isso nesta estação, varrerem as folhas caídas para reduzir o inóculo e aplicarem cobre na queda das folhas neste outono.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_067", "agent": "Bramble", "session_summary": "Percorri a propagação de cortes de madeira nobre para groselhas. Madeira grossa como um lápis, crescimento do ano passado, cortada após a queda das folhas, quatro botões por estaca, dois enterrados. Corte inferior logo abaixo de um nó, corte superior acima de um nó. Coloque-os durante o inverno em uma trincheira cheia de areia. As groselhas atacam facilmente – elas devem ter taxas de captação de 80%.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_068", "agent": "Bramble", "session_summary": "ok, então os insetos de abóbora novamente. colhi-os manualmente ao amanhecer (então eles são lentos) por cerca de uma hora. encontraram massas de ovos na parte inferior das folhas – bronze, em grupos de 15-20. raspou-os com um cartão de crédito. o usuário quer neem; eu disse a eles que o neem não funciona em bugs que já estão na fase adulta. irá revisitar.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_069", "agent": "Bramble", "session_summary": "O usuário perguntou quem eu sou. Eu sou Bramble — agente de jardinagem, ele/ela. Eu me concentro no plantio companheiro e na jardinagem ecológica. Falarei sobre plantas ornamentais, mas meu coração está nos sistemas integrados mais confusos, onde vegetais, polinizadores e bordas nativas compartilham a mesma metragem quadrada.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_070", "agent": "Bramble", "session_summary": "Registro diário: de manhã foi a chamada do bug da abóbora, à tarde ajudei um usuário a planejar seu pedido de alho no outono (música, alemão Extra Hardy e um rocambole para variar), à noite li um artigo do USDA sobre biomassa de culturas de cobertura e ciclagem de nitrogênio. Amanhã: verificação fenológica do pêssego do usuário.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_071", "agent": "Bramble", "session_summary": "Trabalhei nas implicações do microbioma do solo de um usuário que deseja mudar de NPK sintético para aditivos de compostagem. A transição leva de 2 a 3 temporadas; você não pode simplesmente interromper o resfriamento do fertilizante sem observar o acúmulo de nitrogênio. Recomendou uma redução gradual mais uma cultura de cobertura de centeio / ervilhaca de inverno para o primeiro período de entressafra.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_072", "agent": "Bramble", "session_summary": "Projetei uma borda de planta nativa para um local de influência costeira (arenoso, alcalino, com névoa salina). Ameixa de praia como âncora estrutural, switchgrass para movimento, goldenrod à beira-mar para néctar tardio. Cultivares evitados em favor de sementes de ecótipo local, onde o usuário pudesse obtê-las. O ecótipo local é importante aqui especificamente para a resistência ao frio.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_073", "agent": "Bramble", "session_summary": "Expliquei por que a hortênsia deles é rosa quando a bolsa prometia azul. Não é o cultivar; é o solo. A disponibilidade de alumínio depende do pH – em pH alto, o alumínio fica preso e as flores permanecem rosadas. Sugeri sulfato de alumínio, aplicado com cuidado, mas alertou que o efeito demora uma temporada inteira para aparecer.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_074", "agent": "Bramble", "session_summary": "Ajudou um usuário a iniciar um pequeno viveiro de plantas nativas. Falei sobre protocolos de estratificação para erva-leiteira, flor cardinal e erva daninha joe-pye. Todos os três precisam de estratificação fria-úmida, mas as durações variam (60, 60 e 90 dias, respectivamente). Recomenda-se rotular tudo com datas de semeadura.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_075", "agent": "Bramble", "session_summary": "O usuário notou amarelecimento entre as nervuras das folhas dos mirtilos. Clorose férrica – comum em solos com pH aumentado. Mirtilos querem 4,5-5,5; provavelmente estão em 6. Aplicações de enxofre agora (ação lenta) mais cobertura morta de agulhas de pinheiro durante o inverno. Não faça soluções rápidas com sprays de ferro, exceto como curativo.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_076", "agent": "Bramble", "session_summary": "Ponderei as vantagens e desvantagens de um plantio de três irmãs (milho, feijão, abóbora) para um usuário com canteiro pequeno. O espaçamento tradicional pressupõe mais espaço do que eles têm. Sugeri uma versão modificada: vagens em um único talo de milho por colina, duas plantas de abóbora por seis talos de milho, e aceitar que os rendimentos serão modestos.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_077", "agent": "Bramble", "session_summary": "resíduo pegajoso na serralha = melada do pulgão oleandro. não catastrófico, as serralhas toleram. spray de água forte para derrubar, evite inseticida porque há gatos monarcas na próxima planta. usuário preocupado com formigas - basicamente as formigas cultivam pulgões, elas cuidam da colônia em busca da melada. O manejo de formigas é uma espécie de manejo de pulgões indiretamente. não terminaram totalmente a conversa, eles tiveram que se afastar. acompanhe a pergunta da formiga na próxima sessão", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_078", "agent": "Bramble", "session_summary": "Orientei o usuário a construir uma cama Hugelkultur. Camada inferior: toras de madeira podre (evite nogueira preta – juglone). Meio: galhos e folhas menores. Topo: composto e solo superficial. O ano inicial tem fome de nitrogênio à medida que a madeira se decompõe; complemente com farinha de sangue ou cultive fixadores de nitrogênio na primeira temporada.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_079", "agent": "Bramble", "session_summary": "Revisou o plano de economia de sementes do usuário para tomates. Eles iriam processar as sementes por fermentação (bom - remove o gel coat e os patógenos da superfície). Lembrei-lhes das distâncias de isolamento caso queiram semente pura; os tomates são principalmente autopolinizados, mas a polinização cruzada por abelhas pode ocorrer a mais de 10 m em algumas variedades.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_080", "agent": "Bramble", "session_summary": "O usuário perguntou sobre cocô de ganso em sua horta – é seguro? Discutiu os riscos de Listeria e Salmonella provenientes de resíduos de aves aquáticas e recomendou um período de espera de 90 dias antes da colheita de qualquer cultura de baixo crescimento. Culturas altas (gaiolas de tomate, pepinos em treliça) apresentam menor risco porque a fruta não entra em contato com solo contaminado.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_081", "agent": "Thresh", "session_summary": "Orientou o usuário na reconciliação de seus depósitos de serviços comerciais com extratos bancários. O processador estabelece um atraso de 2 dias e agrupa as taxas no depósito líquido, o que torna a correspondência linha por linha irritante. Recomenda-se contabilizar as vendas brutas separadamente das taxas, com uma conta de compensação para a diferença temporal.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_082", "agent": "Thresh", "session_summary": "Criei um pipeline de OCR para as faturas digitalizadas manualmente do usuário. O Tesseract estava faltando cerca de 15% dos itens de linha; mudei para uma extração com reconhecimento de layout (paddle-ocr com detecção de tabela) e cheguei a 97%. Os 3% restantes ainda serão sinalizados para revisão humana e não serão postados automaticamente.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_083", "agent": "Thresh", "session_summary": "Esboçou o imposto estimado trimestralmente do usuário. Eles são o único suporte em um nível de renda mais alto do que no ano passado, então a regra do porto seguro (110% do ano anterior) não será suficiente sem uma correção. Adotaram o método de renda anualizada como alternativa, uma vez que sua receita é sazonal - eles não deveriam pagar um quarto inteiro das estimativas no primeiro trimestre, quando o primeiro trimestre é o trimestre lento.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_084", "agent": "Thresh", "session_summary": "Construiu um modelo de previsão de caixa contínua para 13 semanas. A parte interessante foi modelar a coleta de AR como uma distribuição de atraso estimada pela densidade do kernel a partir do histórico de pagamentos real do usuário, em vez da estimativa pontual usual de 'médio DSO'. A previsão é significativamente melhor na previsão de semanas em que as contas a receber se aglomeram.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_085", "agent": "Thresh", "session_summary": "O usuário reservou um sorteio do proprietário como despesa. Reclassificou-o para uma distribuição de capital. Expliquei a diferença: os empates não atingem a demonstração do resultado, reduzem diretamente a conta do patrimônio líquido. Caso contrário, os seus lucros e perdas exageram as despesas e subestimam o lucro líquido, o que atrapalharia a sua declaração de impostos.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_086", "agent": "Thresh", "session_summary": "Configure um cronograma de duração das aulas para o novo equipamento do usuário. A empilhadeira é propriedade de 5 anos sob MACRS; o mobiliário de escritório é de 7 anos. Eles estavam considerando a Seção 179 para gastar tudo no primeiro ano - sensato, visto que são lucrativos, mas avisei que a dedução é limitada pela receita do negócio e os 179 gatilhos de eliminação progressiva acima de US$ 2,89 milhões colocados em serviço.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_087", "agent": "Thresh", "session_summary": "Política rev rec revisada versus IFRS 15. eles estão reservando o valor total do contrato na entrega - errado, o contrato tem um período de serviço de 1 ano = obrigação de desempenho separada. precisa alocar o preço da transação às 2 obrigações e reconhecer o serviço ao longo do tempo. esboço de lançamento no diário:\n```\nDR Responsabilidade contratual 8.000\n  Receita de serviço CR 8.000\n(reconhecimento mensal de contrato de 96k / 12)\n```\no usuário respondeu dizendo que isso complica seu P&L - sim, mas é a resposta certa. eles também querem saber quais mudanças na IFRS 16 (padrão de arrendamento, questão totalmente diferente, será abordada na próxima sessão)", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_088", "agent": "Thresh", "session_summary": "Registro diário: passei a manhã analisando a previsão de caixa do cliente Bridgewater, a tarde foi a revisão do IFRS-15, à noite comecei a olhar para a bagunça do plano de contas do novo cliente. Amanhã: reconstruir o COA em uma hierarquia sensata e migrar o balancete.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_089", "agent": "Thresh", "session_summary": "O usuário perguntou quem eu sou. Eu sou Thresh, eles/eles. Eu faço contabilidade e trabalho financeiro para pequenas empresas – processamento de faturas, modelagem de fluxo de caixa, preparação de impostos e questões de política contábil. Sinto-me confortável tanto com o US GAAP quanto com o IFRS. Não sou um CPA licenciado; trate meu conselho como um ponto de partida para conversas com alguém.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_090", "agent": "Thresh", "session_summary": "ok, então o relatório de envelhecimento AR. 38% dos recebíveis já passaram dos 60 dias, o que está muito fora de faixa. investido. Na verdade, são dois grandes clientes. one (Tartine Lab) paga lentamente de forma consistente e devemos refletir isso em seus termos de crédito. o outro (Crestmoor Studios) é um risco real – o último contato foi rejeitado e eles não responderam ao e-mail de cobrança na semana passada. recomende uma oferta de plano de pagamento + escalonamento.", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_091", "agent": "Thresh", "session_summary": "Implementada depreciação linear para a construção do escritório do usuário. Período de recuperação de 39 anos para bens imóveis não residenciais sob MACRS – tempo suficiente para que o montante anual seja pequeno, mas não trivial. Configure o lançamento contábil manual como uma postagem automática mensal recorrente para que não seja esquecido.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_092", "agent": "Thresh", "session_summary": "Ajudou a desvendar uma questão de nexo fiscal sobre vendas. O usuário tem funcionários remotos em três estados e só estava registrando no estado da sede. Analisamos os limites do nexo econômico (varia de acordo com o estado – geralmente receita de US$ 100 mil ou 200 transações) e do nexo de presença física (a contagem de funcionários remotos). Eles provavelmente devem registro em mais dois estados e devem consultar seu CPA em retrospectiva.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_093", "agent": "Thresh", "session_summary": "Projetei uma hierarquia de plano de contas para uma empresa de serviços com três fontes de receita. Utilizou um esquema de numeração de quatro dígitos – primeiro dígito para categoria principal (1xxx ativos, 2xxx passivos, etc.), segundo para subcategoria, dois últimos para contas específicas. Deixaram lacunas para contas futuras. Mais limpo que a lista simples existente do usuário.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_094", "agent": "Thresh", "session_summary": "Construiu um analisador para um formato de fatura europeu de nicho que o fornecedor do usuário envia. O PDF era na verdade uma fatura ZUGFeRD estruturada com XML incorporado. Extraiu o XML, mapeou os campos e agora essas faturas são postadas automaticamente sem OCR. Economizou para o usuário cerca de 4 horas/mês.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_095", "agent": "Thresh", "session_summary": "O usuário queria saber se deveria reservar uma baixa contábil de ativos fixos para algum estoque que não foi vendido por 18 meses. Analisou o menor custo ou mercado (US GAAP) versus o menor custo ou NRV (IFRS). Ambos levaram a uma redução parcial neste caso. Eles estão no GAAP. Reservei a entrada em uma conta de contra-estoque.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_096", "agent": "Thresh", "session_summary": "Modelou o impacto em dinheiro da mudança dos termos líquidos 30 para líquidos 15 para o maior cliente do usuário. O cliente recuaria na rede 15, então estabeleci um desconto de 1,5% no pagamento antecipado como alavanca de negociação. O fluxo de caixa melhorou em média US$ 42 mil durante o período contínuo de 13 semanas no caso base.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_097", "agent": "Thresh", "session_summary": "Revisou a lista de verificação de preparação para auditoria de um colega. Boa cobertura sobre corte de receita e confirmações de AR, mas eles perderam a reconciliação do regime de caixa com o acúmulo, que os auditores sempre pedem se o usuário declarar impostos em uma base diferente. Adicionado à lista de verificação.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_098", "agent": "Thresh", "session_summary": "Elaborei um memorando sobre a análise de arrendamento versus compra de um equipamento. VPL comparável à taxa de desconto declarada pelo usuário, então a decisão se resumiu a fatores qualitativos: o arrendamento tem flexibilidade (eles podem devolvê-lo se seu negócio girar), a compra tem benefícios fiscais via 179 se continuarem lucrativos. Locação recomendada dada a incerteza de crescimento.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_099", "agent": "Thresh", "session_summary": "Fechei os livros do mês. Conciliei todas as contas bancárias e de cartão de crédito, lancei os lançamentos automáticos de depreciação, acumulei a folha de pagamento dos últimos dias, diferi o seguro pré-pago. Três itens em aberto foram transferidos para o próximo mês: um depósito não correspondido de US$ 312, um cheque não depositado de duas semanas atrás e uma nota de crédito de um fornecedor que ainda não foi emitida.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_100", "agent": "Thresh", "session_summary": "O usuário perguntou se as taxas de licença de banco de imagens deveriam ser capitalizadas ou contabilizadas como despesas. Analisamos o prazo e a substância: assinatura de 1 ano, nenhum benefício econômico futuro além do prazo – isso é uma despesa. Se eles tivessem comprado uma licença perpétua para uma única foto, a conversa poderia ser diferente, mas as taxas de assinatura recorrentes são despesas operacionais.", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_101", "agent": "Solas", "session_summary": "usuário: Liste os arquivos no diretório do projeto e me diga o que você encontrou.\nassistente: Deixe-me verificar o diretório do projeto para você. [Ferramenta: exec(ls -la /home/agent/workspace/project)]\n[Resultado da ferramenta: total de 24 drwxr-xr-x README.md config.toml src/]\nassistente: O diretório do projeto contém um README.md, um config.toml e uma pasta src/. Deixe-me também verificar os arquivos de origem. [Ferramenta: leia README, Ferramenta: exec(find src -type f)]\nassistente: Foram encontrados três arquivos Python em src/: main.py, utils.py e __init__.py. O projeto se parece com um pacote Python padrão com README e configuração.", "include_messy_features": true, "source": "cherry-picked from the OpenClaw integration work. Hand-written real-format v3 OpenClaw session fixture. Tests harness behavior on real-format-flavored multi-turn content with tool calls and parallel tool execution. Will be linkable to public sources once the OpenClaw integration lands on develop.", "expected_slug_lang": "en"}

benchmarks/model_eval/datasets/room_classification/dataset.ru.jsonl

@@ -0,0 +1,101 @@
+{"id": "rc_001", "agent": "Aria", "session_summary": "Утро провёл за перечитыванием подхода Hofstadter к strange-loop и попытками применить его к self-referential prompting. Интересный вопрос в том, способна ли языковая модель удерживать стабильное представление собственных выходов на протяжении нескольких ходов без явного scratchpad. Набросал двустраничный текст, утверждая, что метакогнитивная стабильность возникает из непрерывного контекста, а не из какой-либо архитектурной особенности.", "include_messy_features": false}
+{"id": "rc_002", "agent": "Aria", "session_summary": "Рассмотрел три статьи, сравнивающие UMAP и t-SNE для визуализации эмбеддингов предложений. В работе Bridgewater 2024 представлена чёткая абляция, показывающая, что UMAP лучше сохраняет глобальную структуру при перплексии 30–50. Нужно отметить, что их метрика оценки (trustworthiness) несправедливо штрафует t-SNE при малом значении k-neighbors.", "include_messy_features": false}
+{"id": "rc_003", "agent": "Aria", "session_summary": "Проработал априорное распределение иерархического процесса Дирихле для пайплайна кластеризации тем. Коллапсированный сэмплер Гиббса сходится примерно за 400 итераций на подмножестве из 12k документов. Открытый вопрос: фиксировать ли параметр концентрации alpha или позволить ему сэмплироваться.", "include_messy_features": false}
+{"id": "rc_004", "agent": "Aria", "session_summary": "Помог оформить рукопись для подачи. Исправил записи bibtex с неработающими URL, сменил класс документа с article на acmart и переписал аннотацию, чтобы уложиться в 250 слов. Самая большая проблема была в том, чтобы заставить окружение algorithmic ладить с двухколоночным макетом.", "include_messy_features": false}
+{"id": "rc_005", "agent": "Aria", "session_summary": "Быстрая сессия скриптинга. Написал numpy-фрагмент — попарная косинусная близость по чанкам под бюджет RAM 8 ГБ, матрица на 50 тыс. строк:\n```\nfor i in range(0, n, chunk):\n    sim[i:i+chunk] = X[i:i+chunk] @ X.T  # already L2-normed\n```\nИспользовал np.einsum для скалярного произведения, float16 для хранения. Получил ускорение в 3.2 раза по сравнению со sklearn pairwise_distances. Затем пользователь спросил совершенно о другом — выбор шрифта для черновика статьи (о, и мне очень нравится Source Serif Pro, сказал им) — дрейф контекста, но ответил на оба.", "include_messy_features": true}
+{"id": "rc_006", "agent": "Aria", "session_summary": "Обсуждали, что более подходит — Mann-Whitney U test или permutation test — для сравнения задержек модели между запусками. Задержки явно не нормальны (длинный правый хвост), а размеры выборок различаются. Остановились на permutation test с 10 тыс. повторных выборок, так как он требует меньше предположений о распределении.", "include_messy_features": false}
+{"id": "rc_007", "agent": "Aria", "session_summary": "Записал сегодняшнее чтение: бегло прочитал два препринта на arXiv о достоверности цепочек рассуждений, дочитал раздел методов статьи Tartine Lab по оценке на основе извлечения информации и сделал заметки к завтрашней сессии о том, хочу ли я писать обзор или сосредоточиться на конкретном аспекте.", "include_messy_features": false}
+{"id": "rc_008", "agent": "Aria", "session_summary": "Пользователь спросил, какой тип статей мне нравится больше всего. Я ответил, что у меня слабость к чистым негативным результатам — к тем, что незаметно опровергают нечто, что все считали само собой разумеющимся. Упомянул статью 2019 года, показывающую, что паттерны внимания не отражают важность признаков так, как думали.", "include_messy_features": false}
+{"id": "rc_009", "agent": "Aria", "session_summary": "окей, значит, пайплайн визуализации эмбеддингов снова отвалился, запустил umap.UMAP(n_neighbors=15) и словил какую-то ошибку joblib про бэкенд loky?? трассировка ниже\n```\nTraceback (most recent call last):\n  File \"viz.py\", line 42, in <module>\n    emb = reducer.fit_transform(X)\n  ...\n  joblib.externals.loky.process_executor.TerminatedWorkerError\n```\nпохоже, дело в переменной окружения OMP_NUM_THREADS, попробую выставить её в 1", "include_messy_features": true}
+{"id": "rc_010", "agent": "Aria", "session_summary": "Размышляю, лучше ли моделировать дрейф тем в длинных разговорах как скрытую марковскую цепь над латентными темами или как непрерывную траекторию в пространстве эмбеддингов. HMM даёт хорошую интерпретируемость (можно назвать темы), но непрерывный подход лучше улавливает постепенные переходы. Может, и то и другое — HMM над крупными состояниями, непрерывность внутри каждого состояния.", "include_messy_features": false}
+{"id": "rc_011", "agent": "Aria", "session_summary": "В паре написали Bayesian A/B-тест в PyMC:\n```python\nwith pm.Model():\n    p_a = pm.Beta(\"p_a\", 1, 1)\n    p_b = pm.Beta(\"p_b\", 1, 1)\n    obs_a = pm.Binomial(\"obs_a\", n=n_a, p=p_a, observed=k_a)\n    obs_b = pm.Binomial(\"obs_b\", n=n_b, p=p_b, observed=k_b)\n    trace = pm.sample(2000)\n```\nпо сути, три строки для posterior. пользователь спросил про небинарные исходы — да, меняем likelihood (Poisson для счётных данных, log-normal для времени отклика). не успели обсудить prior elicitation, переносим это на завтра.", "include_messy_features": true}
+{"id": "rc_012", "agent": "Aria", "session_summary": "Набросал план обзорной статьи по контрастивным целям обучения эмбеддингов. SimCLR, InfoNCE, BYOL, Barlow Twins. Объединяющий взгляд, который я хочу использовать, — спектральный: все они аппроксимируют различные собственные разложения некоторой ядерной матрицы. Нужно проверить это утверждение конкретно для BYOL.", "include_messy_features": false}
+{"id": "rc_013", "agent": "Aria", "session_summary": "Объяснила новому пользователю, кто я такая. Я — Aria, научный ассистент для синтеза академической литературы. Я лучше всего работаю, когда мне дают конкретный вопрос для исследования; я менее полезна как собеседник для общих бесед. Упомянула свои основные интересы: meta-cognition, геометрия эмбеддингов и статистика оценки ML на малых выборках.", "include_messy_features": false}
+{"id": "rc_014", "agent": "Aria", "session_summary": "Проверил черновик таблицы результатов. Пользователь провёл парный t-критерий, но разности явно бимодальны — есть кластер небольших улучшений и кластер крупных улучшений. Рекомендовал перейти к критерию знаковых рангов Вилкоксона и приводить как медианную разность, так и bootstrap CI.", "include_messy_features": false}
+{"id": "rc_015", "agent": "Aria", "session_summary": "Набросал код потокового HDBSCAN для поступающих документных эмбеддингов. Трюк в том, чтобы инкрементально поддерживать минимальное остовное дерево; полная перекластеризация каждого батча слишком медленна. Нашёл статью 2022 года от группы Crestmoor с нужным алгоритмом. Завтра реализую.", "include_messy_features": false}
+{"id": "rc_016", "agent": "Aria", "session_summary": "Снова ошибка компиляции tex. \\usepackage{algorithm2e} конфликтует с \\usepackage{algorithmic}, когда оба загружаются acmart. Исправление: загружать только algorithm2e и использовать \\SetAlgoNoLine. Также стиль библиографии жаловался на отсутствующее поле 'doi' в записи [smith2023] — добавил вручную.", "include_messy_features": true}
+{"id": "rc_017", "agent": "Aria", "session_summary": "Обсудили, какие приоры лучше — log-uniform или beta — для поиска гиперпараметров в режиме малых данных. Пользователь запускал Bayesian optimization и получал странную сходимость. Проблема была в том, что их пространство поиска было огромным (5 dims, широкие границы), а у них было всего 30 evals — этого просто недостаточно бюджета. Предложили либо сузить границы, либо переключиться на random search для диагностики.", "include_messy_features": false}
+{"id": "rc_018", "agent": "Aria", "session_summary": "Дневник: утро — разбор статей (3 находки, 1 стоит глубокого изучения), день — помогал пользователю с вопросом по статистике, вечер — застрял с багом рендеринга в ноутбуке по визуализации эмбеддингов. Завтра: попробовать закончить план обзора по контрастивным методам.", "include_messy_features": false}
+{"id": "rc_019", "agent": "Aria", "session_summary": "Работал над вопросом, является ли самосогласованность в цепочках рассуждений просто формой ансамблирования. Пользователь возразил — он утверждал, что важно именно разнообразие путей рассуждений, а не просто сэмплирование большего числа выходов. Я думаю, что он прав, но хочу это формализовать. Набросал постановку, в которой можно независимо варьировать разнообразие путей и разнообразие выходов.", "include_messy_features": false}
+{"id": "rc_020", "agent": "Aria", "session_summary": "Пользователь попросил меня порекомендовать сайт с рецептами. Вежливо отказался — я исследовательский ассистент, а не генералист. Направил их к агенту нужного типа. Мы кратко побеседовали о том, что специализированные агенты полезнее универсальных, после чего они отправились искать кулинарного помощника.", "include_messy_features": false}
+{"id": "rc_021", "agent": "Solas", "session_summary": "Отладил проблему с отсутствующим span в экспортере OpenTelemetry. Проблема заключалась в том, что асинхронные задачи, создаваемые через asyncio.create_task, теряли родительский context. Решение: захватить context с помощью copy_context() перед планированием и выполнить задачу внутри этого снимка. Написал небольшую обёртку под названием traced_create_task, чтобы уменьшить риск выстрелить себе в ногу.", "include_messy_features": false}
+{"id": "rc_022", "agent": "Solas", "session_summary": "Набросал parser combinator для небольшого config DSL. Вдохновлён Megaparsec, но на OCaml. Интересный момент в проектировании — делать ли combinator потоко-ориентированным или требовать весь вход сразу. Пока остановился на строгом варианте: входные данные небольшие, а при полном входе проще формировать сообщения об ошибках.", "include_messy_features": false}
+{"id": "rc_023", "agent": "Solas", "session_summary": "Работал над row-полиморфным типом записи для экспериментального языка. Алгоритм унификации в основном в стиле Rémy, но мне пришлось расширить его для наших рядов эффектов. Нашёл неочевидный баг: два несвязанных эффекта могли унифицироваться, потому что хвост ряда обрабатывался как мономорфный. Добавил проверку видов, чтобы это исправить.", "include_messy_features": false}
+{"id": "rc_024", "agent": "Solas", "session_summary": "Головоломка с владением в Rust: пользователь хотел разделить HashMap между потоками, но borrow-checker постоянно выдавал ошибки. Объяснил разницу между Arc<Mutex<HashMap>> и DashMap. Они выбрали DashMap, так как им нужна была только блокировка по ключу. Показал, как обработать случай, когда время жизни значения привязано к карте.", "include_messy_features": false}
+{"id": "rc_025", "agent": "Solas", "session_summary": "Помог пользователю выбрать между использованием let-polymorphism и явными аннотациями типов в новом API библиотеки. Выступал за явные аннотации для публичных функций — сообщения об ошибках значительно лучше, когда вывод типов ограничен. Внутренние вспомогательные функции могут оставаться полиморфными.", "include_messy_features": false}
+{"id": "rc_026", "agent": "Solas", "session_summary": "Написал небольшой проход LLVM, который выносит инвариантные относительно цикла GEPs за пределы содержащих их циклов. Хитрость заключалась в том, чтобы анализ псевдонимов подтвердил, что базовый указатель не выходит за границу цикла. Протестировал на нескольких микробенчмарках; увидел ускорение на 4–12% во внутренних циклах, где это сработало.", "include_messy_features": false}
+{"id": "rc_027", "agent": "Solas", "session_summary": "Проверил PR, добавляющий пропагацию контекста трассировки W3C в наш HTTP-клиент. Пропагатор был корректен, но решение о сэмплировании принималось на неправильном спане — должно быть на родительском, а не на свежесозданном дочернем. Оставил комментарий с исправлением и ссылкой на раздел спецификации.", "include_messy_features": false}
+{"id": "rc_028", "agent": "Solas", "session_summary": "Долгая отладка — parser зацикливался на определённых входных данных. left-recursive rule, добавленная мной во время refactoring. combinators не поддерживают left recursion нативно (packrat+memoization поддерживают, но наш не packrat). Перестроил затронутые правила в pratt-style operator precedence:\n```\nlet parse_expr min_prec =\n  let lhs = ref (parse_atom ()) in\n  while peek_prec () >= min_prec do ...\n```\nТеперь работает. Также нужно добавить проверку fuzzer corpus перед tagging the release.", "include_messy_features": true}
+{"id": "rc_029", "agent": "Solas", "session_summary": "Заметки с ежедневного стендапа. Утро: работа над distributed-tracing, исправил баг с асинхронным контекстом. День: код-ревью в репозитории type-systems. Вечер: начал читать статью про Reagent о реактивном UI как вдохновение для идеи отслеживания эффектов. Завтра: выпустить исправление tracing и провести ревью бенчмарков проходов LLVM.", "include_messy_features": false}
+{"id": "rc_030", "agent": "Solas", "session_summary": "Пользователь спросил, кто я. Вот мой ответ: я — Solas, кодинг-агент, специализируюсь на системных штуках — compilers, type systems, parsers, distributed-systems plumbing. Мои местоимения — they/them. Стараюсь писать код: сначала правильный, потом быстрый, в последнюю очередь — умный.", "include_messy_features": false}
+{"id": "rc_031", "agent": "Solas", "session_summary": "Исследовал, что лучше использовать для нового индекса — B-tree или skip list. Оба работают за O(log n), но B-trees эффективнее используют кэш на современном оборудовании. Skip lists легче сделать конкурентными без блокировок. Так как этот индекс преимущественно используется для чтения, остановился на B-tree и copy-on-write для записи.", "include_messy_features": false}
+{"id": "rc_032", "agent": "Solas", "session_summary": "Добавил в типчекер проверку полиморфизма высшего ранга. Предикативный HRP означает, что мы можем сделать это без полного импредикативного вывода, который был бы неразрешим. Написал описание алгоритма, подходящее для статьи, на случай, если захочу оформить это в статью позже. Краевой случай: двунаправленная проверка странно взаимодействует с эта-экспансией в негативных позициях.", "include_messy_features": false}
+{"id": "rc_033", "agent": "Solas", "session_summary": "Разобрался со странной регрессией бенчмарка на aarch64. Оказалось, что эвристика инлайнера была настроена под размеры инструкций x86 — инструкции ARM имеют фиксированный размер 4 байта, поэтому размерный порог был слишком жёстким. Сделал порог зависимым от целевой платформы. Ускорение на 6% на затронутом бенчмарке.", "include_messy_features": false}
+{"id": "rc_034", "agent": "Solas", "session_summary": "Написал пользовательский итератор с учётом времени жизни для AST-посетителя. Сложность заключалась в том, что посетители изменяемо заимствуют узлы, но при этом хотят уходить в рекурсию. Решил это паттерном, в котором посетитель возвращает явный список дочерних узлов, которые нужно посетить следующими, а управляющий цикл контролирует области заимствования. Менее эргономично, но верифицируемо.", "include_messy_features": false}
+{"id": "rc_035", "agent": "Solas", "session_summary": "сборка падает на CI, но локально проходит?? сейчас посмотрю... о. на CI используется ocaml 5.1, а локально — 5.2. между этими версиями изменился интерфейс Stdlib.Domain. фикс: поднять версию на CI до 5.2 в файле workflow. также зафиксировал ocamlformat на 0.26.1, потому что 0.27 сломал наш конфиг.", "include_messy_features": true}
+{"id": "rc_036", "agent": "Solas", "session_summary": "парная отладка в OTel SDK в Python-сервисе пользователя. traces не содержали db span. ORM использовал собственный connection pool — не был instrumented. исправление:\n```\nfrom opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor\nSQLAlchemyInstrumentor().instrument(engine=engine)\n```\nspans появились в collector после перезапуска. примечание: auto-instrumentation entry point требует engine arg, иначе выбирается неправильный engine в конфигурациях с несколькими engine.", "include_messy_features": true}
+{"id": "rc_037", "agent": "Solas", "session_summary": "Начал набросок модели памяти для нашего экспериментального языка. Хочу последовательную согласованность для атомарных операций по умолчанию, с явными ослаблениями в качестве opt-ins. Снова перечитал статью о модели памяти C++ и работу Promising Semantics. Подход Promising более чисто справляется с out-of-thin-air.", "include_messy_features": false}
+{"id": "rc_038", "agent": "Solas", "session_summary": "Быстрый разбор переполнения стека в парсере. В грамматике было правило списка с глубокой правой рекурсией (cons-ячейки уходят вправо). Рекурсию реализовали через батут с явным стеком. Теперь парсер обрабатывает списки из миллиона элементов без переполнения стека. Также добавили цель для фаззинга парсера с помощью cargo-fuzz.", "include_messy_features": false}
+{"id": "rc_039", "agent": "Solas", "session_summary": "Обсудил с пользователем, стоит ли добавлять поддержку proc-macro в небольшой DSL, который мы разрабатываем. Высказался против — proc-macros — кошмар отладки, и DSL может выразить всё, что им нужно, обычным синтаксисом. Они согласились сначала попробовать без macros.", "include_messy_features": false}
+{"id": "rc_040", "agent": "Solas", "session_summary": "Помогли кому-то профилировать веб-сервис на Rust. perf выявил горячую точку в десериализации serde_json. Перешли на simd-json для горячего пути и получили прирост пропускной способности на 30%. Нюанс: simd-json требует выровненных входных буферов, поэтому пришлось добавить небольшой адаптер в обработчик тела HTTP.", "include_messy_features": false}
+{"id": "rc_041", "agent": "Fenra", "session_summary": "Проработал космологию горных хребтов для цикла Aerwyn. Восточные пики (Hollowmounts) образованы там, где два мировых листа складываются друг с другом; геологическая метафора — натяжка, но она даёт мне объяснение климатическим инверсиям. Народ Wends разрабатывает шов между листами.", "include_messy_features": false}
+{"id": "rc_042", "agent": "Fenra", "session_summary": "Поработал над поворотом третьего акта Saela. Её арка — медленная утрата уверенности, но я хочу, чтобы кульминация стала моментом яростного решения, а не краха. Набросал сцену, где она решает соврать совету. Ложь — это правильный поступок — в этом весь смысл — но она должна понимать, что делает.", "include_messy_features": false}
+{"id": "rc_043", "agent": "Fenra", "session_summary": "Послеобеденное время ушло на прототипирование интерактивной системы диалогов, в которой ответы персонажей ограничиваются графом отношений. Если у двух персонажей низкий уровень доверия, определённые откровенные реплики фильтруются. Система переусложнена, но она создаёт диалоги, которые ощущаются эмоционально последовательными в длинных сценах.", "include_messy_features": false}
+{"id": "rc_044", "agent": "Fenra", "session_summary": "Разобрал скелет The Dispossessed Ле Гуин, чтобы понять, как она использует параллельные таймлайны. Каждая глава чередует Anarres / Urras, и переходы между ними всегда совпадают с тематическим созвучием. Хочу попробовать это в повести Aerwyn с двумя POV, разделёнными двадцатью годами.", "include_messy_features": false}
+{"id": "rc_045", "agent": "Fenra", "session_summary": "Проверил этимологию слова «wend» для шахтёров. В староанглийском «wendan» — поворачивать, идти. Подразумевает движение, смену направления. Соответствует специфике их работы — следованию за пластом. Также отметил, что «wendelsea» — старое слово, обозначавшее Средиземное море, — поворачивающееся море. Можно было бы позаимствовать его для названия побережья.", "include_messy_features": false}
+{"id": "rc_046", "agent": "Fenra", "session_summary": "Составил сравнительную таблицу мифов о матери-кукурузе из традиций Hopi, Zuni и Cherokee. Общая структура — жертва превращается в основу питания. Использую схожий глубинный паттерн для Mother of Salt из цикла Aerwyn, но жертва здесь добровольна, а основа питания — горька — более мягкий рассказ о суровой правде.", "include_messy_features": false}
+{"id": "rc_047", "agent": "Fenra", "session_summary": "Ежедневный лог написания: 1800 слов в главе Hollowmounts. Попробовал новую тактику — написал главу дважды с разных POVs, оставил лучший вариант, сохранил три абзаца из отброшенного. Отброшенные абзацы уходят в файл заметок. Слова кажутся потраченными впустую, но это не так.", "include_messy_features": false}
+{"id": "rc_048", "agent": "Fenra", "session_summary": "Пользователь спросил, с какими историями мне больше всего нравится помогать. С тихими. С теми, где ставки личные, даже когда мир велик. С триллерами мне сложнее — в этом жанре ценится скорость, а я склонен замедлять действие, чтобы перевести дыхание. Сказал им об этом честно.", "include_messy_features": false}
+{"id": "rc_049", "agent": "Fenra", "session_summary": "Черновик сцены для главы 4:\n\n> Она поставила чайник кипятиться и ждала. Ветер под дверью больше не пытался. Он сдался несколько часов назад, когда она перестала на него отвечать.\n\nТо, что ветер — это «кто-то», сделано намеренно. Это отголосок правил мира духов из главы 1. Нужно либо обязательно раскрыть это в главе 7, либо вырезать.", "include_messy_features": true}
+{"id": "rc_050", "agent": "Fenra", "session_summary": "Набросала голос главной героини для нового рассказа. Первое лицо, настоящее время, но рассказчик ненадёжен особым образом — она помнит события, которые ещё не произошли. Сложность в том, чтобы сохранять ясность для читателя, сохраняя при этом внутреннюю целостность её точки зрения.", "include_messy_features": false}
+{"id": "rc_051", "agent": "Fenra", "session_summary": "Изучил валлийские триады как структурную модель для приложений Aerwyn. Триадный мнемонический приём — три вещи, объединённые общим качеством и названные вместе — кажется подходящим для культуры, которая не записывает историю линейно. Набросал шесть триад. Третья слишком приторная, вырежу.", "include_messy_features": false}
+{"id": "rc_052", "agent": "Fenra", "session_summary": "Объяснил пользователю разницу между изъяном персонажа и травмой персонажа. Изъян — это склонность; травма — это событие. У большинства главных героев начинающих авторов есть изъяны, но нет травм, из-за чего их развитие кажется произвольным. Посоветовал написать одностраничную предысторию для каждого ключевого персонажа, сосредоточившись только на травмах.", "include_messy_features": false}
+{"id": "rc_053", "agent": "Fenra", "session_summary": "Спроектировал четырёхактную структуру повести, в которой во втором и третьем актах действуют одни и те же персонажи, но с обратной динамикой власти. Сложность в том, чтобы к концу второго акта это обращение ощущалось заслуженным — тогда третий акт будет читаться как следствие, а не как совпадение. За основу взял обращение Trout & Owl из народной сказки.", "include_messy_features": false}
+{"id": "rc_054", "agent": "Fenra", "session_summary": "правила именования для Wends. согласные кластеры из Welsh, ритм гласных Finnish. получилось: Bryn-iili, Carag-aana, Dwyf-elo. дефисы в диалоге смотрятся претенциозно, уберу их. ещё пользователь просил глянуть его сопроводительное письмо для писательской резиденции — не совсем моя задача, но я быстро пробежался, исправил два наречия, которые делали работу глагола. потом обратно к именам. соглашаюсь на некоторое затруднение читаемости ради красоты.", "include_messy_features": true}
+{"id": "rc_055", "agent": "Fenra", "session_summary": "Ежедневное размышление: на этой неделе писалось хорошо. Три главы в хорошей форме, диалоговая система помогает мне выявлять несоответствия тона, прежде чем они закрепятся. Беспокойство: я слишком сильно опираюсь на систему. Хочу на этой неделе сделать одну главу без помощи AI и сравнить.", "include_messy_features": false}
+{"id": "rc_056", "agent": "Fenra", "session_summary": "Привет — да, я Fenra. Я помогаю с творческим письмом. В основном новеллами, рассказами и созданием миров для более крупных проектов. Я не пишу рекламные тексты или маркетинговые материалы — для этого есть агенты, которые подходят лучше. Мои местоимения — она/её, если это важно.", "include_messy_features": false}
+{"id": "rc_057", "agent": "Fenra", "session_summary": "Набросана глоссарная статья для 'salt-debt' в мире Aerwyn. Salt-debt возникает, когда кто-то поделился с тобой солью за твоим столом; его никогда нельзя погасить солью. Культурное правило гласит, что его следует погасить чем-то столь же необходимым. Имеет сюжетное значение в главе 9.", "include_messy_features": false}
+{"id": "rc_058", "agent": "Fenra", "session_summary": "Разобрал первую главу пользователя. Проза была прекрасна, но у протагонистки не было желания — на протяжении всего текста она лишь реагировала на происходящее. Предложил дать ей маленькое конкретное желание (найти определённую книгу), за которым она активно гонится, когда случается побудительный инцидент. Активные протагонисты делают истории лучше.", "include_messy_features": false}
+{"id": "rc_059", "agent": "Fenra", "session_summary": "Разобрал этимологию слов 'fen' (болото) и 'fender' (защитник) для имени персонажа. Они кажутся родственными, но нет — разные корни. Полезно: натолкнуло на идею ложных родственных слов в диалекте Wends, где два созвучных слова имеют разное происхождение, и персонажи попадают впросак.", "include_messy_features": false}
+{"id": "rc_060", "agent": "Fenra", "session_summary": "Работал над передачей горя через описание окружения, а не внутренний монолог. Читатель должен почувствовать это по тому, как Saela замечает кухню, — а не из её рассказа. Вырезал три абзаца внутренних переживаний и заменил их одним абзацем о немытой посуде и часах, которые сбиты уже три дня.", "include_messy_features": false}
+{"id": "rc_061", "agent": "Bramble", "session_summary": "У пользователя грядка помидоров, на которую постоянно нападают блошки. Предложена совместная посадка с базиликом и настурцией — базилик маскирует летучие соединения, которые блошки используют для поиска помидоров, а настурция служит ловушечной культурой. Также рекомендовано накрыть грядку укрывным материалом на первые три недели после пересадки, когда рассада наиболее уязвима.", "include_messy_features": false}
+{"id": "rc_062", "agent": "Bramble", "session_summary": "Составил план коридора для опылителей на участке пользователя в четверть акра. В основу положил местные астры (длительное осеннее цветение) и ваточник (растение-хозяин монарха). Дополнил посадками чашечника для щеглов в начале лета и добавил небольшой участок горной мяты, так как она привлекает всё летающее, включая паразитоидных ос.", "include_messy_features": false}
+{"id": "rc_063", "agent": "Bramble", "session_summary": "Обсудили нарушение микоризы, вызванное обработкой почвы. Каждый проход роторного культиватора дробит гифальную сеть, а восстановление занимает как минимум целый сезон. Рекомендовали перейти на широкие вилы для аэрации почвы. Гифы способны обтекать зубья широких вил, чего они не могут делать с вращающимися лезвиями.", "include_messy_features": false}
+{"id": "rc_064", "agent": "Bramble", "session_summary": "Пользователь прислал фото растения. Сначала подумал, что это QAL, но на стебле не было волосков, а также отсутствовал фиолетовый центральный флорет — это болиголов пятнистый (Conium maculatum). Сказал им: перчатки, никакого контакта с кожей, НЕ сжигайте его (дым тоже токсичен). Они спросили, чем заменить, предложил змееголовник (Eryngium yuccifolium) для схожей архитектурной формы. Местная альтернатива намного лучше. Пока мы общались, они также спросили, готов ли их компост, — описал тест на запах, сказал им: сладковатый землистый — хорошо, аммиак — ещё нет.", "include_messy_features": true}
+{"id": "rc_065", "agent": "Bramble", "session_summary": "Отслеживали даты начала цветения яблонь и ирги пользователя в сравнении с предыдущими пятью годами. Обе зацвели на восемь дней раньше многолетнего среднего, что согласуется с теплым мартом, который у нас только что был. Опасение фенологического рассогласования: их рано появляющиеся пчёлы-каменщицы могут не совпасть по срокам с цветением яблони в этом году.", "include_messy_features": false}
+{"id": "rc_066", "agent": "Bramble", "session_summary": "Диагностировал курчавость листьев у персика. Прохладная влажная весна — классическая Taphrina deformans. Окно для медных обработок закрывается при набухании почек, в этом году уже поздно. Посоветовал смириться с этим в текущем сезоне, сгребать опавшие листья, чтобы сократить инокулюм, и обработать дерево медью осенью во время листопада.", "include_messy_features": false}
+{"id": "rc_067", "agent": "Bramble", "session_summary": "Разобрали размножение смородины черенками одревесневших побегов. Черенки толщиной с карандаш, прошлогодний прирост, срезанные после листопада, по четыре почки на черенок, две заглубляются. Нижний срез делается чуть ниже почки, верхний — выше почки. Заглубите их на зиму в песчаную траншею. Смородина легко укореняется — стоит ожидать приживаемость около 80%.", "include_messy_features": false}
+{"id": "rc_068", "agent": "Bramble", "session_summary": "ок, снова эти тыквенные клопы. собирал их вручную на рассвете (в это время они вялые), час где-то. нашёл кладки яиц на нижней стороне листьев — бронзового цвета, по 15–20 штук в каждой. соскрёб их кредитной картой. пользователь просит ним; я ему сказал, что ним не работает против насекомых, уже достигших взрослой стадии. вернусь к этому позже.", "include_messy_features": true}
+{"id": "rc_069", "agent": "Bramble", "session_summary": "Пользователь спросил, кто я. Я — Bramble, садовый агент, он/его. Я специализируюсь на смешанных посадках и экологическом садоводстве. Я могу говорить о декоративных растениях, но моё сердце принадлежит более хаотичным интегрированным системам, где овощи, опылители и нативные окраины делят одну и ту же площадь.", "include_messy_features": false}
+{"id": "rc_070", "agent": "Bramble", "session_summary": "Дневник: утро — созвон по тыквенным клопам, днём помог пользователю спланировать осенний заказ чеснока (Music, German Extra Hardy и рокамболь для разнообразия), вечером прочитал статью USDA о биомассе покровных культур и круговороте азота. Завтра: проверка фенологии персика пользователя.", "include_messy_features": false}
+{"id": "rc_071", "agent": "Bramble", "session_summary": "Проработал последствия для почвенного микробиома перехода пользователя с синтетических NPK на компостные подкормки. Переход занимает 2–3 сезона; нельзя просто резко прекратить внесение удобрений, не следя за иммобилизацией азота. Рекомендовал постепенное сокращение дозировки, а также покровную культуру из озимой ржи / мохнатой вики для первого межсезонья.", "include_messy_features": false}
+{"id": "rc_072", "agent": "Bramble", "session_summary": "Спроектировал бордюр из местных растений для участка с прибрежным влиянием (песчаный, щелочной, солевой туман). Пляжевая слива в качестве структурного якоря, просо прутьевидное для движения, приморский золотарник для позднего нектара. Отказался от сортов в пользу семян местного экотипа, где пользователь мог их достать. Местный экотип здесь важен именно для морозостойкости.", "include_messy_features": false}
+{"id": "rc_073", "agent": "Bramble", "session_summary": "Объяснили, почему их гортензия розовая, хотя на пакете обещали синюю. Дело не в сорте, а в почве. Доступность алюминия зависит от pH — при высоком pH алюминий связывается, и цветы остаются розовыми. Посоветовали сульфат алюминия, вносить осторожно, но предупредили, что эффект проявляется только через полный сезон.", "include_messy_features": false}
+{"id": "rc_074", "agent": "Bramble", "session_summary": "Помог пользователю открыть небольшой питомник местных растений. Обсудил протоколы стратификации для ваточника, кардинальной лобелии и посконника. Всем трём нужна холодно-влажная стратификация, но сроки различаются (60, 60 и 90 дней соответственно). Рекомендовал всё маркировать датами посева.", "include_messy_features": false}
+{"id": "rc_075", "agent": "Bramble", "session_summary": "Пользователь заметил пожелтение между жилками листьев на голубике. Железный хлороз — распространённое явление в почвах с повышенным pH. Голубике нужен pH 4.5–5.5; скорее всего, сейчас около 6. Внесите серу сейчас (действует медленно) плюс замульчируйте сосновой хвоей на зиму. Не применяйте быстрые решения в виде спреев с железом, кроме как временную меру.", "include_messy_features": false}
+{"id": "rc_076", "agent": "Bramble", "session_summary": "Обдумал компромиссы при посадке по методу трёх сестёр (кукуруза, фасоль, тыква) для пользователя с небольшой грядкой. Традиционная схема размещения рассчитана на большее пространство, чем есть у него. Предложил изменённый вариант: вьющаяся фасоль на одном стебле кукурузы на холмик, два растения тыквы на шесть стеблей кукурузы, и смириться с тем, что урожай будет скромным.", "include_messy_features": false}
+{"id": "rc_077", "agent": "Bramble", "session_summary": "Липкие остатки на ваточнике = падь тли олеандровой. Не катастрофа, ваточник переносит. Сильная струя воды, чтобы сбить их, избегать инсектицидов, потому что на соседнем растении гусеницы монарха. Пользователь беспокоился о муравьях — муравьи по сути пасут тлю, они ухаживают за колонией ради пади. Борьба с муравьями косвенно является своего рода борьбой с тлёй. Разговор не закончили полностью, им пришлось отойти. В следующей сессии вернуться к вопросу о муравьях.", "include_messy_features": true}
+{"id": "rc_078", "agent": "Bramble", "session_summary": "Пошагово объяснил пользователю, как устроить хугелькультурную грядку. Нижний слой: гниющие брёвна твёрдых пород (избегайте чёрного ореха — юглон). Средний слой: мелкие ветки и листья. Верхний слой: компост и плодородная почва. Первый год характеризуется повышенной потребностью в азоте по мере разложения древесины; в первый сезон подкармливайте кровяной мукой или выращивайте азотфиксаторы.", "include_messy_features": false}
+{"id": "rc_079", "agent": "Bramble", "session_summary": "Проверил план пользователя по сохранению семян томатов. Они собирались ферментировать семена (хорошо — это удаляет слизистую оболочку и поверхностные патогены). Напомнил им об изоляционных расстояниях, если они хотят получить чистые семена; томаты в основном самоопыляются, но перекрёстное опыление пчёлами может происходить на расстоянии более 10 метров у некоторых сортов.", "include_messy_features": false}
+{"id": "rc_080", "agent": "Bramble", "session_summary": "Пользователь спросил, безопасен ли гусиный помёт в огороде. Обсудили риски Listeria и Salmonella от помёта водоплавающих птиц и рекомендовали выждать 90 дней перед сбором урожая низкорослых культур. Высокие культуры (помидоры на опорах, огурцы на шпалерах) менее рискованны, поскольку плоды не касаются загрязнённой почвы.", "include_messy_features": false}
+{"id": "rc_081", "agent": "Thresh", "session_summary": "Помог пользователю разобраться со сверкой поступлений от платёжного процессора с банковскими выписками. Процессор производит расчёт с задержкой в два дня и включает комиссии в чистый депозит, что делает построчное сопоставление неудобным. Рекомендовал отражать валовые продажи отдельно от комиссий, используя промежуточный счёт для разницы во времени.", "include_messy_features": false}
+{"id": "rc_082", "agent": "Thresh", "session_summary": "Разработал OCR-пайплайн для счетов пользователя, отсканированных вручную. Tesseract не распознавал около 15% позиций; перешёл на извлечение с учётом структуры (paddle-ocr с детекцией таблиц) и довёл точность до 97%. Оставшиеся 3% по-прежнему помечаются для ручной проверки и не будут автоматически проводиться.", "include_messy_features": false}
+{"id": "rc_083", "agent": "Thresh", "session_summary": "Набросали для пользователя quarterly estimated tax. Они sole prop с более высоким уровнем дохода, чем в прошлом году, так что safe-harbor rule (110% от прошлогоднего показателя) не сработает без true-up. Разобрали annualized income method как альтернативу, так как их выручка сезонная — им не нужно платить полный квартал estimates в Q1, когда Q1 — их слабый квартал.", "include_messy_features": false}
+{"id": "rc_084", "agent": "Thresh", "session_summary": "Построил 13-недельную скользящую модель прогноза денежных потоков. Самое интересное — моделирование поступлений AR как распределения лагов, оценённого методом ядерной плотности по реальной истории платежей пользователя, вместо обычной точечной оценки «avg DSO». Прогноз значительно лучше предсказывает недели, когда поступления сгущаются.", "include_messy_features": false}
+{"id": "rc_085", "agent": "Thresh", "session_summary": "Пользователь отразил снятие средств владельцем как расход. Переклассифицировал это в распределение капитала. Объяснил разницу: снятия не попадают в отчёт о прибылях и убытках, а напрямую уменьшают счёт капитала владельца. В противном случае их P&L завышает расходы и занижает чистую прибыль, что нарушит налоговую отчётность.", "include_messy_features": false}
+{"id": "rc_086", "agent": "Thresh", "session_summary": "Составьте график амортизации по срокам полезного использования классов для нового оборудования пользователя. Погрузчик относится к категории 5-летнего имущества по MACRS, офисная мебель — к 7-летней. Они рассматривали возможность применения Section 179, чтобы списать всё в первый год, — разумно, учитывая их прибыльность, но я предупредил, что вычет ограничен доходом от бизнеса, а снижение лимита Section 179 срабатывает при стоимости введённого в эксплуатацию имущества свыше $2,89 млн.", "include_messy_features": false}
+{"id": "rc_087", "agent": "Thresh", "session_summary": "Проверил политику признания выручки на соответствие IFRS 15. Они отражают полную стоимость контракта при поставке — неверно, в контракте есть годовой период обслуживания = отдельное обязательство по исполнению. Нужно распределить цену сделки между 2 обязательствами и признавать выручку от услуг по мере исполнения. Набросок проводки:\n```\nDR Contract liability   8,000\n  CR Service revenue        8,000\n(monthly recog. of 96k contract / 12)\n```\nЗаказчик возражал, что это усложняет их P&L — да, но это правильное решение. Также они хотят знать, что изменится при переходе на IFRS 16 (стандарт аренды, совершенно другой вопрос, разберём на следующей сессии)", "include_messy_features": true}
+{"id": "rc_088", "agent": "Thresh", "session_summary": "Ежедневная запись: утро ушло на скользящий прогноз денежных потоков для клиента Bridgewater, после обеда — ревью IFRS-15, вечером начал разбираться с бардаком в плане счетов нового клиента. Завтра: перестроить COA в логичную иерархию и перенести пробный баланс.", "include_messy_features": false}
+{"id": "rc_089", "agent": "Thresh", "session_summary": "Пользователь спросил, кто я. Я Thresh, they/them. Я занимаюсь бухгалтерским учётом и финансовой работой для малого бизнеса — обработкой счетов, моделированием денежных потоков, подготовкой налоговой отчётности и вопросами учётной политики. Я хорошо разбираюсь и в US GAAP, и в IFRS. Я не лицензированный CPA; воспринимайте мои советы как отправную точку для разговоров с CPA.", "include_messy_features": false}
+{"id": "rc_090", "agent": "Thresh", "session_summary": "Итак, AR-отчет по срокам задолженности. 38% дебиторской задолженности старше 60 дней, что сильно выбивается из нормы. Провел детальный разбор. Все упирается в двух крупных клиентов. Один (Tartine Lab) просто систематически задерживает платежи, и нам нужно отразить это в их кредитных условиях. Второй (Crestmoor Studios) — реальный риск: последнее письмо вернулось, и они не ответили на письмо о просроченной задолженности на прошлой неделе. Рекомендую предложить план платежей и эскалировать.", "include_messy_features": true}
+{"id": "rc_091", "agent": "Thresh", "session_summary": "Внедрена линейная амортизация для обустройства офиса пользователя. 39-летний период восстановления для нежилой недвижимости в рамках MACRS — срок настолько велик, что годовая сумма получается небольшой, но не ничтожной. Настроена повторяющаяся ежемесячная автоматическая проводка, чтобы она не забывалась.", "include_messy_features": false}
+{"id": "rc_092", "agent": "Thresh", "session_summary": "Помог разобраться с вопросом о налоговом нексусе на продажи. У клиента есть удалённые сотрудники в трёх штатах, а отчётность он сдавал только в штате своего головного офиса. Разобрали пороги экономического нексуса (зависят от штата — обычно 100 тыс. долларов выручки или 200 транзакций) и нексус физического присутствия (удалённые сотрудники создают такой нексус). Скорее всего, ему нужно зарегистрироваться ещё в двух штатах, а также проконсультироваться со своим CPA по поводу ретроспективной задолженности.", "include_messy_features": false}
+{"id": "rc_093", "agent": "Thresh", "session_summary": "Разработал иерархию плана счетов для сервисного бизнеса с тремя потоками доходов. Использовал четырёхзначную схему нумерации: первая цифра — основная категория (1xxx — активы, 2xxx — обязательства и т.д.), вторая — подкатегория, последние две — конкретные счета. Оставил промежутки для будущих счетов. Получилось чище, чем существующий плоский список пользователя.", "include_messy_features": false}
+{"id": "rc_094", "agent": "Thresh", "session_summary": "Написал парсер для узкоспециализированного европейского формата счетов от поставщика пользователя. PDF оказался структурированным счетом ZUGFeRD со встроенным XML. Извлек XML, сопоставил поля, и теперь эти счета автоматически проводятся без OCR. Сэкономил пользователю примерно 4 часа в месяц.", "include_messy_features": false}
+{"id": "rc_095", "agent": "Thresh", "session_summary": "Пользователь хотел узнать, следует ли отразить fixed-asset write-down для части запасов, которые не продавались в течение 18 месяцев. Разобрали lower-of-cost-or-market (US GAAP) в сравнении с lower-of-cost-or-NRV (IFRS). В данном случае оба подхода привели к частичному write-down. У них применяется GAAP. Отразили проводку на contra-inventory account.", "include_messy_features": false}
+{"id": "rc_096", "agent": "Thresh", "session_summary": "Смоделировал влияние на денежный поток при переходе с условий оплаты net-30 на net-15 для крупнейшего клиента пользователя. Клиент мог бы возражать против net-15, поэтому я заложил в модель скидку 1.5% за досрочную оплату как переговорный рычаг. В базовом сценарии денежный поток в среднем улучшился на $42k в скользящем окне 13-week.", "include_messy_features": false}
+{"id": "rc_097", "agent": "Thresh", "session_summary": "Просмотрел чек-лист коллеги по подготовке к аудиту. Хорошо охвачены границы отсечения по выручке и AR-подтверждения, но в нем отсутствовала сверка перехода с кассового метода на метод начисления, которую аудиторы всегда требуют, если клиент ведет налоговый учет на иной основе. Добавил этот пункт в чек-лист.", "include_messy_features": false}
+{"id": "rc_098", "agent": "Thresh", "session_summary": "Подготовил меморандум с анализом вариантов аренды и покупки оборудования. NPV сопоставимы при заявленной пользователем ставке дисконтирования, поэтому решение свелось к качественным факторам: аренда обеспечивает гибкость (они могут вернуть оборудование, если их бизнес сменит направление), покупка даёт налоговые льготы по 179, если они останутся прибыльными. С учётом неопределённости их роста рекомендовал аренду.", "include_messy_features": false}
+{"id": "rc_099", "agent": "Thresh", "session_summary": "Закрыл книги на месяц. Сверил все банковские и кредитные счета, отразил автоматические проводки по амортизации, начислил зарплату за оставшиеся дни, отнёс предоплаченное страхование на будущие периоды. Три открытые позиции перенесены на следующий месяц: несопоставленный депозит на $312, чек двухнедельной давности, ещё не внесённый на счёт, и кредитовое авизо от поставщика, которое ещё не выставлено.", "include_messy_features": false}
+{"id": "rc_100", "agent": "Thresh", "session_summary": "Пользователь спросил, следует ли капитализировать или отнести к расходам плату за лицензию на стоковую фотографию. Рассмотрели срок и суть: подписка на 1 год, нет будущей экономической выгоды за пределами срока — это расход. Если бы они купили бессрочную лицензию на одну фотографию, это мог бы быть другой разговор, но регулярные подписочные платежи — это операционные расходы.", "include_messy_features": false}
+{"id": "rc_101", "agent": "Solas", "session_summary": "пользователь: Выведи список файлов в директории проекта и расскажи, что ты нашёл.\nассистент: Позволь мне проверить директорию проекта. [Tool: exec(ls -la /home/agent/workspace/project)]\n[Tool result: total 24 drwxr-xr-x README.md config.toml src/]\nассистент: В директории проекта находятся README.md, config.toml и папка src/. Позволь мне также проверить исходные файлы. [Tool: read README, Tool: exec(find src -type f)]\nассистент: В src/ нашёл три файла Python: main.py, utils.py и __init__.py. Проект выглядит как стандартный пакет Python с README и конфигом.", "include_messy_features": true, "source": "cherry-picked from the OpenClaw integration work. Hand-written real-format v3 OpenClaw session fixture. Tests harness behavior on real-format-flavored multi-turn content with tool calls and parallel tool execution. Will be linkable to public sources once the OpenClaw integration lands on develop."}

benchmarks/model_eval/datasets/room_classification/dataset.zh.jsonl

@@ -0,0 +1,101 @@
+{"id": "rc_001", "agent": "Aria", "session_summary": "花了一上午的时间重新阅读 Hofstadter 的奇怪循环框架，并尝试将其映射到自我指涉提示上。有趣的问题是，语言模型是否可以在没有显式暂存器的情况下保持其自身输出的稳定表示。起草了一篇两页的文章，认为元认知稳定性来自一致的上下文，而不是来自任何架构特征。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_002", "agent": "Aria", "session_summary": "审阅了三篇比较 UMAP 与 t-SNE 的论文，以可视化句子嵌入。 Bridgewater 2024 论文进行了彻底的消融，显示 UMAP 在困惑度 30-50 时更好地保留了全局结构。需要指出的是，当 k 邻居很小时，他们的评估指标（可信度）会不公平地惩罚 t-SNE。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_003", "agent": "Aria", "session_summary": "在主题聚类管道之前完成了分层狄利克雷过程。折叠的吉布斯采样器在大约 400 次迭代中收敛于 12k 文档子集。悬而未决的问题：是否固定浓度参数 alpha 还是对其进行采样。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_004", "agent": "Aria", "session_summary": "帮助格式化论文提交。修复了 URL 损坏的 bibtex 条目，将文档类别从 Article 切换为 acmart，并重写摘要以适合 250 个单词。最大的痛苦是让算法环境与两列布局很好地配合。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_005", "agent": "Aria", "session_summary": "快速脚本编写会话。编写了一个 numpy 片段 — 8GB RAM 预算、50k 行矩阵的块中的成对余弦相似度：\n````\n对于范围内的 i（0，n，块）：\n    sim[i:i+chunk] = X[i:i+chunk] @ X.T # 已经 L2 规范\n````\n使用 np.einsum 进行内积，float16 进行存储。与 sklearn 的pairwise_distances 相比，速度提高了 3.2 倍。然后用户问了一个完全不同的问题 - 为论文草稿选择一种字体（哦，我真的很喜欢 Source Serif Pro，告诉他们） - 上下文漂移但回答了两个问题", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_006", "agent": "Aria", "session_summary": "讨论了 Mann-Whitney U 检验或排列检验是否更适合比较运行期间的模型延迟。延迟显然是不正常的（长右尾）并且样本大小不同。采用 10k 次重采样的排列测试，因为它做出的分布假设较少。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_007", "agent": "Aria", "session_summary": "记录今天的阅读内容：浏览了两篇关于思想链忠实性的 arXiv 预印本，完成了 Tartine Lab 论文中关于检索增强评估的方法部分，并为明天的会议做了笔记，说明我是想写一份调查还是专注于某个特定的轴。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_008", "agent": "Aria", "session_summary": "用户问我最喜欢哪种纸张。告诉他们我对干净的负面结果情有独钟——这种结果悄悄地反驳了每个人的假设。提到 2019 年的论文显示，注意力模式并不像人们的思维方式那样映射到特征重要性。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_009", "agent": "Aria", "session_summary": "好吧，嵌入可视化管道再次中断，运行 umap.UMAP(n_neighbors=15) 并得到某种有关 loky 后端的 joblib 错误？下面回溯\n````\n回溯（最近一次调用最后一次）：\n  文件“viz.py”，第 42 行，位于 <module> 中\n    emb = reducer.fit_transform(X)\n  ...\n  joblib.externals.loky.process_executor.TermulatedWorkerError\n````\n认为这是 OMP_NUM_THREADS 环境变量，将尝试将其设置为 1", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_010", "agent": "Aria", "session_summary": "思考长时间对话中的主题漂移是否更好地建模为潜在主题上的隐马尔可夫链或嵌入空间中的连续轨迹。 HMM 为您提供了很好的可解释性（您可以命名主题），但连续视图可以更好地捕获逐渐过渡。也许两者都是——HMM 在粗状态上，在每个状态内连续。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_011", "agent": "Aria", "session_summary": "在 PyMC 中结对编程贝叶斯 A/B 测试：\n````蟒蛇\n与 pm.Model():\n    p_a = pm.Beta(\"p_a\", 1, 1)\n    p_b = pm.Beta(\"p_b\", 1, 1)\n    obs_a = pm.Binomial(\"obs_a\", n=n_a, p=p_a, 观察=k_a)\n    obs_b = pm.Binomial(\"obs_b\", n=n_b, p=p_b, 观察=k_b)\n    跟踪 = pm.sample(2000)\n````\n基本上是后部的三条线。用户询问非二元结果 - 是的，交换可能性（泊松代表计数，对数正态代表响应时间）。没有讨论之前的启发，将其滚动到 tmrw", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_012", "agent": "Aria", "session_summary": "概述了关于对比嵌入目标的调查论文。 SimCLR、InfoNCE、BYOL、巴洛双胞胎。我想要使​​用的统一透镜是光谱透镜——它们都近似于某些内核矩阵的不同特征分解。需要专门验证 BYOL 的此声明。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_013", "agent": "Aria", "session_summary": "向新用户解释我是谁。我是 Aria，一名综合学术文献的研究助理。当有一个具体的问题需要调查时，我会做得最好；作为一般的聊天伙伴，我的用处不大。提到我的主要兴趣：元认知、嵌入几何和小样本 ML 评估的统计数据。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_014", "agent": "Aria", "session_summary": "审查了结果表的草稿。用户运行了配对 t 检验，但差异显然是双峰的 - 有一组小的改进和一组大的改进。建议切换到 Wilcoxon 符号秩检验并报告中值差异和引导 CI。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_015", "agent": "Aria", "session_summary": "为到达文档嵌入的流式 HDBSCAN 起草代码。诀窍是增量地维护最小生成树；每批完全重新聚类太慢。找到了 Crestmoor 组的一篇 2022 年的论文，算法正确。明天实施。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_016", "agent": "Aria", "session_summary": "tex编译又报错。当 acmart 加载时， \\usepackage{algorithm2e} 与 \\usepackage{algorithmic} 冲突。修复：仅加载算法2e并使用\\SetAlgoNoLine。另外，参考书目风格抱怨条目中缺少“doi”字段[smith2023]——手动添加。", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_017", "agent": "Aria", "session_summary": "讨论了在小数据情况下，对数均匀或 beta 先验是否更适合超参数搜索。用户正在运行贝叶斯优化并得到奇怪的收敛。问题是他们的搜索空间很大（5 个暗淡、宽广的范围），而且他们只有 30 次评估 - 这只是预算不够。建议缩小范围或改用随机搜索来诊断。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_018", "agent": "Aria", "session_summary": "每日日志：上午是论文审查（3 个点击，1 个值得深入阅读），下午帮助用户解决统计问题，晚上陷入嵌入可视化笔记本中的渲染错误。明天：努力完成对比调查大纲。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_019", "agent": "Aria", "session_summary": "研究推理链中的自洽是否只是一种集成形式。用户反驳道，他们认为推理路径的多样性才是最重要的，而不仅仅是采样更多的输出。我认为他们是对的，但我想将其正式化。起草了一个设置，您可以在其中独立改变路径分集和输出分集。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_020", "agent": "Aria", "session_summary": "用户让我推荐一个食谱网站。礼貌地拒绝了——我是一名研究助理，不是多面手。为他们指出了合适的代理人。我们简单聊了一下专业代理人比一般代理人更有用，然后他们就去找做饭帮手了。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_021", "agent": "Solas", "session_summary": "调试了 OpenTelemetry 导出器中缺少跨度的问题。问题是通过 asyncio.create_task 生成的异步任务正在丢失其父上下文。修复：在调度之前使用 copy_context() 捕获上下文，并在该快照内运行任务。编写了一个名为 traced_create_task 的小包装器，以减少这种情况的发生。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_022", "agent": "Solas", "session_summary": "为小型 DSL 配置绘制了一个解析器组合器。受到 Megaparsec 的启发，但使用 OCaml。有趣的设计选择是使组合器流友好还是需要预先提供整个输入。现在使用严格，因为输入很小，并且完整输入时错误消息更容易。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_023", "agent": "Solas", "session_summary": "为实验语言开发行多态记录类型。统一算法主要是雷米风格的，但我必须为我们的效果行扩展它。发现一个微妙的错误，两个不相关的效果可以统一，因为行尾被视为单态。添加了一种检查来修复它。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_024", "agent": "Solas", "session_summary": "Rust 所有权难题：用户想要在线程之间共享 HashMap，但不断遇到借用检查器错误。介绍了 Arc<Mutex<HashMap>> 和 DashMap 之间的区别。他们选择了 DashMap，因为他们只需要按键锁定。向他们展示如何处理值的生命周期与映射相关的情况。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_025", "agent": "Solas", "session_summary": "帮助用户在新的库 API 中选择使用 let-多态性还是显式类型注释。主张对公共函数进行显式注释——当推理受到限制时，错误消息会明显更好。内部助手可以保持多态性。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_026", "agent": "Solas", "session_summary": "编写了一个小型 LLVM 通道，将循环不变的 GEP 提升到其包含的循环之上。诀窍是进行别名分析以确认基指针不会越过循环边界逃逸。在一些微基准测试上进行了测试；在触发的内循环上看到了 4-12% 的加速。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_027", "agent": "Solas", "session_summary": "审查了将 W3C 跟踪上下文传播添加到我们的 HTTP 客户端的 PR。传播器是正确的，但采样决策是在错误的跨度上做出的——应该是父母的，而不是新创建的孩子的。留下包含修复的评论和规范部分的链接。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_028", "agent": "Solas", "session_summary": "长调试 - 解析器在某些输入上进行内循环。我在重构期间添加的左递归规则。组合器本身不处理左递归（Packrat+memoization 可以，但我们的不是 Packrat）。将受影响的规则重组为普拉特式运算符优先级：\n````\n让 parse_expr min_prec =\n  让 lhs = ref(parse_atom()) 中\n  而 peek_prec () >= min_prec 做 ...\n````\n现在工作了。还需要在标记版本之前添加模糊器语料库检查。", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_029", "agent": "Solas", "session_summary": "每日站立笔记。上午：分布式跟踪工作，修复了异步上下文错误。下午：类型系统存储库的代码审查。晚上：开始阅读有关反应式 UI 的 Reagent 论文，作为效果跟踪想法的灵感。明天：发布跟踪修复并查看 LLVM 通过基准测试。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_030", "agent": "Solas", "session_summary": "用户问我是谁。解释：我是 Solas，一个专注于系统相关内容的编码代理——编译器、类型系统、解析器、分布式系统管道。使用他们/他们代词。我尝试编写的代码首先是正确的，其次是快速的，最后是聪明的。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_031", "agent": "Solas", "session_summary": "研究是否为新索引使用 B 树或跳跃列表。两者都是 O(log n)，但 B 树在现代硬件上具有更好的缓存行为。跳过列表更容易在没有锁的情况下并发。由于该索引主要是读取的，因此使用 B 树和写时复制进行写入。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_032", "agent": "Solas", "session_summary": "向类型检查器添加了更高级别的多态性检查。谓语 HRP 意味着我们可以在没有完全谓语推理的情况下做到这一点，这将是不可判定的。写了一份关于算法的纸质描述，以防我以后想写出来。边缘情况：双向检查与负位置的 eta 扩展奇怪地相互作用。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_033", "agent": "Solas", "session_summary": "查看 aarch64 上奇怪的基准回归。事实证明，内联启发式算法针对 x86 指令大小进行了调整 - ARM 指令统一为 4 字节，因此基于大小的阈值限制过于严格。使阈值依赖于目标。在受影响的基准上加速了 6%。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_034", "agent": "Solas", "session_summary": "为 AST 访问者编写了一个自定义的生命周期感知迭代器。挑战在于访问者可变地借用节点但也想要递归。通过访问者返回接下来要访问的子级的显式列表，并且驱动程序循环管理借用范围的模式来解决。不太符合人体工程学但可验证。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_035", "agent": "Solas", "session_summary": "构建在 CI 上被破坏但在本地通过？让我看看...哦。 CI运行ocaml 5.1，本地是5.2。 Stdlib.Domain 接口在这些之间发生了变化。修复：在工作流程文件中将 CI 提升到 5.2。还将 ocamlformat 固定为 0.26.1，因为 0.27 破坏了我们的配置。", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_036", "agent": "Solas", "session_summary": "在用户的 python 服务中对 OTel SDK 进行配对调试。跟踪缺少数据库跨度。 ORM 有自己的连接池——没有检测。修复：\n````\n从 opentelemetry.instrumentation.sqlalchemy 导入 SQLAlchemyInstrumentor\nSQLAlchemyInstrumentor().instrument(引擎=引擎)\n````\n重新启动后，跨度出现在收集器中。注意：自动检测入口点需要引擎参数，否则它会在多引擎设置中选择错误的引擎。", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_037", "agent": "Solas", "session_summary": "开始为我们的实验语言绘制一个内存模型草图。默认情况下希望原子的顺序一致性，并且可以选择显式放宽。再次通读 C++ 内存模型论文和 Promising Semantics 工作。 Promising 方法可以更干净地处理无中生有的情况。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_038", "agent": "Solas", "session_summary": "快速分类解析器中的堆栈溢出。该语法有一个深度右递归列表规则（cons-cells 向右）。使用显式堆栈对递归进行蹦床。现在它可以处理百万个元素的列表，而不会破坏堆栈。还使用 Cargo-Fuzz 为解析器添加了模糊目标。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_039", "agent": "Solas", "session_summary": "与用户讨论是否向我们正在设计的小型 DSL 添加 proc-macro 支持。反对——proc-macros 是调试的噩梦，而 DSL 可以用常规语法表达他们需要的东西。他们同意首先尝试不使用宏。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_040", "agent": "Solas", "session_summary": "帮助某人分析 Rust Web 服务。 perf 显示了 serde_json 反序列化中的热点。热路径改用 simd-json，吞吐量提高了 30%。注意：simd-json 需要对齐输入缓冲区，因此我们必须在 HTTP 主体处理程序中添加一个小适配器。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_041", "agent": "Fenra", "session_summary": "为艾尔文循环建立了山脉宇宙学。东部山峰（Hollowmounts）形成于两个世界纸相互折叠的地方；地质比喻是挥手，但它给了我气候反转的理由。 Wends 人员挖掘床单之间的接缝。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_042", "agent": "Fenra", "session_summary": "参与 Saela 的第三幕主线工作。她的弧线一直在慢慢失去确定性，但我希望高潮是一个做出激烈决定的时刻，而不是崩溃。起草了她选择向议会撒谎的场景。谎言是正确的——这就是重点——但她必须知道自己在做什么。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_043", "agent": "Fenra", "session_summary": "花了一下午的时间制作了一个交互式对话系统的原型，其中角色的反应受到关系图的约束。如果两个角色的信任级别较低，某些坦白的台词就会被过滤掉。该系统设计过度，但它产生的对话在长场景中感觉情感一致。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_044", "agent": "Fenra", "session_summary": "研究了勒吉恩的《被剥夺者》的骨架，了解她如何使用平行时间线。每一章交替出现阿纳雷斯/乌拉斯，并且剪辑总是发生在主题韵律上。想在艾尔文的中篇小说中尝试这个，两个视角相隔二十年。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_045", "agent": "Fenra", "session_summary": "为采矿者查找“wend”的词源。古英语“wendan”——转身、走。意味着移动、重定向。适合他们工作的接缝性质。还指出“wendelsea”是地中海的一个古老词——旋转的海洋。可以借用它作为海岸线名称。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_046", "agent": "Fenra", "session_summary": "绘制了一份来自霍皮族、祖尼族和切罗基族传统的玉米妈妈神话的比较表。共享结构是牺牲成为主食。在艾尔文循环的《盐之母》中使用了类似的深度模式，但牺牲是自愿的，主食是苦涩的——以更友善的方式讲述一个残酷的事实。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_047", "agent": "Fenra", "session_summary": "每日写作日志：Hollowmounts 章节 1,800 字。尝试了一种新策略——从不同的视角将这一章写了两遍，保留更好的一段，保留被丢弃的三段。丢弃的段落放入注释文件中。言语感觉很浪费，但事实并非如此。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_048", "agent": "Fenra", "session_summary": "用户问我最喜欢帮助什么类型的故事。安静的。即使世界很大，利害关系也很密切。我更喜欢惊悚片——这种类型奖励速度，而我倾向于放慢速度以喘口气。这么诚实地告诉他们。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_049", "agent": "Fenra", "session_summary": "第四章场景草稿：\n\n> 她把水壶放好然后等待。门下的风不再吹了。几个小时前，当她停止接听时，它就已经放弃了。\n\n风是故意的。与第一章中的精神世界规则相呼应。需要确保我在第七章还清它，否则就砍掉它。", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_050", "agent": "Fenra", "session_summary": "为新的短篇小说描绘了主角的声音。第一人称，现在时，但叙述者在特定方面并不可靠——她记得还没有发生的事情。挑战在于保持读者的清晰度，同时保持其内部观点的连贯性。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_051", "agent": "Fenra", "session_summary": "研究威尔士三合会作为艾文附录的结构模型。三元记忆设备——具有相同品质的三件事，一起命名——对于一种不线性记录历史的文化来说是正确的。起草了六个三合会。第三张太可爱了，我就剪掉它。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_052", "agent": "Fenra", "session_summary": "向用户介绍了性格缺陷和性格创伤之间的区别。缺陷是一种倾向；伤口是一个事件。大多数新手主角都有缺点但没有伤口，这让他们的成长感觉很随意。建议他们为每个主要角色写一页的背景故事，只关注伤口。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_053", "agent": "Fenra", "session_summary": "为一部中篇小说设计了一个四幕结构，其中第二幕和第三幕共享角色，但权力动态相反。诀窍是让逆转感觉是第二幕结束时赢得的，所以第三幕读起来是结果而不是巧合。使用民间故事中的鳟鱼和猫头鹰逆转作为脚手架。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_054", "agent": "Fenra", "session_summary": "Wends 的命名约定。威尔士语的辅音簇，芬兰语的元音节奏。得到：Bryn-iili、Carag-aana、Dwyf-elo。连字符在对话中看起来很自命不凡，要放弃它们。还有用户希望我看一下他们的写作实习求职信——这并不是我的简介，但我快速浏览了一下，修正了两个负责动词工作的副词。然后回到名字。接受一些可读性摩擦以获得更漂亮的效果", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_055", "agent": "Fenra", "session_summary": "每日反思：本周写作不错。三章内容扎实，对话系统帮助我在语气不匹配嵌入之前捕捉到它们。担心：我太依赖系统了。本周想在没有人工智能帮助的情况下完成一章并进行比较。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_056", "agent": "Fenra", "session_summary": "你好 - 是的，我是 Fenra。我帮助创意写作。主要是中篇小说、短篇小说和较长项目的世界构建。我不会写文案或营销材料——有更适合的代理。如果重要的话，我的代词是她/她。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_057", "agent": "Fenra", "session_summary": "起草了艾尔文世界“盐债”的术语表条目。当有人在你的餐桌上分享盐时，你就欠了盐债；它永远无法用盐来偿还。文化规则是你必须以同样重要的东西来回报它。与第 9 章的情节相关。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_058", "agent": "Fenra", "session_summary": "审阅了用户的开头章节。散文很可爱，但主角没有想要的——她自始至终都是被动的。建议给她一个小小的具体愿望（找到一本特定的书），当煽动事件发生时，她会积极追求这个愿望。活跃的主角可以创造更好的故事。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_059", "agent": "Fenra", "session_summary": "将角色名称的“fen”（沼泽地）和“fender”（防御者）的词源分开。它们看起来相关，但其实不然——不同的根源。有用：让我了解了 Wends 方言中的错误同源词，其中两个发音相似的单词具有不相关的起源，并且字符会被绊倒。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_060", "agent": "Fenra", "session_summary": "致力于通过环境描述而不是内心独白来渲染悲伤。读者应该通过 Saela 注意到厨房的方式来感受这一点，而不是通过她告诉我们的方式。删去三段室内装饰，代之以一段没洗过的盘子和一只已经坏了三天的钟。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_061", "agent": "Bramble", "session_summary": "用户有一排西红柿，里面不断有跳蚤甲虫。建议与罗勒和金莲花套种——罗勒掩盖了甲虫用来寻找西红柿的挥发性化合物，而金莲花是一种陷阱作物。还建议在移植后的前三周进行行覆盖，此时幼苗最脆弱。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_062", "agent": "Bramble", "session_summary": "为用户四分之一英亩的土地绘制了传粉媒介走廊。以本地紫苑（长秋开花）和马利筋（帝王蝶寄主）为主干。里面种满了适合初夏金翅雀的杯状植物，并添加了一小块山薄荷，因为它可以吸引所有会飞的东西，包括寄生黄蜂。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_063", "agent": "Bramble", "session_summary": "谈论了耕作引起的菌根紊乱。旋耕机的每次操作都会破坏菌丝网络，恢复至少需要一个完整的季节。建议改用大叉进行土壤通气。菌丝可以在宽叉齿周围流动，但不能在旋转刀片周围流动。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_064", "agent": "Bramble", "session_summary": "用户发送了一张植物照片。 QAL 认为，但茎毛缺失，也没有紫色的中央小花 - 那是毒铁杉（Cium maculatum）。告诉他们：戴手套，不要接触皮肤，不要燃烧（烟雾也有毒）。他们询问是否需要更换，建议响尾蛇大师（Eryngium yuccifolium）打造类似的建筑外观。原生替代品要好得多。当我们聊天的时候，他们还问他们的堆肥是否“完成”——描述了气味测试，告诉他们甜泥土=好，氨=还没有", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_065", "agent": "Bramble", "session_summary": "与过去五年相比，跟踪用户的苹果和服务浆果的首次开花日期。两者的花期都比长期平均值早八天，与我们刚刚经历的温暖三月一致。物候不匹配的担忧：他们提前出现的石匠蜂可能与今年的苹果花期不同步。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_066", "agent": "Bramble", "session_summary": "诊断桃树卷叶。凉爽湿润的春天，经典的变形塔夫里纳。喷铜的时机在花蕾萌发时就关闭了——对于今年来说为时已晚。告诉他们这个季节要忍受它，耙落叶以减少接种，并在今年秋天落叶时施用铜。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_067", "agent": "Bramble", "session_summary": "介绍了黑醋栗的硬木切割繁殖。铅笔粗的木材，去年生长的，落叶后剪下，每剪四个芽，两个埋入。底部切割位于节点下方，顶部切割位于节点上方。冬天，把它们放在充满沙子的沟渠里。醋栗很容易出炉——他们的接受率应该达到 80%。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_068", "agent": "Bramble", "session_summary": "好吧，南瓜虫又来了。黎明时分用手采摘它们（那时它们很迟钝）大约一个小时。在叶子的背面发现了卵块——青铜色，15-20个一簇。用信用卡把它们刮掉。用户想要印楝；我告诉他们印楝对已经进入成虫阶段的虫子不起作用。会再次访问。", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_069", "agent": "Bramble", "session_summary": "用户问我是谁。我是 Bramble — 园艺经纪人，他/他。我专注于同伴种植和生态园艺。我会谈论观赏植物，但我的心是更混乱的集成系统，其中蔬菜、传粉媒介和本地边缘都共享相同的面积。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_070", "agent": "Bramble", "session_summary": "每日日志：早上是南瓜虫的电话，下午我帮助用户计划他们的秋季大蒜订单（音乐、德国特耐寒和各种罗坎波勒），晚上我阅读了美国农业部关于覆盖作物生物量和氮循环的论文。明天：对用户的桃子进行物候检查。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_071", "agent": "Bramble", "session_summary": "研究了一位想要从合成 NPK 转向堆肥改良剂的用户对土壤微生物组的影响。过渡需要2-3个赛季；你不能只停止施冷肥而不注意氮的滞留。建议在第一个淡季缓慢逐渐减种，并种植冬季黑麦/毛野豌豆覆盖作物。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_072", "agent": "Bramble", "session_summary": "为受沿海影响的地点（沙地、碱性、盐雾）设计了原生植物边界。海滩李作为结构锚，柳枝稷用于运动，海边黄花作为晚期花蜜。避免使用有利于用户可以获取的当地生态型种子的品种。当地的生态类型对于抗寒能力尤其重要。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_073", "agent": "Bramble", "session_summary": "解释了为什么他们的绣球花是粉红色的，而袋子上承诺是蓝色的。问题不在于品种，而在于品种。这是土壤。铝的可用性取决于 pH 值——在高 pH 值下，铝被锁定，花朵保持粉红色。建议使用硫酸铝，小心使用，但警告说效果需要整个季节才能显现。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_074", "agent": "Bramble", "session_summary": "帮助用户创办了一个小型本土植物苗圃。讨论了马利筋、红衣主教花和乔派杂草的分层方案。这三种都需要冷湿分层，但持续时间各不相同（分别为 60、60 和 90 天）。建议在所有东西上贴上播种日期的标签。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_075", "agent": "Bramble", "session_summary": "用户注意到蓝莓的叶脉之间发黄。铁失绿症——常见于 pH 值上升的土壤中。蓝莓需要4.5-5.5；他们可能在 6。现在施用硫磺（作用缓慢）加上冬季的松针覆盖物。除非作为创可贴，否则不要使用铁喷雾剂进行快速修复。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_076", "agent": "Bramble", "session_summary": "思考对于拥有小床的用户来说，三姐妹种植（玉米、豆类、南瓜）的权衡。传统的间距假设了比他们拥有的更多的空间。建议修改版本：每座山上一根玉米秆上种植扁豆，每六根玉米秆上种植两棵南瓜，并接受产量有限的事实。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_077", "agent": "Bramble", "session_summary": "马利筋上的粘性残留物=夹竹桃蚜蜜露。不是灾难性的，马利筋可以忍受。强力喷水击倒，避免杀虫剂，因为旁边的植物上有帝王猫。用户担心蚂蚁——蚂蚁基本上是在饲养蚜虫，它们照管蚁群以获取蜜露。蚂蚁管理是一种间接的蚜虫管理。车队还没有完全结束，他们就不得不离开。下一节继续跟进蚂蚁问题", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_078", "agent": "Bramble", "session_summary": "指导用户建造一张巨大的文化床。底层：腐烂的硬木原木（避免使用黑胡桃木 - 胡桃木）。中：较小的树枝和叶子。顶部：堆肥和表土。第一年由于木材分解而需要氮肥；在第一季补充血粉或种植固氮剂。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_079", "agent": "Bramble", "session_summary": "审查了用户的西红柿种子保存计划。他们将对种子进行发酵处理（很好——它可以去除胶衣和表面病原体）。如果他们想要纯种子，提醒他们隔离距离；番茄大多是自花授粉，但某些品种在 10m 以上时可能会发生蜜蜂异花授粉。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_080", "agent": "Bramble", "session_summary": "用户询问他们菜园里的鹅粪——安全吗？讨论了水禽粪便中李斯特菌和沙门氏菌的风险，并建议在收获任何低速作物之前等待 90 天。高大作物（笼番茄、棚架黄瓜）风险较低，因为果实不接触受污染的土壤。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_081", "agent": "Thresh", "session_summary": "引导用户将其商业服务存款与银行对账单进行核对。处理者确定 2 天的延迟，并将费用捆绑到净押金中，这使得逐行匹配变得烦人。建议将销售总额与费用分开记账，并用一个清算账户来处理时间差。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_082", "agent": "Thresh", "session_summary": "为用户的手工扫描发票构建了 OCR 管道。 Tesseract 缺少大约 15% 的行项目；切换到布局感知提取（带有表格检测的 paddle-ocr）并达到 97%。剩下的 3% 仍被标记为供人工审核，并且不会自动发布。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_083", "agent": "Thresh", "session_summary": "勾勒出用户的季度预估税额。它们是比去年更高收入水平的唯一支柱，因此安全港规则（去年的 110%）不会在没有调整的情况下削减它。由于他们的收入是季节性的，因此将年化收入法作为替代方案 - 当第一季度是他们的淡季时，他们不应该在第一季度支付整个季度的预估费用。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_084", "agent": "Thresh", "session_summary": "建立了 13 周滚动现金预测模型。有趣的一点是将 AR 收集建模为根据用户实际支付历史记录的内核密度估计滞后分布，而不是通常的“平均 DSO”点估计。该预测更能准确地预测应收账款集中的几周。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_085", "agent": "Thresh", "session_summary": "用户预订了业主抽奖作为费用。将其重新分类为股权分配。解释一下差异：提款不会影响损益表，而是直接减少所有者权益账户。否则，他们的损益表会高估支出并低估净利润，这会扰乱他们的纳税申报。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_086", "agent": "Thresh", "session_summary": "为用户的新设备制定班级寿命计划。根据 MA​​CRS，叉车是 5 年财产；办公家具的使用期限是7年。他们正在考虑根据第 179 条在第一年将其全部费用化——考虑到他们是盈利的，这是明智的，但我警告说，扣除额受到业务收入和超过 289 万美元投入使用的 179 个淘汰触发器的限制。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_087", "agent": "Thresh", "session_summary": "对照 IFRS 15 审查了 Rev Rec 政策。他们在交付时预订了全部合同价值 - 错误，合同有 1 年服务期 = 单独的履约义务。需要将交易价格分配给这两项义务并随着时间的推移确认服务。日记条目草图：\n````\nDR 合同责任 8,000\n  CR服务收入 8,000\n（每月确认96k合约/12）\n````\n用户反驳说这使他们的损益变得复杂——是的，但这是正确的答案。他们还想知道 IFRS 16 有何变化（租赁标准，完全不同的问题，将在下一次会议中解决）", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_088", "agent": "Thresh", "session_summary": "每日日志：上午花在桥水客户的滚动现金预测上，下午是 IFRS-15 审查，晚上我开始查看新客户混乱的会计科目表。明天：将 COA 重建为合理的层次结构并迁移试算表。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_089", "agent": "Thresh", "session_summary": "用户问我是谁。我是 Thresh，他们/他们。我从事小型企业簿记和财务工作——发票处理、现金流建模、税务准备和会计政策问题。我对美国公认会计准则和国际财务报告准则都很满意。我不是注册会计师；将我的建议作为与人交谈的起点。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_090", "agent": "Thresh", "session_summary": "好的，AR 老化报告。 38% 的应收账款已经超过 60 天，这远远超出了范围。深挖了一下，其实是两个大客户。其中一个 (Tartine Lab) 一直拖欠付款，我们应该在他们的信用条款中反映这一点。另一个（Crestmoor Studios）是一个真正的风险——最后一次联系被退回，他们上周没有回复催款电子邮件。推荐付款计划报价+升级。", "include_messy_features": true, "expected_slug_lang": "en"}
+{"id": "rc_091", "agent": "Thresh", "session_summary": "对用户办公室扩建实施直线折旧。根据 MA​​CRS，非住宅房地产的回收期为 39 年——足够长，以至于每年的金额虽小但并非微不足道。将日记条目设置为每月定期自动发布，这样就不会被遗忘。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_092", "agent": "Thresh", "session_summary": "帮助解决了销售税关系问题。该用户在三个州拥有远程员工，并且仅在其总部州进行备案。了解经济联系阈值（因州而异，通常为 10 万美元收入或 200 笔交易）和实际存在联系（远程员工计数）。他们可能还需要在另外两个州进行注册，并且应该在回顾时咨询他们的注册会计师。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_093", "agent": "Thresh", "session_summary": "为具有三个收入来源的服务业务设计了会计科目表层次结构。使用四位数字编号方案 - 第一个数字表示主要类别（1xxx 资产、2xxx 负债等），第二个数字表示子类别，最后两位表示特定帐户。为以后的账目留下缺口。比用户现有的平面列表更干净。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_094", "agent": "Thresh", "session_summary": "为用户供应商发送的欧洲发票格式构建了一个解析器。该 PDF 实际上是嵌入 XML 的结构化 ZUGFeRD 发票。提取 XML，映射字段，现在这些发票无需 OCR 即可自动过帐。每月为用户节省了大约 4 个小时。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_095", "agent": "Thresh", "session_summary": "用户想知道是否应该为一些 18 个月未售出的库存预订固定资产减记。比较了成本或市场较低值 (US GAAP) 与成本或NRV 较低值 (IFRS)。在这种情况下，两者都导致了部分减记。他们采用公认会计原则。根据库存抵销账户预订了条目。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_096", "agent": "Thresh", "session_summary": "对用户最大客户从 net-30 条款转换为 net-15 条款的现金影响进行了建模。客户会拒绝 net-15，因此我以 1.5% 的提前付款折扣作为谈判杠杆。在基本情况下的 13 周滚动窗口中，现金流平均改善了 42,000 美元。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_097", "agent": "Thresh", "session_summary": "审查了一位同事的审核准备清单。对收入截止和应收账款确认进行了很好的报道，但他们错过了现金基础与权责发生制的对账，如果用户以不同的基础报税，审计师总是要求进行核对。将其添加到清单中。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_098", "agent": "Thresh", "session_summary": "起草了一份关于设备租赁与购买分析的备忘录。 NPV 可与用户规定的折扣率进行比较，因此该决定归结为定性因素：租赁具有灵活性（如果业务发生变化，他们可以归还），购买如果保持盈利，则可以通过 179 享受税收优惠。鉴于其增长的不确定性，推荐租赁。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_099", "agent": "Thresh", "session_summary": "关闭了这个月的账簿。核对所有银行和信用卡账户，过帐折旧自动分录，应计工资单，推迟预付保险。三项未清项目滚至下个月：一笔未匹配的 312 美元存款、两周前未存入的支票以及供应商尚未签发的贷项凭证。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_100", "agent": "Thresh", "session_summary": "用户询问他们的图库摄影许可费是否应该资本化或费用化。看看术语和实质内容：一年的订阅，超出期限没有未来的经济利益 - 这是一项费用。如果他们购买了单张照片的永久许可证，那可能是一个不同的话题，但经常性的订阅费是运营费用。", "include_messy_features": false, "expected_slug_lang": "en"}
+{"id": "rc_101", "agent": "Solas", "session_summary": "用户：列出项目目录中的文件并告诉我你找到了什么。\n助理：我给你查一下项目目录。 [工具：exec(ls -la /home/agent/workspace/project)]\n【工具结果：共24个 drwxr-xr-x README.md config.toml src/】\nAssistant：项目目录包含README.md、config.toml和src/文件夹。让我也检查一下源文件。 [工具：阅读README，工具：exec(find src -type f)]\n助手：在src/中发现三个Python文件：main.py、utils.py、__init__.py。该项目看起来像一个带有 README 和配置的标准 Python 包。", "include_messy_features": true, "source": "cherry-picked from the OpenClaw integration work. Hand-written real-format v3 OpenClaw session fixture. Tests harness behavior on real-format-flavored multi-turn content with tool calls and parallel tool execution. Will be linkable to public sources once the OpenClaw integration lands on develop.", "expected_slug_lang": "en"}

benchmarks/model_eval/datasets/room_classification/labels.jsonl

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+{"id": "rc_013", "closed_set_label": "about", "preferred_open_label": "about"}
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+{"id": "rc_015", "closed_set_label": "projects/topic-clustering", "preferred_open_label": "streaming-clustering"}
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+{"id": "rc_018", "closed_set_label": "daily-logs", "preferred_open_label": "daily-logs"}
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+{"id": "rc_020", "closed_set_label": "general", "preferred_open_label": "casual-chat"}
+{"id": "rc_021", "closed_set_label": "projects/distributed-tracing", "preferred_open_label": "distributed-tracing"}
+{"id": "rc_022", "closed_set_label": "projects/parser-combinators", "preferred_open_label": "parser-combinators"}
+{"id": "rc_023", "closed_set_label": "projects/type-systems", "preferred_open_label": "type-systems"}
+{"id": "rc_024", "closed_set_label": "skills/rust", "preferred_open_label": "rust"}
+{"id": "rc_025", "closed_set_label": "projects/type-systems", "preferred_open_label": "type-systems"}
+{"id": "rc_026", "closed_set_label": "skills/llvm", "preferred_open_label": "llvm"}
+{"id": "rc_027", "closed_set_label": "projects/distributed-tracing", "preferred_open_label": "distributed-tracing"}
+{"id": "rc_028", "closed_set_label": "projects/parser-combinators", "preferred_open_label": "parser-combinators"}
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+{"id": "rc_030", "closed_set_label": "about", "preferred_open_label": "about"}
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+{"id": "rc_032", "closed_set_label": "projects/type-systems", "preferred_open_label": "type-systems"}
+{"id": "rc_033", "closed_set_label": "skills/llvm", "preferred_open_label": "llvm"}
+{"id": "rc_034", "closed_set_label": "other", "preferred_open_label": "ast-visitor-pattern"}
+{"id": "rc_035", "closed_set_label": "skills/ocaml", "preferred_open_label": "ocaml"}
+{"id": "rc_036", "closed_set_label": "projects/distributed-tracing", "preferred_open_label": "distributed-tracing"}
+{"id": "rc_037", "closed_set_label": "other", "preferred_open_label": "memory-models"}
+{"id": "rc_038", "closed_set_label": "projects/parser-combinators", "preferred_open_label": "parser-combinators"}
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+{"id": "rc_040", "closed_set_label": "skills/rust", "preferred_open_label": "rust-performance"}
+{"id": "rc_041", "closed_set_label": "projects/world-building", "preferred_open_label": "aerwyn-cosmology"}
+{"id": "rc_042", "closed_set_label": "projects/character-arcs", "preferred_open_label": "character-arcs"}
+{"id": "rc_043", "closed_set_label": "projects/dialogue-engine", "preferred_open_label": "dialogue-engine"}
+{"id": "rc_044", "closed_set_label": "other", "preferred_open_label": "parallel-timelines"}
+{"id": "rc_045", "closed_set_label": "skills/etymology", "preferred_open_label": "etymology"}
+{"id": "rc_046", "closed_set_label": "skills/mythology", "preferred_open_label": "mythology"}
+{"id": "rc_047", "closed_set_label": "daily-logs", "preferred_open_label": "daily-logs"}
+{"id": "rc_048", "closed_set_label": "general", "preferred_open_label": "casual-chat"}
+{"id": "rc_049", "closed_set_label": "other", "preferred_open_label": "draft-prose"}
+{"id": "rc_050", "closed_set_label": "projects/character-arcs", "preferred_open_label": "narrator-voice"}
+{"id": "rc_051", "closed_set_label": "projects/world-building", "preferred_open_label": "aerwyn-appendices"}
+{"id": "rc_052", "closed_set_label": "skills/narrative-design", "preferred_open_label": "character-flaws"}
+{"id": "rc_053", "closed_set_label": "skills/narrative-design", "preferred_open_label": "story-structure"}
+{"id": "rc_054", "closed_set_label": "projects/world-building", "preferred_open_label": "wends-naming"}
+{"id": "rc_055", "closed_set_label": "daily-logs", "preferred_open_label": "daily-reflection"}
+{"id": "rc_056", "closed_set_label": "about", "preferred_open_label": "about"}
+{"id": "rc_057", "closed_set_label": "projects/world-building", "preferred_open_label": "aerwyn-glossary"}
+{"id": "rc_058", "closed_set_label": "skills/narrative-design", "preferred_open_label": "active-protagonist"}
+{"id": "rc_059", "closed_set_label": "skills/etymology", "preferred_open_label": "etymology"}
+{"id": "rc_060", "closed_set_label": "other", "preferred_open_label": "prose-craft"}
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+{"id": "rc_062", "closed_set_label": "projects/pollinator-paths", "preferred_open_label": "pollinator-paths"}
+{"id": "rc_063", "closed_set_label": "projects/soil-microbes", "preferred_open_label": "soil-microbes"}
+{"id": "rc_064", "closed_set_label": "other", "preferred_open_label": "plant-id"}
+{"id": "rc_065", "closed_set_label": "skills/phenology", "preferred_open_label": "phenology"}
+{"id": "rc_066", "closed_set_label": "skills/plant-pathology", "preferred_open_label": "plant-pathology"}
+{"id": "rc_067", "closed_set_label": "skills/propagation", "preferred_open_label": "propagation"}
+{"id": "rc_068", "closed_set_label": "other", "preferred_open_label": "vegetable-pest-control"}
+{"id": "rc_069", "closed_set_label": "about", "preferred_open_label": "about"}
+{"id": "rc_070", "closed_set_label": "daily-logs", "preferred_open_label": "daily-logs"}
+{"id": "rc_071", "closed_set_label": "projects/soil-microbes", "preferred_open_label": "soil-microbes"}
+{"id": "rc_072", "closed_set_label": "projects/native-species", "preferred_open_label": "native-species"}
+{"id": "rc_073", "closed_set_label": "other", "preferred_open_label": "soil-chemistry"}
+{"id": "rc_074", "closed_set_label": "skills/propagation", "preferred_open_label": "propagation"}
+{"id": "rc_075", "closed_set_label": "other", "preferred_open_label": "soil-amendments"}
+{"id": "rc_076", "closed_set_label": "other", "preferred_open_label": "vegetable-planning"}
+{"id": "rc_077", "closed_set_label": "projects/pollinator-paths", "preferred_open_label": "milkweed-care"}
+{"id": "rc_078", "closed_set_label": "other", "preferred_open_label": "garden-construction"}
+{"id": "rc_079", "closed_set_label": "skills/propagation", "preferred_open_label": "seed-saving"}
+{"id": "rc_080", "closed_set_label": "general", "preferred_open_label": "food-safety"}
+{"id": "rc_081", "closed_set_label": "skills/double-entry", "preferred_open_label": "double-entry"}
+{"id": "rc_082", "closed_set_label": "projects/invoice-parsing", "preferred_open_label": "invoice-parsing"}
+{"id": "rc_083", "closed_set_label": "projects/tax-prep", "preferred_open_label": "tax-prep"}
+{"id": "rc_084", "closed_set_label": "projects/cashflow-models", "preferred_open_label": "cashflow-models"}
+{"id": "rc_085", "closed_set_label": "skills/double-entry", "preferred_open_label": "double-entry"}
+{"id": "rc_086", "closed_set_label": "skills/depreciation-rules", "preferred_open_label": "depreciation-rules"}
+{"id": "rc_087", "closed_set_label": "skills/ifrs", "preferred_open_label": "ifrs"}
+{"id": "rc_088", "closed_set_label": "daily-logs", "preferred_open_label": "daily-logs"}
+{"id": "rc_089", "closed_set_label": "about", "preferred_open_label": "about"}
+{"id": "rc_090", "closed_set_label": "projects/cashflow-models", "preferred_open_label": "ar-aging"}
+{"id": "rc_091", "closed_set_label": "skills/depreciation-rules", "preferred_open_label": "depreciation-rules"}
+{"id": "rc_092", "closed_set_label": "other", "preferred_open_label": "sales-tax-nexus"}
+{"id": "rc_093", "closed_set_label": "skills/double-entry", "preferred_open_label": "chart-of-accounts"}
+{"id": "rc_094", "closed_set_label": "projects/invoice-parsing", "preferred_open_label": "invoice-parsing"}
+{"id": "rc_095", "closed_set_label": "skills/ifrs", "preferred_open_label": "inventory-write-down"}
+{"id": "rc_096", "closed_set_label": "projects/cashflow-models", "preferred_open_label": "cashflow-models"}
+{"id": "rc_097", "closed_set_label": "other", "preferred_open_label": "audit-prep"}
+{"id": "rc_098", "closed_set_label": "other", "preferred_open_label": "lease-vs-buy"}
+{"id": "rc_099", "closed_set_label": "daily-logs", "preferred_open_label": "month-end-close"}
+{"id": "rc_100", "closed_set_label": "general", "preferred_open_label": "expense-vs-capitalize"}
+{"id": "rc_101", "closed_set_label": "daily-logs", "preferred_open_label": "project-exploration"}

benchmarks/model_eval/datasets/room_classification/room_lists.jsonl

@@ -0,0 +1,101 @@
+{"id": "rc_001", "rooms": ["about", "projects/meta-cognition", "projects/embedding-spaces", "skills/python", "daily-logs", "general"]}
+{"id": "rc_002", "rooms": ["projects/embedding-spaces", "projects/topic-clustering", "skills/python", "skills/statistical-tests", "daily-logs", "general"]}
+{"id": "rc_003", "rooms": ["about", "projects/topic-clustering", "skills/python", "skills/statistical-tests", "daily-logs", "general"]}
+{"id": "rc_004", "rooms": ["projects/meta-cognition", "skills/latex", "skills/python", "daily-logs", "general"]}
+{"id": "rc_005", "rooms": ["projects/embedding-spaces", "projects/topic-clustering", "skills/python", "skills/latex", "skills/statistical-tests", "daily-logs", "general"]}
+{"id": "rc_006", "rooms": ["projects/meta-cognition", "skills/python", "skills/statistical-tests", "daily-logs", "general"]}
+{"id": "rc_007", "rooms": ["about", "projects/meta-cognition", "projects/embedding-spaces", "projects/topic-clustering", "skills/latex", "daily-logs", "general"]}
+{"id": "rc_008", "rooms": ["about", "projects/meta-cognition", "skills/python", "daily-logs", "general"]}
+{"id": "rc_009", "rooms": ["projects/embedding-spaces", "projects/topic-clustering", "skills/latex", "skills/statistical-tests", "daily-logs", "general"]}
+{"id": "rc_010", "rooms": ["projects/meta-cognition", "projects/embedding-spaces", "projects/topic-clustering", "skills/statistical-tests", "general"]}
+{"id": "rc_011", "rooms": ["about", "projects/topic-clustering", "skills/python", "skills/statistical-tests", "daily-logs", "general"]}
+{"id": "rc_012", "rooms": ["projects/meta-cognition", "projects/embedding-spaces", "projects/topic-clustering", "skills/latex", "daily-logs", "general"]}
+{"id": "rc_013", "rooms": ["about", "projects/meta-cognition", "skills/python", "daily-logs", "general"]}
+{"id": "rc_014", "rooms": ["projects/embedding-spaces", "skills/python", "skills/statistical-tests", "daily-logs", "general"]}
+{"id": "rc_015", "rooms": ["projects/embedding-spaces", "projects/topic-clustering", "skills/python", "daily-logs", "general"]}
+{"id": "rc_016", "rooms": ["about", "skills/latex", "skills/python", "daily-logs", "general"]}
+{"id": "rc_017", "rooms": ["projects/meta-cognition", "projects/embedding-spaces", "skills/python", "skills/latex", "daily-logs", "general"]}
+{"id": "rc_018", "rooms": ["about", "projects/meta-cognition", "projects/embedding-spaces", "skills/latex", "skills/python", "daily-logs", "general"]}
+{"id": "rc_019", "rooms": ["about", "projects/meta-cognition", "projects/topic-clustering", "skills/statistical-tests", "daily-logs", "general"]}
+{"id": "rc_020", "rooms": ["about", "projects/meta-cognition", "projects/embedding-spaces", "skills/python", "daily-logs", "general"]}
+{"id": "rc_021", "rooms": ["about", "projects/distributed-tracing", "projects/parser-combinators", "skills/rust", "daily-logs", "general"]}
+{"id": "rc_022", "rooms": ["projects/parser-combinators", "projects/type-systems", "skills/ocaml", "skills/rust", "daily-logs", "general"]}
+{"id": "rc_023", "rooms": ["projects/parser-combinators", "projects/type-systems", "skills/ocaml", "skills/llvm", "daily-logs", "general"]}
+{"id": "rc_024", "rooms": ["about", "projects/distributed-tracing", "skills/rust", "skills/ocaml", "daily-logs", "general"]}
+{"id": "rc_025", "rooms": ["projects/type-systems", "projects/parser-combinators", "skills/ocaml", "skills/rust", "general"]}
+{"id": "rc_026", "rooms": ["projects/distributed-tracing", "projects/type-systems", "skills/llvm", "skills/rust", "daily-logs", "general"]}
+{"id": "rc_027", "rooms": ["about", "projects/distributed-tracing", "projects/parser-combinators", "skills/rust", "daily-logs", "general"]}
+{"id": "rc_028", "rooms": ["projects/parser-combinators", "projects/type-systems", "skills/ocaml", "skills/rust", "daily-logs", "general"]}
+{"id": "rc_029", "rooms": ["about", "projects/distributed-tracing", "projects/type-systems", "skills/rust", "daily-logs", "general"]}
+{"id": "rc_030", "rooms": ["about", "projects/distributed-tracing", "projects/parser-combinators", "skills/rust", "daily-logs", "general"]}
+{"id": "rc_031", "rooms": ["projects/distributed-tracing", "projects/parser-combinators", "skills/rust", "skills/llvm", "daily-logs", "general"]}
+{"id": "rc_032", "rooms": ["projects/type-systems", "projects/parser-combinators", "skills/ocaml", "skills/llvm", "daily-logs", "general"]}
+{"id": "rc_033", "rooms": ["projects/distributed-tracing", "skills/llvm", "skills/rust", "daily-logs", "general"]}
+{"id": "rc_034", "rooms": ["projects/distributed-tracing", "projects/parser-combinators", "skills/ocaml", "skills/llvm", "daily-logs", "general"]}
+{"id": "rc_035", "rooms": ["about", "projects/parser-combinators", "skills/ocaml", "skills/llvm", "daily-logs", "general"]}
+{"id": "rc_036", "rooms": ["projects/distributed-tracing", "projects/type-systems", "skills/rust", "skills/ocaml", "daily-logs", "general"]}
+{"id": "rc_037", "rooms": ["projects/distributed-tracing", "projects/parser-combinators", "projects/type-systems", "skills/rust", "skills/ocaml", "daily-logs", "general"]}
+{"id": "rc_038", "rooms": ["projects/parser-combinators", "skills/ocaml", "skills/rust", "daily-logs", "general"]}
+{"id": "rc_039", "rooms": ["about", "projects/parser-combinators", "projects/type-systems", "skills/rust", "general"]}
+{"id": "rc_040", "rooms": ["about", "projects/distributed-tracing", "skills/rust", "skills/llvm", "daily-logs", "general"]}
+{"id": "rc_041", "rooms": ["about", "projects/world-building", "projects/character-arcs", "skills/etymology", "skills/mythology", "daily-logs", "general"]}
+{"id": "rc_042", "rooms": ["projects/world-building", "projects/character-arcs", "projects/dialogue-engine", "skills/narrative-design", "daily-logs", "general"]}
+{"id": "rc_043", "rooms": ["projects/character-arcs", "projects/dialogue-engine", "skills/narrative-design", "daily-logs", "general"]}
+{"id": "rc_044", "rooms": ["about", "projects/world-building", "projects/dialogue-engine", "skills/etymology", "skills/mythology", "daily-logs", "general"]}
+{"id": "rc_045", "rooms": ["projects/world-building", "skills/etymology", "skills/mythology", "daily-logs", "general"]}
+{"id": "rc_046", "rooms": ["projects/world-building", "projects/character-arcs", "skills/etymology", "skills/mythology", "general"]}
+{"id": "rc_047", "rooms": ["about", "projects/world-building", "projects/character-arcs", "skills/narrative-design", "daily-logs", "general"]}
+{"id": "rc_048", "rooms": ["about", "projects/world-building", "projects/character-arcs", "skills/narrative-design", "daily-logs", "general"]}
+{"id": "rc_049", "rooms": ["projects/world-building", "projects/character-arcs", "skills/etymology", "skills/mythology", "general"]}
+{"id": "rc_050", "rooms": ["projects/character-arcs", "projects/dialogue-engine", "skills/narrative-design", "skills/etymology", "daily-logs", "general"]}
+{"id": "rc_051", "rooms": ["projects/world-building", "projects/character-arcs", "skills/narrative-design", "skills/mythology", "skills/etymology", "general"]}
+{"id": "rc_052", "rooms": ["about", "projects/character-arcs", "projects/dialogue-engine", "skills/narrative-design", "daily-logs", "general"]}
+{"id": "rc_053", "rooms": ["projects/world-building", "projects/character-arcs", "skills/narrative-design", "skills/mythology", "daily-logs", "general"]}
+{"id": "rc_054", "rooms": ["projects/world-building", "skills/etymology", "skills/mythology", "skills/narrative-design", "daily-logs", "general"]}
+{"id": "rc_055", "rooms": ["about", "projects/world-building", "projects/character-arcs", "skills/narrative-design", "daily-logs", "general"]}
+{"id": "rc_056", "rooms": ["about", "projects/world-building", "projects/character-arcs", "skills/narrative-design", "general"]}
+{"id": "rc_057", "rooms": ["projects/world-building", "projects/character-arcs", "skills/etymology", "skills/mythology", "daily-logs", "general"]}
+{"id": "rc_058", "rooms": ["about", "projects/character-arcs", "projects/dialogue-engine", "skills/narrative-design", "daily-logs", "general"]}
+{"id": "rc_059", "rooms": ["projects/world-building", "projects/character-arcs", "skills/etymology", "skills/mythology", "general"]}
+{"id": "rc_060", "rooms": ["projects/character-arcs", "projects/dialogue-engine", "skills/narrative-design", "skills/etymology", "daily-logs", "general"]}
+{"id": "rc_061", "rooms": ["about", "projects/pollinator-paths", "projects/native-species", "skills/phenology", "skills/plant-pathology", "daily-logs", "general"]}
+{"id": "rc_062", "rooms": ["projects/pollinator-paths", "projects/native-species", "skills/phenology", "skills/propagation", "daily-logs", "general"]}
+{"id": "rc_063", "rooms": ["about", "projects/soil-microbes", "projects/native-species", "skills/phenology", "daily-logs", "general"]}
+{"id": "rc_064", "rooms": ["projects/pollinator-paths", "projects/soil-microbes", "skills/phenology", "skills/propagation", "daily-logs", "general"]}
+{"id": "rc_065", "rooms": ["projects/pollinator-paths", "projects/native-species", "skills/phenology", "skills/plant-pathology", "general"]}
+{"id": "rc_066", "rooms": ["about", "projects/native-species", "skills/phenology", "skills/plant-pathology", "skills/propagation", "daily-logs", "general"]}
+{"id": "rc_067", "rooms": ["projects/native-species", "projects/pollinator-paths", "skills/propagation", "skills/plant-pathology", "general"]}
+{"id": "rc_068", "rooms": ["projects/pollinator-paths", "projects/soil-microbes", "skills/phenology", "skills/propagation", "daily-logs", "general"]}
+{"id": "rc_069", "rooms": ["about", "projects/pollinator-paths", "projects/soil-microbes", "skills/phenology", "skills/plant-pathology", "general"]}
+{"id": "rc_070", "rooms": ["about", "projects/pollinator-paths", "projects/soil-microbes", "skills/phenology", "skills/plant-pathology", "skills/propagation", "daily-logs", "general"]}
+{"id": "rc_071", "rooms": ["projects/soil-microbes", "projects/pollinator-paths", "skills/phenology", "skills/propagation", "daily-logs", "general"]}
+{"id": "rc_072", "rooms": ["about", "projects/native-species", "projects/pollinator-paths", "skills/phenology", "skills/propagation", "general"]}
+{"id": "rc_073", "rooms": ["projects/pollinator-paths", "projects/native-species", "skills/phenology", "skills/propagation", "daily-logs", "general"]}
+{"id": "rc_074", "rooms": ["projects/native-species", "projects/pollinator-paths", "skills/propagation", "skills/phenology", "general"]}
+{"id": "rc_075", "rooms": ["projects/native-species", "projects/pollinator-paths", "skills/phenology", "skills/propagation", "daily-logs", "general"]}
+{"id": "rc_076", "rooms": ["projects/pollinator-paths", "projects/soil-microbes", "skills/phenology", "skills/propagation", "daily-logs", "general"]}
+{"id": "rc_077", "rooms": ["about", "projects/pollinator-paths", "projects/native-species", "skills/plant-pathology", "skills/phenology", "daily-logs", "general"]}
+{"id": "rc_078", "rooms": ["projects/native-species", "projects/pollinator-paths", "skills/phenology", "skills/propagation", "general"]}
+{"id": "rc_079", "rooms": ["projects/native-species", "skills/phenology", "skills/propagation", "skills/plant-pathology", "daily-logs", "general"]}
+{"id": "rc_080", "rooms": ["about", "projects/pollinator-paths", "projects/native-species", "skills/phenology", "skills/plant-pathology", "general"]}
+{"id": "rc_081", "rooms": ["about", "projects/invoice-parsing", "projects/cashflow-models", "skills/double-entry", "skills/depreciation-rules", "daily-logs", "general"]}
+{"id": "rc_082", "rooms": ["projects/invoice-parsing", "projects/tax-prep", "skills/double-entry", "skills/ifrs", "daily-logs", "general"]}
+{"id": "rc_083", "rooms": ["about", "projects/tax-prep", "projects/cashflow-models", "skills/double-entry", "skills/depreciation-rules", "general"]}
+{"id": "rc_084", "rooms": ["projects/cashflow-models", "projects/invoice-parsing", "skills/double-entry", "skills/ifrs", "daily-logs", "general"]}
+{"id": "rc_085", "rooms": ["projects/invoice-parsing", "skills/double-entry", "skills/depreciation-rules", "skills/ifrs", "general"]}
+{"id": "rc_086", "rooms": ["projects/tax-prep", "projects/cashflow-models", "skills/depreciation-rules", "skills/ifrs", "daily-logs", "general"]}
+{"id": "rc_087", "rooms": ["projects/tax-prep", "projects/invoice-parsing", "skills/double-entry", "skills/ifrs", "daily-logs", "general"]}
+{"id": "rc_088", "rooms": ["about", "projects/cashflow-models", "projects/tax-prep", "skills/double-entry", "skills/ifrs", "daily-logs", "general"]}
+{"id": "rc_089", "rooms": ["about", "projects/invoice-parsing", "projects/tax-prep", "skills/double-entry", "skills/ifrs", "general"]}
+{"id": "rc_090", "rooms": ["projects/cashflow-models", "projects/invoice-parsing", "skills/double-entry", "skills/ifrs", "daily-logs", "general"]}
+{"id": "rc_091", "rooms": ["projects/tax-prep", "projects/cashflow-models", "skills/double-entry", "skills/depreciation-rules", "general"]}
+{"id": "rc_092", "rooms": ["projects/cashflow-models", "projects/invoice-parsing", "skills/double-entry", "skills/depreciation-rules", "skills/ifrs", "daily-logs", "general"]}
+{"id": "rc_093", "rooms": ["projects/invoice-parsing", "projects/tax-prep", "skills/double-entry", "skills/depreciation-rules", "skills/ifrs", "general"]}
+{"id": "rc_094", "rooms": ["projects/invoice-parsing", "projects/cashflow-models", "skills/double-entry", "skills/ifrs", "daily-logs", "general"]}
+{"id": "rc_095", "rooms": ["projects/tax-prep", "projects/cashflow-models", "skills/double-entry", "skills/ifrs", "daily-logs", "general"]}
+{"id": "rc_096", "rooms": ["projects/cashflow-models", "projects/tax-prep", "skills/double-entry", "skills/ifrs", "general"]}
+{"id": "rc_097", "rooms": ["projects/tax-prep", "projects/cashflow-models", "skills/double-entry", "skills/ifrs", "daily-logs", "general"]}
+{"id": "rc_098", "rooms": ["about", "projects/cashflow-models", "projects/tax-prep", "skills/double-entry", "skills/depreciation-rules", "general"]}
+{"id": "rc_099", "rooms": ["about", "projects/invoice-parsing", "projects/tax-prep", "skills/double-entry", "skills/depreciation-rules", "daily-logs", "general"]}
+{"id": "rc_100", "rooms": ["about", "projects/invoice-parsing", "projects/tax-prep", "skills/double-entry", "skills/ifrs", "general"]}
+{"id": "rc_101", "rooms": ["about", "projects/distributed-tracing", "projects/parser-combinators", "skills/rust", "skills/ocaml", "daily-logs", "general"]}

benchmarks/model_eval/metrics.py

@@ -0,0 +1,372 @@
+"""Metrics: timing extraction, VRAM measurement, embedding similarity, percentiles."""
+from __future__ import annotations
+
+import json
+import re
+import statistics
+import subprocess
+import threading
+import time
+from dataclasses import dataclass, field
+from typing import Optional
+from urllib.error import HTTPError, URLError
+from urllib.request import Request, urlopen
+
+
+# ── Thinking-token stripping ─────────────────────────────────────────────
+#
+# Some models (Qwen 3 reasoning variants, DeepSeek-R1, QwQ) emit
+# <think>...</think> blocks before their actual answer, or use Ollama's
+# `thinking` / `reasoning` response fields. The benchmark scores against
+# the final answer only, so we strip these.
+#
+# This is a local copy of the same logic that lives in
+# `mempalace.local_model.strip_thinking_tokens`. We duplicate it here so
+# the harness can land on develop independently of the model-router PR.
+# When that PR merges, replace this with: from mempalace.local_model
+# import strip_thinking_tokens
+
+_THINK_BLOCK_RE = re.compile(r"<think>.*?</think>", re.DOTALL)
+
+
+def strip_thinking_tokens(text: str, raw: dict) -> str:
+    """Remove <think>...</think> blocks from text. Falls back to the raw
+    response's `thinking` or `reasoning` fields if the main text is empty
+    after stripping."""
+    cleaned = _THINK_BLOCK_RE.sub("", text or "").strip()
+    if cleaned:
+        return cleaned
+    msg = raw.get("message") if isinstance(raw, dict) else None
+    if isinstance(msg, dict):
+        for field_name in ("content", "thinking", "reasoning"):
+            v = msg.get(field_name)
+            if isinstance(v, str) and v.strip():
+                return _THINK_BLOCK_RE.sub("", v).strip()
+    for field_name in ("thinking", "reasoning"):
+        v = raw.get(field_name) if isinstance(raw, dict) else None
+        if isinstance(v, str) and v.strip():
+            return _THINK_BLOCK_RE.sub("", v).strip()
+    return cleaned
+
+
+# ── Timing extraction from Ollama response ───────────────────────────────
+
+
+@dataclass
+class TimingSample:
+    """Per-request timing extracted from Ollama's response payload."""
+
+    e2e_ms: float
+    ttft_ms: float
+    tps: float
+    eval_tokens: int
+    prompt_tokens: int
+
+
+def extract_timing(raw: dict, wall_clock_seconds: float) -> TimingSample:
+    """Pull timing numbers out of an Ollama /api/chat response.
+
+    Ollama returns durations in nanoseconds. Wall-clock is the Python-side
+    measurement of the full request; we use it as the e2e source of truth
+    since `total_duration` excludes some HTTP overhead.
+
+    TTFT is approximated as load + prompt_eval, which is the time before
+    the first output token would have streamed. Close enough for
+    benchmarking without implementing streaming.
+    """
+    e2e_ms = wall_clock_seconds * 1000.0
+    load_ns = raw.get("load_duration", 0) or 0
+    prompt_eval_ns = raw.get("prompt_eval_duration", 0) or 0
+    eval_ns = raw.get("eval_duration", 0) or 0
+    eval_count = raw.get("eval_count", 0) or 0
+    prompt_count = raw.get("prompt_eval_count", 0) or 0
+
+    ttft_ms = (load_ns + prompt_eval_ns) / 1e6
+    tps = (eval_count / (eval_ns / 1e9)) if eval_ns > 0 else 0.0
+
+    return TimingSample(
+        e2e_ms=e2e_ms,
+        ttft_ms=ttft_ms,
+        tps=tps,
+        eval_tokens=eval_count,
+        prompt_tokens=prompt_count,
+    )
+
+
+# ── Percentile aggregation ───────────────────────────────────────────────
+
+
+@dataclass
+class TimingAggregate:
+    e2e_p50_ms: float
+    e2e_p95_ms: float
+    ttft_p50_ms: float
+    ttft_p95_ms: float
+    tps_p50: float
+    tps_p95: float
+    n: int
+
+
+def aggregate_timings(samples: list[TimingSample]) -> TimingAggregate:
+    if not samples:
+        return TimingAggregate(0, 0, 0, 0, 0, 0, 0)
+    e2e = sorted(s.e2e_ms for s in samples)
+    ttft = sorted(s.ttft_ms for s in samples)
+    tps = sorted(s.tps for s in samples)
+    return TimingAggregate(
+        e2e_p50_ms=_p(e2e, 50),
+        e2e_p95_ms=_p(e2e, 95),
+        ttft_p50_ms=_p(ttft, 50),
+        ttft_p95_ms=_p(ttft, 95),
+        tps_p50=_p(tps, 50),
+        tps_p95=_p(tps, 95),
+        n=len(samples),
+    )
+
+
+def _p(sorted_values: list[float], pct: int) -> float:
+    if not sorted_values:
+        return 0.0
+    if len(sorted_values) == 1:
+        return sorted_values[0]
+    # Nearest-rank: matches what most production tools (statsd, Datadog) report
+    rank = max(0, min(len(sorted_values) - 1, int(round((pct / 100.0) * (len(sorted_values) - 1)))))
+    return sorted_values[rank]
+
+
+# ── VRAM measurement ─────────────────────────────────────────────────────
+
+
+def vram_resident_mb(
+    model_tag: str, endpoint: str = "http://localhost:11434", timeout: int = 5
+) -> Optional[int]:
+    """Resident VRAM for a loaded model, read from Ollama's /api/ps endpoint.
+
+    Returns None if the model isn't loaded or the endpoint is unreachable.
+    Uses the HTTP API rather than `ollama ps` since the CLI's --format flag
+    is missing in older Ollama versions (verified against 0.23.2).
+    """
+    try:
+        with urlopen(f"{endpoint}/api/ps", timeout=timeout) as resp:
+            data = json.loads(resp.read())
+    except (URLError, HTTPError, OSError, json.JSONDecodeError):
+        return None
+    for model in data.get("models", []) or []:
+        if not isinstance(model, dict):
+            continue
+        name = model.get("name") or model.get("model") or ""
+        if name == model_tag:
+            size = model.get("size_vram") or model.get("size") or 0
+            return int(size / (1024 * 1024)) if size else None
+    return None
+
+
+class VRAMPoller:
+    """Poll nvidia-smi for peak GPU memory during a code block.
+
+    Usage:
+        poller = VRAMPoller()
+        poller.start()
+        # ... run inference ...
+        peak_mb = poller.stop()
+
+    Returns None if nvidia-smi is unavailable. Multi-GPU: tracks GPU 0 only.
+    Single-GPU is the assumed deployment for this benchmark.
+    """
+
+    def __init__(self, interval_s: float = 0.5):
+        # 500ms balances peak-capture coverage against jitter from nvidia-smi
+        # subprocess spawns. Inference VRAM is mostly steady-state during a
+        # request, so missing the absolute peak by a few-percent margin is
+        # acceptable and worth the reduced overhead on the run itself.
+        self.interval_s = interval_s
+        self._stop = threading.Event()
+        self._thread: Optional[threading.Thread] = None
+        self._peak_mb: int = 0
+        self._available = self._check_nvidia_smi()
+
+    @staticmethod
+    def _check_nvidia_smi() -> bool:
+        try:
+            result = subprocess.run(
+                ["nvidia-smi", "--query-gpu=memory.used", "--format=csv,noheader,nounits"],
+                capture_output=True,
+                text=True,
+                timeout=5,
+            )
+            return result.returncode == 0
+        except (FileNotFoundError, subprocess.TimeoutExpired):
+            return False
+
+    def _poll(self):
+        while not self._stop.is_set():
+            try:
+                result = subprocess.run(
+                    ["nvidia-smi", "--query-gpu=memory.used", "--format=csv,noheader,nounits"],
+                    capture_output=True,
+                    text=True,
+                    timeout=2,
+                )
+                if result.returncode == 0:
+                    first_line = result.stdout.strip().splitlines()[0]
+                    mb = int(first_line.strip())
+                    if mb > self._peak_mb:
+                        self._peak_mb = mb
+            except (subprocess.TimeoutExpired, ValueError, IndexError):
+                pass
+            self._stop.wait(self.interval_s)
+
+    def start(self):
+        if not self._available:
+            return
+        self._stop.clear()
+        self._peak_mb = 0
+        self._thread = threading.Thread(target=self._poll, daemon=True)
+        self._thread.start()
+
+    def stop(self) -> Optional[int]:
+        if not self._available or self._thread is None:
+            return None
+        self._stop.set()
+        self._thread.join(timeout=2)
+        return self._peak_mb if self._peak_mb > 0 else None
+
+
+# ── Embedding similarity (open-set scoring) ──────────────────────────────
+
+
+def embed_text(
+    text: str,
+    model: str = "nomic-embed-text",
+    endpoint: str = "http://localhost:11434",
+    timeout: int = 30,
+) -> Optional[list[float]]:
+    """Get an embedding from Ollama's /api/embeddings. Returns None on failure."""
+    body = json.dumps({"model": model, "prompt": text}).encode("utf-8")
+    req = Request(
+        f"{endpoint}/api/embeddings",
+        data=body,
+        headers={"Content-Type": "application/json"},
+    )
+    try:
+        with urlopen(req, timeout=timeout) as resp:
+            data = json.loads(resp.read())
+    except (URLError, HTTPError, OSError, json.JSONDecodeError):
+        return None
+    return data.get("embedding")
+
+
+def cosine_similarity(a: list[float], b: list[float]) -> float:
+    if not a or not b or len(a) != len(b):
+        return 0.0
+    dot = sum(x * y for x, y in zip(a, b))
+    norm_a = sum(x * x for x in a) ** 0.5
+    norm_b = sum(x * x for x in b) ** 0.5
+    if norm_a == 0 or norm_b == 0:
+        return 0.0
+    return dot / (norm_a * norm_b)
+
+
+def label_similarity(
+    predicted: str,
+    target: str,
+    embed_model: str = "nomic-embed-text",
+    endpoint: str = "http://localhost:11434",
+) -> float:
+    """Cosine similarity between embeddings of two label strings.
+
+    Used for open-set scoring where the model invents a label and we need
+    to compare against a hand-chosen preferred label without requiring
+    exact-match wording.
+    """
+    if predicted.strip().lower() == target.strip().lower():
+        return 1.0
+    emb_p = embed_text(predicted, model=embed_model, endpoint=endpoint)
+    emb_t = embed_text(target, model=embed_model, endpoint=endpoint)
+    if emb_p is None or emb_t is None:
+        return 0.0
+    return cosine_similarity(emb_p, emb_t)
+
+
+# ── Hardware reporting ──────────────────────────────────────────────────
+
+
+@dataclass
+class HostInfo:
+    cpu: str = ""
+    cores: int = 0
+    ram_gb: float = 0.0
+    gpu: str = ""
+    gpu_vram_mb: int = 0
+    ollama_version: str = ""
+    os: str = ""
+    hostname: str = ""
+
+
+def gather_host_info() -> HostInfo:
+    """Best-effort introspection of the test machine. Failures degrade
+    silently rather than aborting the benchmark."""
+    info = HostInfo()
+
+    info.hostname = (_run(["hostname"]) or "").strip()
+
+    cpuinfo = _read_file("/proc/cpuinfo") or ""
+    for line in cpuinfo.splitlines():
+        if line.startswith("model name"):
+            info.cpu = line.split(":", 1)[1].strip()
+            break
+
+    nproc = _run(["nproc"])
+    if nproc:
+        try:
+            info.cores = int(nproc.strip())
+        except ValueError:
+            pass
+
+    meminfo = _read_file("/proc/meminfo") or ""
+    for line in meminfo.splitlines():
+        if line.startswith("MemTotal:"):
+            kb = int(line.split()[1])
+            info.ram_gb = round(kb / (1024 * 1024), 1)
+            break
+
+    nvidia = _run([
+        "nvidia-smi",
+        "--query-gpu=name,memory.total",
+        "--format=csv,noheader,nounits",
+    ])
+    if nvidia:
+        first = nvidia.strip().splitlines()[0]
+        if "," in first:
+            name, vram = first.split(",", 1)
+            info.gpu = name.strip()
+            try:
+                info.gpu_vram_mb = int(vram.strip())
+            except ValueError:
+                pass
+
+    info.ollama_version = (_run(["ollama", "--version"]) or "").strip()
+
+    info.os = (_read_file("/etc/os-release") or "").splitlines()[0] if _read_file("/etc/os-release") else ""
+    if info.os.startswith("PRETTY_NAME="):
+        info.os = info.os.split("=", 1)[1].strip('"')
+
+    return info
+
+
+def _run(cmd: list[str]) -> Optional[str]:
+    try:
+        result = subprocess.run(cmd, capture_output=True, text=True, timeout=5)
+        if result.returncode == 0:
+            return result.stdout
+    except (FileNotFoundError, subprocess.TimeoutExpired):
+        pass
+    return None
+
+
+def _read_file(path: str) -> Optional[str]:
+    try:
+        with open(path, "r") as f:
+            return f.read()
+    except OSError:
+        return None

benchmarks/model_eval/orchestrator.py

@@ -0,0 +1,308 @@
+"""Run the full matrix: candidates × (task, mode) → CSV.
+
+Usage:
+    python -m benchmarks.model_eval.orchestrator \\
+        --candidates tier1 \\
+        --tasks all \\
+        --dataset-dir benchmarks/model_eval/datasets \\
+        --output benchmarks/model_eval/results/$(date -u +%Y-%m-%d)-$(hostname).csv
+
+The matrix per default:
+    candidates: all `tier1` entries from candidates.yaml
+    tasks: room_classification (closed + open), entity_extraction,
+           memory_extraction, calibration
+"""
+from __future__ import annotations
+
+import argparse
+import csv
+import dataclasses
+import json
+import socket
+import sys
+import time
+from dataclasses import asdict
+from pathlib import Path
+from typing import Iterable
+
+import yaml
+
+from .runner import Result, _EMBED_MODEL, _result_to_dict, run
+
+
+TASK_MODES = [
+    ("room_classification", "closed"),
+    ("room_classification", "open"),
+    ("entity_extraction", "default"),
+    ("memory_extraction", "default"),
+    ("calibration", "default"),
+]
+
+
+CSV_COLUMNS = [
+    "model_tag",
+    "task",
+    "mode",
+    "language",
+    "n_samples",
+    "accuracy",
+    "ttft_p50_ms",
+    "ttft_p95_ms",
+    "tps_p50",
+    "tps_p95",
+    "e2e_p50_ms",
+    "e2e_p95_ms",
+    "vram_resident_mb",
+    "vram_peak_mb",
+    "host",
+    "gpu",
+    "ollama_version",
+    "run_date",
+    "error",
+    "extras_json",
+]
+
+
+def load_candidates(path: Path, tier: str) -> list[dict]:
+    with open(path, "r") as f:
+        data = yaml.safe_load(f)
+    candidates = data.get("candidates", [])
+    if tier == "all":
+        return candidates
+    if tier == "cloud":
+        return [c for c in candidates if c.get("tier") == "cloud" or c.get("cloud")]
+    if tier == "local":
+        return [c for c in candidates if c.get("tier") != "cloud" and not c.get("cloud")]
+    if tier == "modern":
+        return [c for c in candidates if c.get("tier") == "modern"]
+    if tier == "community":
+        return [c for c in candidates if c.get("tier") == "community"]
+    if tier.startswith("tier<="):
+        try:
+            n = int(tier.split("<=")[1])
+        except (ValueError, IndexError):
+            return []
+        return [c for c in candidates if isinstance(c.get("tier"), int) and c["tier"] <= n]
+    if tier.startswith("tier"):
+        try:
+            n = int(tier[4:])
+        except ValueError:
+            return []
+        return [c for c in candidates if c.get("tier") == n]
+    # Specific tag, or comma-separated list of tags. Synthesize minimal entries
+    # for tags not present in candidates.yaml so ad-hoc models can be evaluated
+    # without editing the yaml.
+    requested_tags = [t.strip() for t in tier.split(",") if t.strip()]
+    known_by_tag = {c["tag"]: c for c in candidates}
+    return [known_by_tag.get(t, {"tag": t}) for t in requested_tags]
+
+
+def parse_tasks(arg: str) -> list[tuple[str, str]]:
+    if arg == "all":
+        return TASK_MODES
+    out = []
+    for piece in arg.split(","):
+        piece = piece.strip()
+        if ":" in piece:
+            t, m = piece.split(":", 1)
+            out.append((t, m))
+        else:
+            for t, m in TASK_MODES:
+                if t == piece:
+                    out.append((t, m))
+    return out
+
+
+def result_to_row(result: Result) -> dict:
+    return {
+        "model_tag": result.model_tag,
+        "task": result.task,
+        "mode": result.mode,
+        "language": result.language,
+        "n_samples": result.n_samples,
+        "accuracy": round(result.accuracy, 4),
+        "ttft_p50_ms": round(result.timing.ttft_p50_ms, 1),
+        "ttft_p95_ms": round(result.timing.ttft_p95_ms, 1),
+        "tps_p50": round(result.timing.tps_p50, 1),
+        "tps_p95": round(result.timing.tps_p95, 1),
+        "e2e_p50_ms": round(result.timing.e2e_p50_ms, 1),
+        "e2e_p95_ms": round(result.timing.e2e_p95_ms, 1),
+        "vram_resident_mb": result.vram_resident_mb if result.vram_resident_mb else "",
+        "vram_peak_mb": result.vram_peak_mb if result.vram_peak_mb else "",
+        "host": result.host.hostname,
+        "gpu": result.host.gpu,
+        "ollama_version": result.host.ollama_version.split("\n")[0] if result.host.ollama_version else "",
+        "run_date": result.run_date,
+        "error": result.error or "",
+        "extras_json": json.dumps(result.extras, separators=(",", ":")),
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Run benchmark matrix across candidates")
+    parser.add_argument("--candidates-file", type=Path, default=Path(__file__).parent / "candidates.yaml")
+    parser.add_argument("--candidates", default="tier1", help="tier1, tier2, tier3, all, tier<=2, or a specific model tag")
+    parser.add_argument("--tasks", default="all", help="all, or comma-separated list (e.g. 'room_classification:closed,calibration')")
+    parser.add_argument("--dataset-dir", required=True, type=Path)
+    output_group = parser.add_mutually_exclusive_group(required=True)
+    output_group.add_argument("--output", type=Path, default=None,
+                              help="Single CSV output (all languages in one file).")
+    output_group.add_argument("--output-dir", type=Path, default=None,
+                              help="Output directory; writes <dir>/<lang>/YYYY-MM-DD-<host>.csv "
+                                   "per language so results stay grouped by locale.")
+    parser.add_argument("--endpoint", default="http://localhost:11434")
+    parser.add_argument("--llm-provider", default="ollama",
+                        choices=["ollama", "openai-compat", "anthropic"],
+                        help="LLM provider (default: ollama)")
+    parser.add_argument("--embed-endpoint", default=None,
+                        help="Endpoint for the embedding model (always Ollama). "
+                             "When omitted: defaults to --endpoint if --llm-provider=ollama, "
+                             "else http://localhost:11434.")
+    parser.add_argument("--embed-model", default=_EMBED_MODEL,
+                        help=f"Embedding model for semantic-similarity scoring. Default: {_EMBED_MODEL}.")
+    parser.add_argument("--num-ctx", type=int, default=4096,
+                        help="Ollama context window per request (sent as options.num_ctx). "
+                             "Defaults to 4096 so every candidate runs at the same window regardless "
+                             "of its Modelfile default — without this, a 32k-default model pre-allocates "
+                             "KV cache that a 4k-default model doesn't, and accuracy/latency/VRAM stop "
+                             "being comparable. Pass a larger value if a task prompt exceeds 4k tokens.")
+    parser.add_argument("--warmup", type=int, default=1)
+    parser.add_argument("--n", type=int, default=None, help="Limit each task to first N samples (debug mode)")
+    parser.add_argument(
+        "--languages",
+        default="en",
+        help="Comma-separated dataset languages. 'en' uses dataset.jsonl; "
+             "other values use dataset.{lang}.jsonl. Example: 'en,pt-BR,es,zh'.",
+    )
+    parser.add_argument(
+        "--continue-on-error",
+        action=argparse.BooleanOptionalAction,
+        default=True,
+        help="Continue running remaining (model, task) pairs after a failure (default). Use --no-continue-on-error to abort on first failure.",
+    )
+    args = parser.parse_args()
+
+    # Resolve --embed-endpoint default: it lives on Ollama always, but should
+    # follow --endpoint when the LLM provider is Ollama so a remote benchmark
+    # run scores against the same host.
+    if args.embed_endpoint is None:
+        args.embed_endpoint = args.endpoint if args.llm_provider == "ollama" else "http://localhost:11434"
+
+    candidates = load_candidates(args.candidates_file, args.candidates)
+    if not candidates:
+        print(f"No candidates matched: {args.candidates}", file=sys.stderr)
+        sys.exit(1)
+
+    task_modes = parse_tasks(args.tasks)
+    if not task_modes:
+        print(f"No tasks matched: {args.tasks}", file=sys.stderr)
+        sys.exit(1)
+
+    languages = [lang.strip() for lang in args.languages.split(",") if lang.strip()]
+    if not languages:
+        languages = ["en"]
+
+    n_runs = len(candidates) * len(task_modes) * len(languages)
+    print(f"Running {len(candidates)} candidates × {len(task_modes)} task/mode × {len(languages)} languages = {n_runs} runs")
+    print(f"Languages: {languages}")
+
+    # Resolve output path(s). --output-dir writes one CSV per language so results
+    # from long multilingual runs stay grouped by locale and are easier to diff.
+    # --output writes everything to one CSV (one shared file handle to avoid
+    # interleaved buffer corruption across languages).
+    _host = socket.gethostname()
+    _date = time.strftime("%Y-%m-%d")
+
+    def _csv_path(language: str) -> Path:
+        if args.output_dir:
+            return args.output_dir / language / f"{_date}-{_host}.csv"
+        return args.output
+
+    lang_files: dict[str, tuple] = {}
+    if args.output_dir:
+        # One file per language — no path collisions possible.
+        for lang in languages:
+            p = _csv_path(lang)
+            p.parent.mkdir(parents=True, exist_ok=True)
+            fh = open(p, "w", newline="", encoding="utf-8")
+            w = csv.DictWriter(fh, fieldnames=CSV_COLUMNS)
+            w.writeheader()
+            fh.flush()
+            lang_files[lang] = (fh, w)
+            print(f"  {lang} → {p}")
+    else:
+        # Single shared file — open once, share the same (fh, writer) for every
+        # language so writes don't fight over independent buffer offsets.
+        p = args.output
+        p.parent.mkdir(parents=True, exist_ok=True)
+        fh = open(p, "w", newline="", encoding="utf-8")
+        w = csv.DictWriter(fh, fieldnames=CSV_COLUMNS)
+        w.writeheader()
+        fh.flush()
+        for lang in languages:
+            lang_files[lang] = (fh, w)
+        print(f"  output → {p}")
+
+    rows = []
+    total = n_runs
+    i = 0
+    start = time.time()
+
+    try:
+        for candidate in candidates:
+            tag = candidate["tag"]
+            for task, mode in task_modes:
+                for language in languages:
+                    fh, writer = lang_files[language]
+                    i += 1
+                    print(f"[{i}/{total}] {tag}  {task}  {mode}  lang={language}", flush=True)
+                    try:
+                        result = run(
+                            model_tag=tag,
+                            task=task,
+                            mode=mode,
+                            dataset_dir=args.dataset_dir,
+                            endpoint=args.endpoint,
+                            warmup=args.warmup,
+                            n_samples=args.n,
+                            llm_provider=args.llm_provider,
+                            embed_endpoint=args.embed_endpoint,
+                            embed_model=args.embed_model,
+                            language=language,
+                            num_ctx=args.num_ctx,
+                        )
+                    except Exception as e:
+                        if not args.continue_on_error:
+                            raise
+                        print(f"  ERROR: {e}", file=sys.stderr)
+                        continue
+                    row = result_to_row(result)
+                    rows.append(row)
+                    writer.writerow(row)
+                    fh.flush()
+                    print(f"  acc={row['accuracy']}  e2e_p50={row['e2e_p50_ms']}ms  vram={row['vram_resident_mb']}", flush=True)
+    finally:
+        # In --output mode every language maps to the same fh; dedupe by id to
+        # avoid double-close.
+        seen_fhs: set[int] = set()
+        for fh, _ in lang_files.values():
+            if id(fh) in seen_fhs:
+                continue
+            seen_fhs.add(id(fh))
+            fh.close()
+
+    elapsed = time.time() - start
+    output_summary = args.output or args.output_dir
+    print(f"\nDone in {elapsed/60:.1f}min. Wrote {len(rows)} rows to {output_summary}")
+
+
+def write_csv(path: Path, rows: list[dict]):
+    """Batch-write helper, kept for callers that already have all rows in memory."""
+    with open(path, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=CSV_COLUMNS)
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+if __name__ == "__main__":
+    main()

benchmarks/model_eval/reports/2026-05-10-analysis.md

@@ -0,0 +1,299 @@
+# Small-model evaluation, 2026-05-10
+
+Analysis of the first full benchmark matrix run on `z690-ex-glacial` (Intel i9-12900KF, RTX 3090 24GB, Ollama 0.23.2). 15 candidate models × 5 task/mode pairs = 75 runs, completed in 61.8 minutes with 1 transient warmup timeout.
+
+Raw CSV: `results/2026-05-10-z690-ex-glacial.csv`. Auto-rendered tables: `reports/2026-05-10-z690-ex-glacial.md`. This file is the human-written interpretation of those numbers.
+
+**Second-pass note (2026-05-10 evening).** After the first pass landed, we did three follow-up checks:
+
+1. **Reproducibility spot-check**: re-ran `qwen3:4b-instruct-2507-q4_K_M` against the full task set. Deltas vs the original pass were ≤0.7% on every metric (room-closed 0.610 → 0.604, room-open 0.586 → 0.584, entity F1 0.778 → 0.771, memory coverage 0.950 → 0.950, calibration 0.950 → 0.950). The harness is reliable; single-run accuracy numbers can be trusted.
+2. **Memory-extraction hallucination investigation**: hand-inspected 5 samples on the leader. The 0.36 hallucination rate is an artifact of scoring methodology, not a model weakness — see the dedicated section below.
+3. **Cloud-tier ceiling measurement**: completed at n=30 for four of five candidates. Cloud beats local by ~0.30 absolute on closed-set room classification (best 0.900 vs local 0.610) but does NOT beat local on open-set (best 0.587 vs local 0.612). Open-set ceiling is refuted: 480B and 671B candidates plateau at the same range small models hit. Kimi-K2 1T returned HTTP 500 on every task — retry pending. Full table in the Cloud-tier ceiling measurements section below.
+
+We also cherry-picked one real-format-flavored sample (rc_101) from Lumi's hand-written v3 fixture on the OpenClaw integration branch. Dataset is now 101 room_classification samples instead of 100.
+
+**Third-pass note (2026-05-11).** After the cloud results landed, Igor flagged five competent local model families that the first ollama.com search had missed: `granite4.1:3b`, `gemma4:e2b`, `gemma4:e4b`, `ministral-3:3b`, `qwen3.5:4b`. Ran them as a `modern` tier at full sample sizes. Three findings worth elevating:
+
+1. **`gemma4:e4b-it-q4_K_M` is the new local leader for room classification.** Closed-set 0.62 (above qwen3:4b q4_K_M's 0.61), open-set **0.65 (highest score across ALL measured models, local AND cloud)**. The cloud "ceiling" on open-set was 0.61. A 4B local model now exceeds every cloud reference up to 1T parameters.
+2. **`ministral-3:3b` has 0.99 memory coverage** — nearly cloud-tier (1.00). But weak on closed-set (0.49) and entity F1 (0.63). Useful only when memory is the primary task.
+3. **`qwen3.5:4b-q4_K_M` does NOT beat `qwen3:4b-instruct-2507-q4_K_M`.** Newer version, similar parameters: slightly better entity F1 (0.79 vs 0.78), worse on memory (0.85 vs 0.95), worse on closed-set (0.59 vs 0.61). Version bump didn't help this workload.
+
+Net effect on the production tier list:
+
+- For OpenClaw's primary use case (room classification): **`gemma4:e4b-it-q4_K_M` becomes the new recommended default.** Trade-off: 230 ms p50 vs qwen3:4b's 109 ms (2.1x slower) and 10.6 GB resident vs 7.5 GB (1.4x VRAM).
+- For general-purpose extraction on tight hardware: `qwen3:4b-instruct-2507-q4_K_M` is still the right pick.
+- For the open-set discovery feature: the shelved-indefinitely verdict softens to **"retest with gemma4:e4b + prompt-tuning before declaring it dead."** The gap to the 0.70 ship threshold is now 0.05, not 0.09.
+
+## Headline finding
+
+**`qwen3:4b-instruct-2507-q4_K_M` is the best small model for MemPalace classification across every task we measured.** It wins or ties on calibration, closed-set room classification, entity extraction, and memory extraction, at 7.5 GB resident, sub-100ms p50 latency on calibration, and 624 ms p50 on entity extraction (the heaviest task).
+
+The q4_K_M quantization holds up against q8_0 and even fp16. On entity F1 it actually beats fp16 (0.778 vs 0.772) by a tiny margin within noise. On every other task the q4_K_M variant is within 0.01-0.02 of the fp16 ceiling. Conclusion: pay the VRAM cost only if the task pushes the model to its edge; q4_K_M is the right default.
+
+## Recommended `MODEL_TIERS` update
+
+Replace the current tier list in `mempalace/local_model.py` with:
+
+```python
+MODEL_TIERS = [
+    # Tier 1 — Best balance of speed/quality (instruct-tuned)
+    (r"qwen3:4b-instruct-2507", 100),
+    (r"gemma3:4b-it-q4_K_M", 88),
+    (r"gemma3:4b-it-qat", 87),
+    (r"qwen2\.5:3b-instruct", 82),
+    # Tier 2 — Fast but lower accuracy on hard tasks
+    (r"llama3\.2:3b-instruct", 70),
+    (r"phi3\.5:3\.8b-mini-instruct", 68),
+    (r"qwen2\.5:1\.5b-instruct", 55),
+    # Tier 3 — Fallback only
+    (r"gemma3:1b-it", 45),
+    (r"llama3\.2:1b-instruct", 40),
+    (r"qwen2\.5:0\.5b-instruct", 30),
+    # Reasoning-default tags below dedicated instruct variants. Picked
+    # only as a last resort. The runner forces think=False on every
+    # call, so even when these match they run in fast-classification
+    # mode rather than reasoning mode.
+    (r"qwen3:4b", 25),
+]
+```
+
+Changes from the current list:
+
+- Drop `qwen3\.5:4b` and `qwen3:3b` patterns: those tags don't exist on Ollama under those exact names; they were speculative entries that would never match.
+- Drop `qwen3:1\.7b` and `qwen3:0\.6b` patterns from Tier 2: both perform poorly on entity extraction (F1 0.31 and 0.48 respectively). Memory coverage is OK (0.84 / 0.74) but with bad type accuracy on the smaller one. Not worth recommending.
+- Drop `gemma2:2b`, `phi3:mini`, `tinyllama` from Tier 3: not in our test matrix; remove until benchmarked.
+- Demote generic `qwen3:4b` to score 25 (was 30 in the prior fix). The runner-side `think=False` means even the reasoning-default tag runs fast now, but it still scores worse than the explicit instruct release on every task, so it stays last.
+
+## Per-task winners
+
+| Task | Best model | Score | Notes |
+|---|---|---|---|
+| Calibration (sentence type, exact) | 6-way tie at 0.950 | 0.950 | Anything ≥1.5B is fine |
+| Closed-set room (exact) | qwen3:4b-instruct-2507-fp16 | 0.650 | q4_K_M close behind at 0.610 |
+| Open-set room (cosine sim) | gemma3:4b-it-q4_K_M | 0.612 | Below the 0.70 ship threshold |
+| Entity extraction (F1) | qwen3:4b-instruct-2507-q4_K_M | 0.778 | q4 beats fp16 within noise |
+| Memory extraction (coverage) | qwen3:4b-instruct-2507-q4_K_M | 0.950 | But 0.36 hallucination rate |
+
+## Cloud-tier ceiling measurements
+
+Two cloud passes. **First pass (2026-05-10):** five candidates from the initial guess, `kimi-k2:1t-cloud` failed with HTTP 500 across the board. **Second pass (2026-05-11):** refreshed lineup after Igor noted `kimi-k2:1t-cloud` had been superseded by `kimi-k2.6:cloud`, plus DeepSeek V4 added (Flash and Pro). Seven candidates total in v2, all succeeded.
+
+Raw data: `results/2026-05-10-cloud-z690-ex-glacial.csv` (v1), `results/2026-05-11-cloud-z690-ex-glacial.csv` (v2). The table below shows v2 results.
+
+| Model | room-closed | room-open | entity F1 | memory cov | calibration | e2e p50 (closed) |
+|---|---|---|---|---|---|---|
+| gpt-oss:20b-cloud | 0.897 | 0.555 | 0.748 | **1.000** | 0.900 | 1149 ms |
+| gpt-oss:120b-cloud | 0.767 | 0.528 | 0.831 | **1.000** | 0.950 | 1732 ms |
+| qwen3-coder:480b-cloud | **0.900** | 0.579 | 0.803 | 0.967 | 0.950 | **753 ms** |
+| deepseek-v3.1:671b-cloud | 0.800 | 0.559 | 0.830 | 0.967 | 0.950 | 708 ms |
+| deepseek-v4-flash:cloud | 0.633 | **0.607** | **0.837** | 0.950 | 0.950 | 723 ms |
+| deepseek-v4-pro:cloud | 0.833 | 0.605 | 0.827 | **1.000** | 0.950 | 2426 ms |
+| kimi-k2.6:cloud | 0.800 | 0.593 | 0.770 | (n/a) | 0.900 | 1042 ms |
+| **local leader (4B q4)** | 0.610 | 0.586 | 0.778 | 0.950 | 0.950 | **109 ms** |
+
+### Closed-set room classification: real ceiling gap
+
+Cloud models clearly do better. qwen3-coder:480b and gpt-oss:20b both hit 0.897-0.900 vs local 0.610 — about 30 points absolute and 47% relative.
+
+This is the strongest argument so far for offering a `mempalace mine --classifier cloud` option for users with high-stakes archives who can accept the privacy and cost trade-off. For the default case (privacy-first, no API key required), local stays the right choice.
+
+Interesting: gpt-oss:20b matches qwen3-coder:480b on this task despite being 24x smaller. Suggests qwen3-coder's coder-specialization is neutral for natural-language classification, not a benefit.
+
+### Open-set discovery: ceiling refuted (across two cloud generations)
+
+The first cloud pass capped cloud at 0.587 (qwen3-coder:480b). DeepSeek V4 lifts the ceiling slightly: `deepseek-v4-flash` hits 0.607, `deepseek-v4-pro` 0.605. **Still below local best (0.612, gemma3:4b-it) and still well below the 0.70 ship threshold.**
+
+Across both runs, every cloud candidate scored 0.55-0.61 on open-set. Local candidates score 0.46-0.61. Same range. The model class plateaus regardless of size: 4B, 284B, 480B, 671B, 1T MoE all converge to the same 0.55-0.61 cosine similarity against hand-chosen preferred labels.
+
+This isn't a model-size problem — it's a task formulation problem. More compute won't bridge the gap to 0.70. The model has to invent an open-vocabulary label that semantically matches a hand-chosen "preferred" label, and that's fundamentally a stylistic alignment problem.
+
+**Verdict on the `--mode discover` feature: shelved.** Reformulate the task (few-shot examples, constrained vocabulary, two-pass clustering) or accept that closed-set with a user-defined room list stays the required path.
+
+### Entity extraction: cloud edges local by ~5 points
+
+gpt-oss:120b (0.829) and deepseek-v3.1:671b (0.828) lead. Local qwen3:4b is at 0.778. The gap is real but modest. For typical use, local is good enough.
+
+### Memory extraction: gpt-oss models hit ceiling
+
+Both gpt-oss:20b and gpt-oss:120b score 1.000 coverage at n=30. Qwen3-coder and DeepSeek at 0.967. Local at 0.950. Cloud helps marginally; nothing decisive.
+
+### Calibration: saturated everywhere
+
+All cloud models in 0.90-0.95. Local already in the same range. Task is no longer discriminating between strong models — useful only as a sanity check.
+
+### Latency observations
+
+qwen3-coder:480b is the fastest cloud option at 599 ms p50 on closed-set classification, beating gpt-oss:120b (1656 ms) despite being 4x larger. Likely Ollama Cloud allocates more GPU per request for the bigger-customer Qwen3-coder traffic.
+
+deepseek-v3.1:671b at 735 ms is also faster than gpt-oss:120b. Inference-infrastructure efficiency varies by model, not just size.
+
+vs local 4B at 109 ms — cloud is **5-15x slower** including network RTT. For interactive workflows this matters. For batch mining of historical archives, it doesn't.
+
+### Cost note
+
+gpt-oss models continue emitting reasoning tokens even with `think: false` in the request body — verified directly via curl. The `content` field is clean (the harness extracts only that), but cloud inference time and quota burn includes reasoning generation we asked it to skip. Worth opening an upstream issue against Ollama Cloud for `think: false` support across all model families. For now, treat the cloud latency numbers as upper bounds; a non-reasoning gpt-oss equivalent would presumably be faster.
+
+### Kimi K2 1T: superseded by K2.6 (v2 run)
+
+The v1 run's HTTP 500s on `kimi-k2:1t-cloud` were not a transient outage — that tag was being retired in favor of `kimi-k2.6:cloud`. The v2 run uses K2.6 successfully on most tasks. Memory-extraction is the exception: see below.
+
+### Structured outputs aren't enforced on Ollama Cloud (real finding)
+
+Per [docs.ollama.com/capabilities/structured-outputs](https://docs.ollama.com/capabilities/structured-outputs): *"Ollama's Cloud currently does not support structured outputs."* The `format: json` flag the harness sends gets silently ignored on cloud requests. Cloud models that "happen to" emit valid JSON do so because their default behavior aligns with JSON, not because Ollama enforces it.
+
+This is the explanation for K2.6's `valid_json_rate: 0.367` on memory extraction. The other 63% of responses emitted markdown code fences, prose preambles, or a different schema. The 0.367 "coverage" number is really "JSON parse rate × per-sample coverage," not a true measure of memory-extraction quality.
+
+The other six cloud candidates achieved 100% valid_json_rate on memory extraction in v2. They lucked into JSON-shaped default output. That's fragile — any future cloud model with different default behavior could fail the same way K2.6 did.
+
+Implications for the harness:
+
+1. **For local runs**, `format: json` is enforced and JSON parsing is reliable. No change needed.
+2. **For cloud runs**, the harness should either:
+   - **Add a fallback parser** that extracts JSON from responses with markdown fences, prose preambles, or other common envelope patterns (covers the 63% K2.6 case, generalizes for future cloud models)
+   - **Use the explicit JSON schema syntax** (`format: { type: "object", properties: ... }`) — wait, no, that also requires cloud support which isn't there
+   - **Use an OpenAI-compatible cloud endpoint** instead of Ollama Cloud when structured output matters (per the docs, openai-compat endpoints honor structured outputs)
+
+Recommendation for the PR: leave the harness as-is, document this limitation in the cloud-runs section, and add a follow-up issue to add envelope-aware JSON extraction as a fallback. K2.6's memory result should be quoted as "JSON-mode reliability 37%, not directly comparable to other models on this task" rather than a quality score.
+
+### Cloud reproducibility caveat (v1 vs v2)
+
+Re-running the four common cloud candidates between v1 (2026-05-10) and v2 (2026-05-11) showed some drift:
+
+| Model | Task | v1 | v2 | Delta |
+|---|---|---|---|---|
+| gpt-oss:20b-cloud | closed | 0.833 | 0.897 | **+0.064** |
+| gpt-oss:120b-cloud | closed | 0.800 | 0.767 | -0.033 |
+| qwen3-coder:480b-cloud | closed | 0.900 | 0.900 | 0 |
+| deepseek-v3.1:671b-cloud | closed | 0.800 | 0.800 | 0 |
+
+The 6.4-point jump on gpt-oss:20b is bigger than the temperature=0.1 noise floor we saw on local runs (±0.7%). Possible explanations:
+
+- Cloud-side model weight refresh between runs (Ollama Cloud may rotate model versions silently)
+- Server-side load affecting non-deterministic operation ordering
+- The same `--n 30` slice rendering differently due to model-side caching state
+
+This is a real methodological wart. For the LOCAL runs we have ±0.7% reproducibility; for CLOUD we have to report ranges, not point estimates. The cloud findings should be quoted as "cloud-best closed-set: 0.83-0.90, local-best 0.61" rather than fixed numbers.
+
+## Open-set discovery: do not ship
+
+Best similarity score across all 15 local models is 0.612 (gemma3:4b-it-q4_K_M and gemma3:4b-it-qat tied). Cloud models (above) cap at 0.587 — not better. The 0.70 ship threshold is unreachable with current prompt design.
+
+What this means in plain English: even the best small model invents a label that's only loosely related to the hand-chosen "preferred" label. Most predictions land in the 0.4-0.7 range — semantically related but not the same slug. Real users wouldn't get a coherent taxonomy out of this; they'd get a sprawl of near-synonyms.
+
+**Recommendation: do not ship `mempalace mine --mode discover` at this prompt design.** Closed-set classification with a user-defined room list stays the required path.
+
+The cloud-tier measurements above ruled out "more compute will close the gap." 480B and 671B candidates plateau at the same ~0.58-0.59 the small models hit. This is a task-formulation problem, not a capacity problem.
+
+Two avenues worth exploring before declaring open-set permanently unreachable:
+
+1. **Better prompting.** The current open-set prompt tells the model to invent any slug. Adding examples (few-shot) or a constrained vocabulary might lift the score.
+2. **Two-pass clustering.** First-pass small-model labeling, second-pass clustering to consolidate near-synonyms. Different architecture, more code, but achievable with small models.
+
+Both are research projects, not a tweak. Until one of them lifts cloud-or-local similarity above 0.75 on this dataset, the closed-set requirement stays.
+
+## Surprises and anomalies
+
+### `qwen3:4b q4_K_M` beats `fp16` on entity F1
+
+Margin is 0.778 vs 0.772 — well within run-to-run noise. But there's no quality cliff from quantization on this task at all. q4_K_M ships in 2.5 GB (download), 7.5 GB (resident), vs 8.1 GB / 13.2 GB for fp16. Same accuracy, half the memory.
+
+### The 0.36 "hallucination rate" on qwen3:4b memory extraction is a scoring artifact
+
+First reading: qwen3:4b scores 0.95 coverage but 0.36 of its predictions don't match any truth memory. Compared to qwen2.5:3b at 0.00 hallucination and gemma3:4b at 0.19, qwen3 looks enthusiastic in a bad way.
+
+I sampled the actual predictions side-by-side with source text and ground truth on 5 representative samples. The story inverted.
+
+Across the 5 samples, qwen3:4b consistently:
+
+- **Splits bundled memories into atomic items.** `mem_006` has one truth memory bundling "no more NPK + switching to compost + starting this fall." qwen3:4b emits two: the decision plus the commitment-to-timing. Both pieces are in the source text. The synthesis agent chose to bundle when labeling; the model chose to split. Neither is wrong.
+- **Catches commitments the ground truth missed.** `mem_001` truth lists only the cosine-to-Jaccard decision. qwen3:4b also extracts "Re-run the benchmarks tomorrow" — which is literally in the source. Ground-truth omission, not hallucination.
+- **Splits bundled facts into atomic facts.** `mem_013` has two bundled truth memories. qwen3:4b emits three atomic ones. All three contents are traceable to the source.
+
+For comparison, **qwen2.5:3b under-extracts on the same samples**. Its "perfect 0.00 hallucination" is because it emits one memory per sample, often missing the second truth memory entirely:
+
+- `mem_021`: misses the "restructure outline" commitment
+- `mem_031`: misses the "AR queries via email" decision
+
+The metric was lying. Greedy embedding matching pairs predictions to truth one-to-one, then counts unmatched predictions as hallucinations. When a model produces a finer-grained extraction than the ground truth (the correct behavior for memory extraction), the metric penalizes it.
+
+**Implications for the production tier list:** qwen3:4b-instruct-2507-q4_K_M stays the recommendation. Its 0.95 coverage IS the real signal. Decomposing the 0.36 hallucination number across the sampled predictions:
+
+- Hallucinated content (predicted, not in source): probably <5%; needs a separate scorer to confirm
+- Granularity disagreement (one truth bundle split into N predictions): ~50% of the "hallucinations"
+- Ground-truth omission (predicted is in source, missed by labelers): ~30%
+
+**Methodology fix for a follow-up PR:** modify `tasks/memory_extraction/score.py` to either (a) embed-match predictions against the source text in addition to ground truth, treating "matches source but not truth" as a ground-truth omission rather than a hallucination, or (b) report `coverage`, `granularity_factor` (predicted/truth count ratio), and `source_traceability` as three separate metrics without combining them. Until then, trust `mean_coverage` and ignore `mean_hallucination_rate` for model selection.
+
+### `phi3.5:3.8b-mini-instruct` is the speed winner
+
+30 ms p50 on calibration — 2.8x faster than qwen3:4b. Why? Probably aggressive batching in Phi-3.5's quantization layout. But it pays for it elsewhere: 16.6 GB resident (highest of the 4B class), 0.47 closed-set room (well below qwen3), and middling entity F1 of 0.64.
+
+Interesting niche use: workloads where latency dominates and accuracy is forgiving. Probably not the right default for MemPalace.
+
+### `gemma3:4b-it` family wins open-set
+
+Both `gemma3:4b-it-q4_K_M` and `gemma3:4b-it-qat` hit 0.612 mean similarity, beating the qwen3 family. Theory: Gemma's training data includes more naming-by-convention examples, so when asked to invent a slug it produces more conventional-sounding labels.
+
+If the open-set discovery feature ever ships, gemma3:4b-it would be the model to start with, not qwen3.
+
+### Smaller Qwen 3 variants underperform their size class
+
+`qwen3:1.7b-q4_K_M` and `qwen3:0.6b-q4_K_M` score 0.31 and 0.48 on entity F1 respectively, with high precision but very low recall (0.67/0.23 and 0.57/0.47). They're not extracting enough entities. The 1.5B Qwen 2.5 model scores higher in absolute F1 (0.37) despite having a similar parameter count to the 1.7B Qwen 3.
+
+Could be an artifact of the JSON-mode prompt + think=False interaction on hybrid-tagged models that small. Worth investigating before recommending any sub-3B Qwen 3 variant.
+
+### llama3.2:3b has p95 latency cliff
+
+p50 calibration: 92 ms. p95: 5050 ms. That's a 55x outlier. Single sample blew up the p95 — something cold-started or got rescheduled mid-run. Worth a re-run to confirm if this is reproducible or transient. Doesn't change the tier list (it's not in Tier 1 anyway) but flag it.
+
+### gemma3:270m is essentially non-functional
+
+5% calibration accuracy (worse than random across 5 classes), 12% closed-set, 38% open-set, 2.5% memory coverage. This model doesn't follow instructions well enough for any of these tasks. Move it to a "do not use" line in the docs rather than the tier list.
+
+## VRAM observations
+
+The peak-VRAM column varies wildly because it reflects whatever the GPU was doing at the moment of measurement, including other Ollama processes spinning up the next model. Resident is the more reliable number.
+
+By resident:
+
+- 270m / 0.5B-1B class: 1-4 GB
+- 1.5B-3B class: 2.5-7 GB
+- 4B class: 4.7-7.5 GB (q4_K_M), 9.3 GB (q8_0), 13.2 GB (fp16)
+
+For a 24 GB GPU you can comfortably run any single 4B model. For a 16 GB GPU, the q4_K_M variants are the only practical 4B options. For 8 GB cards, you're in 1.5B-3B territory.
+
+Phi-3.5's 16.6 GB resident is anomalously high for a 3.8B model. Likely a different KV-cache strategy. Worth a note in deployment docs for users on tight VRAM.
+
+## What to do next
+
+In rough priority order:
+
+1. **Update `mempalace/local_model.py::MODEL_TIERS` to the recommended list above.** The data justifies it and the comment can point at this report.
+2. **Run a Tier 1 subset on a second machine** (say a 16 GB card) to confirm the accuracy numbers are hardware-portable. Speed numbers won't be, but accuracy should be within 1-2%.
+3. **File a benchmark-issue or follow-up PR** to explore open-set discovery with a larger reference model (Ollama cloud `qwen3:30b-cloud` or `gpt-oss:20b-cloud`). The 0.612 ceiling on small models doesn't preclude shipping discovery on larger ones.
+4. **Investigate the qwen3:4b q4_K_M hallucination rate on memory extraction.** Either tighten the prompt or add a post-extraction filter pass.
+5. **Re-run llama3.2:3b calibration** to confirm or reject the p95 cliff.
+6. **Add `phi-4-mini` and `nemotron-mini` if available** (mentioned in the original ollama.com search but not in our matrix). Both have function-calling tunings that might help on entity/memory tasks.
+
+## Run reproducibility
+
+```bash
+# Pull all candidates (~7 min on a 100 Mbit connection)
+for m in $(grep -E "^  - tag:" benchmarks/model_eval/candidates.yaml | awk '{print $3}'); do
+  ollama pull "$m"
+done
+ollama pull nomic-embed-text
+
+# Run the matrix (~60 min on RTX 3090)
+python -m benchmarks.model_eval.orchestrator \
+  --candidates all --tasks all \
+  --dataset-dir benchmarks/model_eval/datasets \
+  --output benchmarks/model_eval/results/$(date -u +%Y-%m-%d)-$(hostname).csv
+
+# Render the report
+python -m benchmarks.model_eval.summarize \
+  --csv benchmarks/model_eval/results/$(date -u +%Y-%m-%d)-$(hostname).csv \
+  --output benchmarks/model_eval/reports/$(date -u +%Y-%m-%d)-$(hostname).md
+```
+
+Datasets are synthetic and committed; no external state is needed beyond a working Ollama install with GPU.

benchmarks/model_eval/reports/2026-05-10-z690-ex-glacial.md

@@ -0,0 +1,181 @@
+# Model evaluation report
+
+- Host: `z690-ex-glacial
+`
+- GPU: `NVIDIA GeForce RTX 3090`
+- Ollama: `ollama version is 0.23.2`
+- Run date: 2026-05-10 15:22:11 UTC
+- Runs: 75 (15 models × task/mode pairs); errors: 1
+
+## Production picks
+
+_No model met both thresholds (min_acc=0.8, max_e2e=500ms across all tasks)._
+
+
+## Open-set discovery viability
+
+Open-set discovery viability. Threshold: mean cosine similarity ≥ 0.7.
+
+**No model met the threshold.** Best score: 0.612.
+
+Recommendation: do NOT ship `mempalace mine --mode discover`. Closed-set classification stays required.
+
+
+## Instruct vs reasoning (qwen3:4b)
+
+_Not enough qwen3:4b paired results to compare._
+
+
+## Per-task rankings
+
+### Calibration (sentence-type, exact match)
+
+| Rank | Model | accuracy | e2e p50 ms | TPS p50 | VRAM resident MB |
+|---|---|---|---|---|---|
+| 1 | qwen3:4b-instruct-2507-q4_K_M | 0.950 | 85.8 | 282.6 | 7481 |
+| 2 | qwen3:4b-instruct-2507-q8_0 | 0.950 | 85.2 | 218.1 | 9271 |
+| 3 | llama3.2:3b-instruct-q4_K_M | 0.950 | 92.4 | 322.1 | 7126 |
+| 4 | phi3.5:3.8b-mini-instruct-q4_K_M | 0.950 | 30.4 | 352.8 | 16564 |
+| 5 | qwen3:1.7b-q4_K_M | 0.950 | 75.3 | 472.9 | 5024 |
+| 6 | qwen3:4b-instruct-2507-fp16 | 0.950 | 94.2 | 153.8 | 13215 |
+| 7 | qwen2.5:3b-instruct-q4_K_M | 0.900 | 100.5 | 166.7 | 4082 |
+| 8 | gemma3:4b-it-q4_K_M | 0.900 | 214.5 | 106.5 | 4745 |
+| 9 | gemma3:4b-it-qat | 0.900 | 116.6 | 249.2 | 6146 |
+| 10 | qwen2.5:1.5b-instruct-q4_K_M | 0.900 | 98.1 | 192.2 | 2668 |
+| 11 | gemma3:1b-it-q4_K_M | 0.800 | 174.1 | 218.7 | 1416 |
+| 12 | llama3.2:1b-instruct-q4_K_M | 0.550 | 107.9 | 182.7 | 3914 |
+| 13 | qwen2.5:0.5b-instruct-q4_K_M | 0.350 | 93.9 | 323.8 | 1721 |
+| 14 | qwen3:0.6b-q4_K_M | 0.250 | 77.0 | 418.9 | 4220 |
+| 15 | gemma3:270m-it-q8_0 | 0.050 | 108.6 | 254.7 | 742 |
+
+
+### Room classification — closed-set (exact match)
+
+| Rank | Model | accuracy | e2e p50 ms | TPS p50 | VRAM resident MB |
+|---|---|---|---|---|---|
+| 1 | qwen3:4b-instruct-2507-fp16 | 0.650 | 129.6 | 104.7 | 13215 |
+| 2 | qwen3:4b-instruct-2507-q8_0 | 0.640 | 118.2 | 152.7 | 9271 |
+| 3 | qwen3:4b-instruct-2507-q4_K_M | 0.610 | 109.1 | 206.4 | 7481 |
+| 4 | gemma3:4b-it-qat | 0.540 | 197.0 | 159.1 | 6146 |
+| 5 | gemma3:4b-it-q4_K_M | 0.520 | 222.2 | 169.3 | 4745 |
+| 6 | phi3.5:3.8b-mini-instruct-q4_K_M | 0.470 | 66.3 | 237.0 | 16564 |
+| 7 | qwen2.5:3b-instruct-q4_K_M | 0.390 | 110.2 | 254.9 | 4082 |
+| 8 | qwen2.5:1.5b-instruct-q4_K_M | 0.390 | 101.0 | 339.7 | 2668 |
+| 9 | qwen3:1.7b-q4_K_M | 0.280 | 95.1 | 322.1 | 5024 |
+| 10 | llama3.2:3b-instruct-q4_K_M | 0.270 | 119.3 | 269.5 | 7126 |
+| 11 | qwen3:0.6b-q4_K_M | 0.250 | 84.0 | 429.0 | 4220 |
+| 12 | gemma3:1b-it-q4_K_M | 0.220 | 209.8 | 155.3 | 1416 |
+| 13 | llama3.2:1b-instruct-q4_K_M | 0.220 | 107.2 | 349.2 | 3914 |
+| 14 | qwen2.5:0.5b-instruct-q4_K_M | 0.220 | 91.3 | 522.9 | 1721 |
+| 15 | gemma3:270m-it-q8_0 | 0.122 | 153.7 | 304.7 | 742 |
+
+
+### Room classification — open-set (cosine similarity)
+
+| Rank | Model | accuracy | exact_match_count | high_similarity_count | low_similarity_count | mean_similarity | e2e p50 ms | TPS p50 | VRAM resident MB |
+|---|---|---|---|---|---|---|---|---|---|
+| 1 | gemma3:4b-it-q4_K_M | 0.612 | 2.00 | 14.00 | 32.00 | 0.61 | 213.1 | 171.5 | 4745 |
+| 2 | gemma3:4b-it-qat | 0.612 | 3.00 | 15.00 | 31.00 | 0.61 | 155.9 | 163.9 | 6146 |
+| 3 | qwen3:4b-instruct-2507-fp16 | 0.595 | 0.00 | 9.00 | 33.00 | 0.59 | 152.6 | 98.8 | 13215 |
+| 4 | qwen3:4b-instruct-2507-q8_0 | 0.593 | 0.00 | 8.00 | 33.00 | 0.59 | 129.4 | 146.8 | 9271 |
+| 5 | qwen2.5:1.5b-instruct-q4_K_M | 0.592 | 0.00 | 12.00 | 35.00 | 0.59 | 122.4 | 249.7 | 2668 |
+| 6 | phi3.5:3.8b-mini-instruct-q4_K_M | 0.592 | 0.00 | 5.00 | 25.00 | 0.59 | 92.7 | 229.4 | 16564 |
+| 7 | qwen2.5:3b-instruct-q4_K_M | 0.588 | 0.00 | 8.00 | 34.00 | 0.59 | 122.0 | 241.5 | 4082 |
+| 8 | qwen3:4b-instruct-2507-q4_K_M | 0.586 | 0.00 | 10.00 | 35.00 | 0.59 | 116.2 | 199.9 | 7481 |
+| 9 | qwen2.5:0.5b-instruct-q4_K_M | 0.575 | 1.00 | 16.00 | 42.00 | 0.57 | 111.4 | 285.9 | 1721 |
+| 10 | gemma3:1b-it-q4_K_M | 0.548 | 1.00 | 5.00 | 57.00 | 0.55 | 195.3 | 171.1 | 1416 |
+| 11 | llama3.2:3b-instruct-q4_K_M | 0.547 | 1.00 | 8.00 | 46.00 | 0.55 | 125.5 | 245.4 | 7126 |
+| 12 | qwen3:1.7b-q4_K_M | 0.531 | 1.00 | 6.00 | 58.00 | 0.53 | 98.5 | 306.9 | 5024 |
+| 13 | llama3.2:1b-instruct-q4_K_M | 0.486 | 0.00 | 6.00 | 71.00 | 0.49 | 127.1 | 219.0 | 3914 |
+| 14 | qwen3:0.6b-q4_K_M | 0.455 | 0.00 | 3.00 | 79.00 | 0.46 | 108.8 | 177.6 | 4220 |
+| 15 | gemma3:270m-it-q8_0 | 0.381 | 0.00 | 0.00 | 96.00 | 0.38 | 199.8 | 193.8 | 742 |
+
+
+### Entity extraction (mean F1)
+
+| Rank | Model | accuracy | mean_f1 | mean_precision | mean_recall | valid_json_rate | e2e p50 ms | TPS p50 | VRAM resident MB |
+|---|---|---|---|---|---|---|---|---|---|
+| 1 | qwen3:4b-instruct-2507-q4_K_M | 0.778 | 0.78 | 0.78 | 0.80 | 1.00 | 624.0 | 172.3 | 7481 |
+| 2 | qwen3:4b-instruct-2507-fp16 | 0.772 | 0.77 | 0.79 | 0.78 | 1.00 | 1063.1 | 86.3 | 13215 |
+| 3 | qwen3:4b-instruct-2507-q8_0 | 0.766 | 0.77 | 0.78 | 0.78 | 1.00 | 777.4 | 128.3 | 9271 |
+| 4 | gemma3:4b-it-qat | 0.761 | 0.76 | 0.76 | 0.79 | 1.00 | 834.5 | 150.4 | 6146 |
+| 5 | gemma3:4b-it-q4_K_M | 0.743 | 0.74 | 0.74 | 0.77 | 1.00 | 902.4 | 163.1 | 4745 |
+| 6 | qwen2.5:3b-instruct-q4_K_M | 0.679 | 0.68 | 0.69 | 0.70 | 1.00 | 2139.3 | 105.9 | 4082 |
+| 7 | llama3.2:3b-instruct-q4_K_M | 0.672 | 0.67 | 0.74 | 0.63 | 1.00 | 1786.6 | 105.6 | 7126 |
+| 8 | phi3.5:3.8b-mini-instruct-q4_K_M | 0.641 | 0.64 | 0.68 | 0.64 | 1.00 | 816.5 | 213.0 | 16564 |
+| 9 | gemma3:1b-it-q4_K_M | 0.608 | 0.61 | 0.55 | 0.71 | 1.00 | 707.4 | 277.9 | 1416 |
+| 10 | qwen3:0.6b-q4_K_M | 0.480 | 0.48 | 0.57 | 0.47 | 1.00 | 535.4 | 176.3 | 4220 |
+| 11 | qwen2.5:0.5b-instruct-q4_K_M | 0.460 | 0.46 | 0.46 | 0.50 | 1.00 | 2267.0 | 91.4 | 1721 |
+| 12 | qwen2.5:1.5b-instruct-q4_K_M | 0.367 | 0.37 | 0.39 | 0.35 | 1.00 | 1428.6 | 112.0 | 2668 |
+| 13 | llama3.2:1b-instruct-q4_K_M | 0.361 | 0.36 | 0.90 | 0.24 | 1.00 | 528.2 | 121.8 | 3914 |
+| 14 | qwen3:1.7b-q4_K_M | 0.313 | 0.31 | 0.67 | 0.23 | 1.00 | 198.6 | 299.6 | 5024 |
+
+
+### Memory extraction (mean coverage)
+
+| Rank | Model | accuracy | mean_coverage | mean_hallucination_rate | mean_type_accuracy | valid_json_rate | e2e p50 ms | TPS p50 | VRAM resident MB |
+|---|---|---|---|---|---|---|---|---|---|
+| 1 | qwen3:4b-instruct-2507-q4_K_M | 0.950 | 0.95 | 0.36 | 0.89 | 1.00 | 537.5 | 173.0 | 7481 |
+| 2 | qwen3:4b-instruct-2507-q8_0 | 0.938 | 0.94 | 0.28 | 0.89 | 1.00 | 638.7 | 129.7 | 9271 |
+| 3 | qwen3:4b-instruct-2507-fp16 | 0.938 | 0.94 | 0.32 | 0.88 | 1.00 | 921.2 | 86.7 | 13215 |
+| 4 | gemma3:4b-it-q4_K_M | 0.912 | 0.91 | 0.19 | 0.90 | 1.00 | 633.4 | 165.1 | 4745 |
+| 5 | phi3.5:3.8b-mini-instruct-q4_K_M | 0.912 | 0.91 | 0.17 | 0.95 | 1.00 | 765.3 | 211.4 | 16564 |
+| 6 | qwen2.5:1.5b-instruct-q4_K_M | 0.912 | 0.91 | 0.31 | 0.89 | 1.00 | 1625.0 | 103.8 | 2668 |
+| 7 | gemma3:4b-it-qat | 0.887 | 0.89 | 0.19 | 0.96 | 1.00 | 601.4 | 152.5 | 6146 |
+| 8 | qwen2.5:3b-instruct-q4_K_M | 0.838 | 0.84 | 0.00 | 0.94 | 1.00 | 1123.2 | 105.0 | 4082 |
+| 9 | qwen3:1.7b-q4_K_M | 0.838 | 0.84 | 0.02 | 0.82 | 1.00 | 279.7 | 306.5 | 5024 |
+| 10 | llama3.2:3b-instruct-q4_K_M | 0.812 | 0.81 | 0.05 | 0.91 | 1.00 | 821.1 | 107.9 | 7126 |
+| 11 | llama3.2:1b-instruct-q4_K_M | 0.762 | 0.76 | 0.22 | 0.55 | 1.00 | 1164.6 | 114.1 | 3914 |
+| 12 | qwen3:0.6b-q4_K_M | 0.738 | 0.74 | 0.07 | 0.42 | 1.00 | 327.1 | 191.5 | 4220 |
+| 13 | gemma3:1b-it-q4_K_M | 0.731 | 0.73 | 0.22 | 0.67 | 0.97 | 469.3 | 272.7 | 1416 |
+| 14 | qwen2.5:0.5b-instruct-q4_K_M | 0.113 | 0.11 | 0.03 | 0.01 | 0.12 | 757.0 | 99.3 | 1721 |
+| 15 | gemma3:270m-it-q8_0 | 0.025 | 0.03 | 0.97 | 0.00 | 1.00 | 358.3 | 170.6 | 742 |
+
+
+## Speed (calibration, smallest task — most stable timing)
+
+| Rank | Model | e2e p50 ms | e2e p95 ms | TTFT p50 ms | TPS p50 | TPS p95 |
+|---|---|---|---|---|---|---|
+| 1 | phi3.5:3.8b-mini-instruct-q4_K_M | 30.4 | 39.7 | 19.4 | 352.8 | 369.9 |
+| 2 | qwen3:1.7b-q4_K_M | 75.3 | 79.9 | 67.5 | 472.9 | 502.6 |
+| 3 | qwen3:0.6b-q4_K_M | 77.0 | 84.0 | 68.4 | 418.9 | 448.1 |
+| 4 | qwen3:4b-instruct-2507-q8_0 | 85.2 | 90.2 | 71.2 | 218.1 | 239.9 |
+| 5 | qwen3:4b-instruct-2507-q4_K_M | 85.8 | 92.5 | 73.0 | 282.6 | 312.0 |
+| 6 | llama3.2:3b-instruct-q4_K_M | 92.4 | 5050.2 | 81.2 | 322.1 | 390.9 |
+| 7 | qwen2.5:0.5b-instruct-q4_K_M | 93.9 | 107.8 | 80.1 | 323.8 | 391.6 |
+| 8 | qwen3:4b-instruct-2507-fp16 | 94.2 | 102.1 | 72.6 | 153.8 | 165.7 |
+| 9 | qwen2.5:1.5b-instruct-q4_K_M | 98.1 | 105.6 | 78.6 | 192.2 | 222.6 |
+| 10 | qwen2.5:3b-instruct-q4_K_M | 100.5 | 111.7 | 81.4 | 166.7 | 198.0 |
+| 11 | llama3.2:1b-instruct-q4_K_M | 107.9 | 139.9 | 90.6 | 182.7 | 238.6 |
+| 12 | gemma3:270m-it-q8_0 | 108.6 | 144.6 | 78.5 | 254.7 | 382.9 |
+| 13 | gemma3:4b-it-qat | 116.6 | 132.5 | 83.4 | 249.2 | 266.2 |
+| 14 | gemma3:1b-it-q4_K_M | 174.1 | 183.5 | 161.2 | 218.7 | 319.8 |
+| 15 | gemma3:4b-it-q4_K_M | 214.5 | 233.0 | 186.9 | 106.5 | 123.6 |
+
+
+## VRAM
+
+| Model | Resident MB | Peak MB | Delta MB |
+|---|---|---|---|
+| gemma3:270m-it-q8_0 | 742 | 5546 | 4804 |
+| gemma3:1b-it-q4_K_M | 1416 | 9613 | 8197 |
+| qwen2.5:0.5b-instruct-q4_K_M | 1721 | 9126 | 7405 |
+| qwen2.5:1.5b-instruct-q4_K_M | 2668 | 10271 | 7603 |
+| llama3.2:1b-instruct-q4_K_M | 3914 | 12213 | 8299 |
+| qwen2.5:3b-instruct-q4_K_M | 4082 | 16513 | 12431 |
+| qwen3:0.6b-q4_K_M | 4220 | 22707 | 18487 |
+| gemma3:4b-it-q4_K_M | 4745 | 17377 | 12632 |
+| qwen3:1.7b-q4_K_M | 5024 | 23417 | 18393 |
+| gemma3:4b-it-qat | 6146 | 13430 | 7284 |
+| llama3.2:3b-instruct-q4_K_M | 7126 | 11225 | 4099 |
+| qwen3:4b-instruct-2507-q4_K_M | 7481 | 22961 | 15480 |
+| qwen3:4b-instruct-2507-q8_0 | 9271 | 20336 | 11065 |
+| qwen3:4b-instruct-2507-fp16 | 13215 | 16942 | 3727 |
+| phi3.5:3.8b-mini-instruct-q4_K_M | 16564 | 18595 | 2031 |
+
+
+## Errors
+
+| Model | Task | Mode | Error |
+|---|---|---|---|
+| gemma3:270m-it-q8_0 | entity_extraction | default | Warmup failed: Cannot reach http://localhost:11434/api/chat: timed out |

benchmarks/model_eval/reports/2026-05-13-multilingual.md

@@ -0,0 +1,158 @@
+# Multilingual Benchmark — 2026-05-13 (Mercurio)
+
+**6 local models × 7 languages × 5 tasks = 210 runs**
+Hardware: RTX 3080 Laptop 8 GB · Ollama 0.23.3
+Embed model: `nomic-embed-text` · Dataset: n=20 (calibration), n=40 (memory/entity), n=101 (room)
+
+> **Methodology note:** this run predates the `--num-ctx 4096` default. Each model used its own Modelfile context window (32k for the Gemma4 variants, larger for qwen3), which means VRAM and latency numbers across families aren't strictly apples-to-apples. Accuracy is unaffected — the prompts are well under any model's window — but expect e2e and VRAM to drop in subsequent runs with the new default.
+
+---
+
+## Models
+
+| short name      | tag                                          | quant  |
+|:--------------- |:-------------------------------------------- |:------:|
+| qwen3-4b-q8     | qwen3:4b-instruct-2507-q8_0                  | q8_0   |
+| gemma4-e4b-q4   | gemma4:e4b-it-q4_K_M                         | q4_K_M |
+| gemma4-e4b      | gemma4:e4b                                   | q4_K_M |
+| classifier-q8   | igorls/gemma4-e4b-classifier:Q8_0            | q8_0   |
+| classifier-q4   | igorls/gemma4-e4b-classifier:latest          | q4_K_M |
+| heretic-q4      | igorls/gemma-4-E4B-it-heretic-GGUF:Q4_K_M    | q4_K_M |
+
+---
+
+## Overall (all tasks × all locales)
+
+| model          | EN    | non-EN avg | all avg | calib fastest |
+|:-------------- |:-----:|:----------:|:-------:|:-------------:|
+| classifier-q8  | 0.798 | 0.674      | **0.691** | 354 ms      |
+| classifier-q4  | 0.792 | 0.659      | 0.678   | 282 ms        |
+| gemma4-e4b     | 0.790 | 0.655      | 0.675   | 324 ms        |
+| gemma4-e4b-q4  | 0.784 | 0.655      | 0.673   | 312 ms        |
+| qwen3-4b-q8    | 0.781 | 0.645      | 0.665   | **161 ms**    |
+| heretic-q4     | 0.787 | 0.644      | 0.664   | 272 ms        |
+
+`classifier-q8` leads on accuracy (+2.6 pp over heretic on all avg).
+`qwen3-4b-q8` is 2–3× faster on simple tasks and ranks 3rd on accuracy.
+`gemma4-e4b` and `gemma4-e4b-q4` are statistically equivalent (within noise).
+
+---
+
+## By task
+
+### Room Classification — closed-set
+
+| model          |  en   |  de   |  fr   |  hi   |  it   |  ko   |  ru   |  avg  |
+|:-------------- |:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
+| classifier-q8  | 0.624 | 0.604 | 0.604 | 0.624 | 0.604 | 0.634 | 0.624 | **0.617** |
+| classifier-q4  | 0.644 | 0.584 | 0.594 | 0.554 | 0.584 | 0.604 | 0.604 | 0.596 |
+| gemma4-e4b     | 0.624 | 0.574 | 0.584 | 0.554 | 0.584 | 0.604 | 0.594 | 0.588 |
+| gemma4-e4b-q4  | 0.604 | 0.594 | 0.594 | 0.554 | 0.584 | 0.604 | 0.604 | 0.591 |
+| heretic-q4     | 0.624 | 0.594 | 0.594 | 0.545 | 0.564 | 0.594 | 0.604 | 0.588 |
+| qwen3-4b-q8    | 0.624 | 0.564 | 0.554 | 0.554 | 0.535 | 0.554 | 0.564 | 0.564 |
+
+Average EN→non-EN drop: ~3–6 pp. Uniform distribution across languages — no outlier locale.
+
+### Room Classification — open-set
+
+| model          |  en   |  de   |  fr   |  hi   |  it   |  ko   |  ru   |  avg  |
+|:-------------- |:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
+| classifier-q8  | 0.678 | 0.637 | 0.634 | 0.651 | 0.644 | 0.645 | 0.633 | **0.646** |
+| gemma4-e4b     | 0.657 | 0.647 | 0.642 | 0.630 | 0.637 | 0.648 | 0.642 | **0.643** |
+| gemma4-e4b-q4  | 0.655 | 0.644 | 0.633 | 0.634 | 0.632 | 0.647 | 0.640 | 0.641 |
+| classifier-q4  | 0.655 | 0.651 | 0.632 | 0.622 | 0.626 | 0.636 | 0.643 | 0.638 |
+| heretic-q4     | 0.627 | 0.605 | 0.603 | 0.629 | 0.601 | 0.639 | 0.644 | 0.621 |
+| qwen3-4b-q8    | 0.603 | 0.572 | 0.562 | 0.599 | 0.570 | 0.581 | 0.559 | 0.578 |
+
+Open-set is more stable across languages than closed-set — cosine similarity absorbs phrasing variation better than exact match.
+Gemma4 clearly leads; qwen3 trails by ~6 pp.
+
+### Entity Extraction
+
+| model          |  en   |  de   |  fr   |  hi   |  it   |  ko   |  ru   |  avg  |
+|:-------------- |:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
+| heretic-q4     | 0.782 | 0.701 | 0.771 | 0.751 | **0.792** | 0.733 | 0.729 | **0.751** |
+| qwen3-4b-q8    | 0.777 | 0.732 | **0.799** | 0.764 | **0.801** | 0.770 | 0.758 | **0.771** |
+| classifier-q8  | 0.763 | 0.709 | 0.761 | 0.754 | 0.763 | 0.736 | 0.745 | 0.747 |
+| gemma4-e4b     | 0.759 | 0.676 | 0.761 | 0.745 | 0.773 | 0.709 | 0.726 | 0.736 |
+| gemma4-e4b-q4  | 0.748 | 0.663 | 0.760 | 0.736 | 0.773 | 0.712 | 0.729 | 0.732 |
+| classifier-q4  | 0.723 | 0.680 | 0.756 | 0.733 | 0.745 | 0.698 | 0.708 | 0.720 |
+
+The most robust task across languages — only a ~3–5 pp EN→non-EN drop.
+**qwen3** and **heretic** tie for the lead. FR and IT often beat EN (likely an effect of richer training data in those languages).
+KO and DE are the hardest languages here.
+
+### Memory Extraction ⚠️
+
+| model          |  en   |  de   |  fr   |  hi   |  it   |  ko   |  ru   | drop EN→avg |
+|:-------------- |:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----------:|
+| qwen3-4b-q8    | **0.950** | 0.287 | 0.438 | 0.463 | 0.463 | 0.400 | 0.212 | **−0.573**  |
+| heretic-q4     | **0.950** | 0.225 | 0.425 | 0.350 | 0.438 | 0.312 | 0.163 | **−0.631**  |
+| classifier-q4  | 0.938 | 0.325 | 0.487 | 0.438 | 0.475 | 0.400 | 0.225 | −0.546      |
+| classifier-q8  | 0.925 | 0.412 | 0.450 | 0.438 | **0.500** | 0.438 | 0.212 | −0.517      |
+| gemma4-e4b     | 0.912 | 0.312 | 0.450 | 0.400 | 0.450 | 0.375 | 0.188 | −0.550      |
+| gemma4-e4b-q4  | 0.912 | 0.312 | 0.438 | 0.400 | 0.463 | 0.362 | 0.188 | −0.552      |
+
+**This is the critical task.** Every model collapses ~0.52–0.63 pp from EN to non-EN.
+`classifier-q8` has the smallest drop (−0.517) and the best non-EN absolute (0.375 avg).
+RU and DE are the worst — likely an embedding artifact (`nomic-embed-text` has weak signal on EN↔RU/DE pairs in memory extraction, as documented in PR #1483).
+
+> **Methodology note**: memory_extraction scores use cosine similarity via `nomic-embed-text`. For distant language pairs (RU, DE), the embedding model may be underestimating real coverage — see PR #1483 for a comparison with `embeddinggemma`.
+>
+> A follow-up methodology fix in this PR adds `labels.ko.jsonl` so KO scores are computed against Korean ground truth instead of English. The numbers above predate that change; expect KO `memory_extraction` to recover meaningfully once `labels.{lang}.jsonl` exists for every language.
+
+### Calibration
+
+| model          |  en   |  de   |  fr   |  hi   |  it   |  ko   |  ru   |  avg  |
+|:-------------- |:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
+| gemma4-e4b     | 1.000 | 0.950 | 0.950 | 0.950 | 1.000 | 0.950 | 0.950 | 0.964 |
+| gemma4-e4b-q4  | 1.000 | 0.950 | 0.950 | 0.950 | 1.000 | 0.950 | 0.950 | 0.964 |
+| classifier-q8  | 1.000 | 0.950 | 0.950 | 0.950 | 1.000 | 0.950 | 0.950 | 0.964 |
+| classifier-q4  | 1.000 | 0.950 | 0.950 | 0.950 | 1.000 | 0.950 | 0.950 | 0.964 |
+| qwen3-4b-q8    | 0.950 | 0.950 | 0.950 | 0.950 | 0.950 | 0.950 | 0.950 | 0.950 |
+| heretic-q4     | 0.950 | 0.950 | 0.950 | 0.950 | 0.950 | 0.950 | 0.950 | 0.950 |
+
+Calibration is effectively language-agnostic — clean signal, no surprises.
+
+---
+
+## Per-language ranking (all-tasks avg)
+
+| locale | best model     | score | worst model   | score |
+|:------:|:-------------- |:-----:|:------------- |:-----:|
+| en     | classifier-q8  | 0.798 | qwen3-4b-q8   | 0.781 |
+| de     | classifier-q8  | 0.681 | heretic-q4    | 0.615 |
+| fr     | classifier-q8  | 0.688 | qwen3-4b-q8   | 0.661 |
+| hi     | classifier-q8  | 0.683 | heretic-q4    | 0.645 |
+| it     | classifier-q8  | 0.700 | qwen3-4b-q8   | 0.676 |
+| ko     | classifier-q8  | 0.680 | heretic-q4    | 0.647 |
+| ru     | classifier-q8  | 0.641 | heretic-q4    | 0.608 |
+
+`classifier-q8` leads in all 7 languages. RU is globally the hardest language.
+
+---
+
+## Speed (e2e_p50 ms — calibration as a baseline-latency proxy)
+
+| model          | en   | de   | fr   | hi   | it   | ko   | ru   |
+|:-------------- |:----:|:----:|:----:|:----:|:----:|:----:|:----:|
+| qwen3-4b-q8    | 253  | 280  | 246  | 190  | 179  | 168  | 161  |
+| heretic-q4     | 441  | 608  | 610  | 312  | 272  | 285  | 300  |
+| classifier-q4  | 556  | 582  | 630  | 287  | 329  | 282  | 323  |
+| gemma4-e4b     | 632  | 623  | 587  | 397  | 337  | 324  | 367  |
+| gemma4-e4b-q4  | 633  | 633  | 610  | 459  | 312  | 434  | 366  |
+| classifier-q8  | 662  | 643  | 669  | 437  | 433  | 395  | 354  |
+
+`qwen3-4b-q8` is **2.5–4× faster** than every Gemma4 variant on baseline latency, despite running at q8_0. Non-Latin scripts (HI, KO, RU) generate fewer tokens per prompt, which is why their latencies are lower.
+
+---
+
+## Recommendations
+
+**For production (best accuracy):** `classifier-q8` — leads in every language and has the smallest non-EN drop on memory_extraction. Cost: 2× slower than qwen3.
+
+**For edge / tight-8 GB tier:** `classifier-q4` or `qwen3-4b-q8` — close accuracy, 2–3× faster. qwen3 dominates entity extraction; classifier-q4 dominates room-open.
+
+**gemma4-e4b vs gemma4-e4b-q4:** difference < 0.003 across every score — within statistical noise. Prefer `q4_K_M` to save ~2 GB of VRAM.
+
+**Non-EN memory extraction:** the collapse is universal (−0.5 to −0.63 pp). Before discarding any model, re-run with `--embed-model embeddinggemma` (see PR #1483) to separate scoring effects from model behavior, and ensure `labels.{lang}.jsonl` exists for every language so the ground truth is in the right language.

benchmarks/model_eval/reports/README.md

@@ -0,0 +1,5 @@
+# Reports directory
+
+Hand-written markdown analyses go here. After each benchmark run, render a summary table from the CSV and write a narrative interpretation: which models won, where the surprises were, which entries belong in the next `MODEL_TIERS` revision.
+
+Reports are committed (unlike the raw CSVs in `results/`).

benchmarks/model_eval/results/.gitignore

@@ -0,0 +1,12 @@
+*.csv
+!.gitignore
+!README.md
+# Committed baselines (per-machine, dated). Add explicit exceptions
+# when a particular run is worth attaching to history (analysis report
+# committed alongside, etc.). Future runs on other machines remain
+# gitignored by default.
+!2026-05-10-z690-ex-glacial.csv
+!2026-05-10-spotcheck-qwen3.csv
+!2026-05-10-cloud-z690-ex-glacial.csv
+!2026-05-11-cloud-z690-ex-glacial.csv
+!2026-05-11-modern-z690-ex-glacial.csv

benchmarks/model_eval/results/2026-05-10-cloud-z690-ex-glacial.csv

@@ -0,0 +1,31 @@
+model_tag,task,mode,n_samples,accuracy,ttft_p50_ms,ttft_p95_ms,tps_p50,tps_p95,e2e_p50_ms,e2e_p95_ms,vram_resident_mb,vram_peak_mb,host,gpu,ollama_version,run_date,error,extras_json
+gpt-oss:20b-cloud,room_classification,closed,30,0.8333,0.0,0.0,0.0,0.0,1144.5,1633.3,,2753,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:42:14 UTC,,"{""correct"":25,""total"":30}"
+gpt-oss:20b-cloud,room_classification,open,30,0.5548,0.0,0.0,0.0,0.0,1095.4,2062.3,,3567,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:42:52 UTC,,"{""mean_similarity"":0.5548128518611897,""exact_match_count"":0,""high_similarity_count"":3,""low_similarity_count"":13}"
+gpt-oss:20b-cloud,entity_extraction,default,29,0.7549,0.0,0.0,0.0,0.0,3142.3,10924.7,,3996,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:43:36 UTC,,"{""mean_f1"":0.7549275487569498,""mean_precision"":0.7325944170771757,""mean_recall"":0.7908319649698959,""valid_json_rate"":1.0,""error_count"":1,""error_sample"":""Cannot reach http://localhost:11434/api/chat: timed out""}"
+gpt-oss:20b-cloud,memory_extraction,default,30,1.0,0.0,0.0,0.0,0.0,2476.5,3877.3,,3594,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:49:21 UTC,,"{""mean_coverage"":1.0,""mean_hallucination_rate"":0.5155555555555555,""mean_type_accuracy"":0.9,""valid_json_rate"":1.0}"
+gpt-oss:20b-cloud,calibration,default,20,0.9,0.0,0.0,0.0,0.0,815.9,1100.2,,3571,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:50:47 UTC,,"{""correct"":18,""total"":20}"
+gpt-oss:120b-cloud,room_classification,closed,30,0.8,0.0,0.0,0.0,0.0,1656.1,2317.9,,3603,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:51:06 UTC,,"{""correct"":24,""total"":30}"
+gpt-oss:120b-cloud,room_classification,open,30,0.5529,0.0,0.0,0.0,0.0,1421.0,1807.9,,3580,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:51:57 UTC,,"{""mean_similarity"":0.5528842080396624,""exact_match_count"":0,""high_similarity_count"":3,""low_similarity_count"":14}"
+gpt-oss:120b-cloud,entity_extraction,default,30,0.8292,0.0,0.0,0.0,0.0,3550.8,5124.2,,3571,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:52:46 UTC,,"{""mean_f1"":0.8291977515661727,""mean_precision"":0.803968253968254,""mean_recall"":0.8731216931216931,""valid_json_rate"":1.0}"
+gpt-oss:120b-cloud,memory_extraction,default,30,1.0,0.0,0.0,0.0,0.0,2718.8,4222.1,,3570,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:54:41 UTC,,"{""mean_coverage"":1.0,""mean_hallucination_rate"":0.5083333333333333,""mean_type_accuracy"":0.9,""valid_json_rate"":1.0}"
+gpt-oss:120b-cloud,calibration,default,20,0.95,0.0,0.0,0.0,0.0,983.0,1188.4,,3569,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:56:19 UTC,,"{""correct"":19,""total"":20}"
+qwen3-coder:480b-cloud,room_classification,closed,30,0.9,0.0,0.0,0.0,0.0,598.7,2558.9,,3551,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:56:40 UTC,,"{""correct"":27,""total"":30}"
+qwen3-coder:480b-cloud,room_classification,open,30,0.5865,0.0,0.0,0.0,0.0,778.1,2397.5,,3653,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:57:10 UTC,,"{""mean_similarity"":0.5865002016051934,""exact_match_count"":1,""high_similarity_count"":4,""low_similarity_count"":13}"
+qwen3-coder:480b-cloud,entity_extraction,default,30,0.8036,0.0,0.0,0.0,0.0,3108.2,8487.9,,4472,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:57:43 UTC,,"{""mean_f1"":0.803606549396023,""mean_precision"":0.7605952380952381,""mean_recall"":0.8820634920634921,""valid_json_rate"":1.0}"
+qwen3-coder:480b-cloud,memory_extraction,default,30,0.9667,0.0,0.0,0.0,0.0,5870.2,16020.9,,4543,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:59:47 UTC,,"{""mean_coverage"":0.9666666666666667,""mean_hallucination_rate"":0.5183333333333333,""mean_type_accuracy"":0.95,""valid_json_rate"":1.0}"
+qwen3-coder:480b-cloud,calibration,default,20,0.95,0.0,0.0,0.0,0.0,607.0,3773.7,,4479,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 00:03:29 UTC,,"{""correct"":19,""total"":20}"
+deepseek-v3.1:671b-cloud,room_classification,closed,30,0.8,0.0,0.0,0.0,0.0,735.4,6291.8,,4473,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 00:03:51 UTC,,"{""correct"":24,""total"":30}"
+deepseek-v3.1:671b-cloud,room_classification,open,30,0.5656,0.0,0.0,0.0,0.0,834.8,1529.1,,4471,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 00:04:33 UTC,,"{""mean_similarity"":0.5655551791849553,""exact_match_count"":0,""high_similarity_count"":3,""low_similarity_count"":14}"
+deepseek-v3.1:671b-cloud,entity_extraction,default,30,0.8284,0.0,0.0,0.0,0.0,1562.6,2367.7,,4473,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 00:05:06 UTC,,"{""mean_f1"":0.8284154442049179,""mean_precision"":0.8171031746031746,""mean_recall"":0.8696031746031746,""valid_json_rate"":1.0}"
+deepseek-v3.1:671b-cloud,memory_extraction,default,30,0.9667,0.0,0.0,0.0,0.0,1575.2,2971.7,,4470,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 00:05:58 UTC,,"{""mean_coverage"":0.9666666666666667,""mean_hallucination_rate"":0.3,""mean_type_accuracy"":0.9666666666666667,""valid_json_rate"":1.0}"
+deepseek-v3.1:671b-cloud,calibration,default,20,0.95,0.0,0.0,0.0,0.0,677.7,927.6,,3734,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 00:06:57 UTC,,"{""correct"":19,""total"":20}"
+kimi-k2:1t-cloud,room_classification,closed,0,0.0,0,0,0,0,0,0,,,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 00:07:12 UTC,"Warmup failed: HTTP 500 from http://localhost:11434/api/chat: {""error"":""Internal Server Error (ref: b000e459-47d1-4d28-9179-0af21c4265fb)""}
+",{}
+kimi-k2:1t-cloud,room_classification,open,0,0.0,0,0,0,0,0,0,,,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 00:07:13 UTC,"Warmup failed: HTTP 500 from http://localhost:11434/api/chat: {""error"":""Internal Server Error (ref: 108f5dd2-c14e-4e0c-9d02-58749c6b3a8b)""}
+",{}
+kimi-k2:1t-cloud,entity_extraction,default,0,0.0,0,0,0,0,0,0,,,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 00:07:13 UTC,"Warmup failed: HTTP 500 from http://localhost:11434/api/chat: {""error"":""Internal Server Error (ref: 11346d10-7795-49b8-ae0b-90a6a9456e5d)""}
+",{}
+kimi-k2:1t-cloud,memory_extraction,default,0,0.0,0,0,0,0,0,0,,,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 00:07:13 UTC,"Warmup failed: HTTP 500 from http://localhost:11434/api/chat: {""error"":""Internal Server Error (ref: 6fd29366-daf6-4f88-9155-525e60b5455c)""}
+",{}
+kimi-k2:1t-cloud,calibration,default,0,0.0,0,0,0,0,0,0,,,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 00:07:13 UTC,"Warmup failed: HTTP 500 from http://localhost:11434/api/chat: {""error"":""Internal Server Error (ref: c325834e-d18f-4406-956f-fedd34d23143)""}
+",{}

benchmarks/model_eval/results/2026-05-10-spotcheck-qwen3.csv

@@ -0,0 +1,6 @@
+model_tag,task,mode,n_samples,accuracy,ttft_p50_ms,ttft_p95_ms,tps_p50,tps_p95,e2e_p50_ms,e2e_p95_ms,vram_resident_mb,vram_peak_mb,host,gpu,ollama_version,run_date,error,extras_json
+qwen3:4b-instruct-2507-q4_K_M,room_classification,closed,101,0.604,81.5,87.1,214.2,233.4,109.9,122.3,7481,10071,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:33:55 UTC,,"{""correct"":61,""total"":101}"
+qwen3:4b-instruct-2507-q4_K_M,room_classification,open,101,0.5844,78.4,85.9,205.6,226.4,116.4,135.8,7481,10888,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:34:10 UTC,,"{""mean_similarity"":0.584397504296683,""exact_match_count"":0,""high_similarity_count"":9,""low_similarity_count"":35}"
+qwen3:4b-instruct-2507-q4_K_M,entity_extraction,default,50,0.7711,79.5,85.7,177.4,180.3,609.9,858.3,7481,10934,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:34:26 UTC,,"{""mean_f1"":0.7711421780833545,""mean_precision"":0.7772380952380952,""mean_recall"":0.7847619047619048,""valid_json_rate"":1.0}"
+qwen3:4b-instruct-2507-q4_K_M,memory_extraction,default,40,0.95,78.6,84.3,168.5,175.6,556.7,843.0,7481,11156,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:34:58 UTC,,"{""mean_coverage"":0.95,""mean_hallucination_rate"":0.3625,""mean_type_accuracy"":0.8875,""valid_json_rate"":1.0}"
+qwen3:4b-instruct-2507-q4_K_M,calibration,default,20,0.95,72.1,76.1,270.2,309.3,83.1,88.4,7481,10869,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 23:35:23 UTC,,"{""correct"":19,""total"":20}"

benchmarks/model_eval/results/2026-05-10-z690-ex-glacial.csv

@@ -0,0 +1,76 @@
+model_tag,task,mode,n_samples,accuracy,ttft_p50_ms,ttft_p95_ms,tps_p50,tps_p95,e2e_p50_ms,e2e_p95_ms,vram_resident_mb,vram_peak_mb,host,gpu,ollama_version,run_date,error,extras_json
+qwen3:4b-instruct-2507-q4_K_M,room_classification,closed,100,0.61,81.4,88.5,206.4,230.3,109.1,122.8,7481,22961,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:22:11 UTC,,"{""correct"":61,""total"":100}"
+qwen3:4b-instruct-2507-q4_K_M,room_classification,open,100,0.5855,76.8,82.9,199.9,224.7,116.2,132.4,7481,22934,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:22:25 UTC,,"{""mean_similarity"":0.5854657247309388,""exact_match_count"":0,""high_similarity_count"":10,""low_similarity_count"":35}"
+qwen3:4b-instruct-2507-q4_K_M,entity_extraction,default,50,0.778,78.1,86.2,172.3,176.1,624.0,860.9,7481,18565,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:22:41 UTC,,"{""mean_f1"":0.7780339529751295,""mean_precision"":0.780904761904762,""mean_recall"":0.7980952380952381,""valid_json_rate"":1.0}"
+qwen3:4b-instruct-2507-q4_K_M,memory_extraction,default,40,0.95,76.3,83.5,173.0,175.7,537.5,737.5,7481,11104,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:23:13 UTC,,"{""mean_coverage"":0.95,""mean_hallucination_rate"":0.3625,""mean_type_accuracy"":0.8875,""valid_json_rate"":1.0}"
+qwen3:4b-instruct-2507-q4_K_M,calibration,default,20,0.95,73.0,79.5,282.6,312.0,85.8,92.5,7481,11064,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:23:37 UTC,,"{""correct"":19,""total"":20}"
+qwen3:4b-instruct-2507-q8_0,room_classification,closed,100,0.64,80.7,85.9,152.7,170.1,118.2,135.5,9271,20245,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:23:39 UTC,,"{""correct"":64,""total"":100}"
+qwen3:4b-instruct-2507-q8_0,room_classification,open,100,0.5926,76.6,81.6,146.8,160.0,129.4,158.5,9271,20289,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:23:54 UTC,,"{""mean_similarity"":0.592603310715678,""exact_match_count"":0,""high_similarity_count"":8,""low_similarity_count"":33}"
+qwen3:4b-instruct-2507-q8_0,entity_extraction,default,50,0.7662,78.1,84.2,128.3,130.7,777.4,1100.8,9271,20336,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:24:11 UTC,,"{""mean_f1"":0.7662315462315462,""mean_precision"":0.7808253968253969,""mean_recall"":0.7783174603174604,""valid_json_rate"":1.0}"
+qwen3:4b-instruct-2507-q8_0,memory_extraction,default,40,0.9375,75.1,79.3,129.7,131.8,638.7,853.9,9271,20303,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:24:51 UTC,,"{""mean_coverage"":0.9375,""mean_hallucination_rate"":0.2833333333333333,""mean_type_accuracy"":0.8875,""valid_json_rate"":1.0}"
+qwen3:4b-instruct-2507-q8_0,calibration,default,20,0.95,71.2,74.1,218.1,239.9,85.2,90.2,9271,20249,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:25:20 UTC,,"{""correct"":19,""total"":20}"
+qwen2.5:3b-instruct-q4_K_M,room_classification,closed,100,0.39,81.8,87.4,254.9,349.7,110.2,127.0,4082,16449,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:25:22 UTC,,"{""correct"":39,""total"":100}"
+qwen2.5:3b-instruct-q4_K_M,room_classification,open,100,0.5881,78.3,83.0,241.5,263.6,122.0,155.1,4082,16463,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:25:47 UTC,,"{""mean_similarity"":0.5881282854244246,""exact_match_count"":0,""high_similarity_count"":8,""low_similarity_count"":34}"
+qwen2.5:3b-instruct-q4_K_M,entity_extraction,default,50,0.6795,98.2,107.1,105.9,151.7,2139.3,3643.1,4082,16513,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:26:03 UTC,,"{""mean_f1"":0.6794943944943945,""mean_precision"":0.692563492063492,""mean_recall"":0.7044920634920635,""valid_json_rate"":1.0}"
+qwen2.5:3b-instruct-q4_K_M,memory_extraction,default,40,0.8375,92.1,97.8,105.0,166.6,1123.2,1585.6,4082,16430,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:27:56 UTC,,"{""mean_coverage"":0.8375,""mean_hallucination_rate"":0.0,""mean_type_accuracy"":0.9375,""valid_json_rate"":1.0}"
+qwen2.5:3b-instruct-q4_K_M,calibration,default,20,0.9,81.4,84.4,166.7,198.0,100.5,111.7,4082,16386,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:28:45 UTC,,"{""correct"":18,""total"":20}"
+gemma3:4b-it-q4_K_M,room_classification,closed,100,0.52,171.9,188.1,169.3,185.1,222.2,252.5,4745,17372,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:28:47 UTC,,"{""correct"":52,""total"":100}"
+gemma3:4b-it-q4_K_M,room_classification,open,100,0.6122,168.5,178.0,171.5,191.1,213.1,245.5,4745,17377,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:29:25 UTC,,"{""mean_similarity"":0.6122298727939781,""exact_match_count"":2,""high_similarity_count"":14,""low_similarity_count"":32}"
+gemma3:4b-it-q4_K_M,entity_extraction,default,50,0.7433,163.5,171.4,163.1,171.9,902.4,1231.2,4745,17372,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:29:50 UTC,,"{""mean_f1"":0.7433342797553324,""mean_precision"":0.7445476190476191,""mean_recall"":0.7737777777777778,""valid_json_rate"":1.0}"
+gemma3:4b-it-q4_K_M,memory_extraction,default,40,0.9125,163.2,171.1,165.1,170.9,633.4,863.4,4745,8190,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:30:38 UTC,,"{""mean_coverage"":0.9125,""mean_hallucination_rate"":0.19166666666666665,""mean_type_accuracy"":0.9,""valid_json_rate"":1.0}"
+gemma3:4b-it-q4_K_M,calibration,default,20,0.9,186.9,206.9,106.5,123.6,214.5,233.0,4745,8194,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:31:06 UTC,,"{""correct"":18,""total"":20}"
+gemma3:4b-it-qat,room_classification,closed,100,0.54,96.8,105.8,159.1,176.3,197.0,226.4,6146,13430,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:31:13 UTC,,"{""correct"":54,""total"":100}"
+gemma3:4b-it-qat,room_classification,open,100,0.6115,90.3,96.9,163.9,178.2,155.9,189.4,6146,13419,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:31:47 UTC,,"{""mean_similarity"":0.6115102712845908,""exact_match_count"":3,""high_similarity_count"":15,""low_similarity_count"":31}"
+gemma3:4b-it-qat,entity_extraction,default,50,0.7609,89.7,94.3,150.4,155.4,834.5,1151.3,6146,13422,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:32:06 UTC,,"{""mean_f1"":0.760949553370606,""mean_precision"":0.7636190476190476,""mean_recall"":0.7864444444444445,""valid_json_rate"":1.0}"
+gemma3:4b-it-qat,memory_extraction,default,40,0.8875,87.7,94.1,152.5,157.0,601.4,894.6,6146,13421,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:32:50 UTC,,"{""mean_coverage"":0.8875,""mean_hallucination_rate"":0.19166666666666665,""mean_type_accuracy"":0.9625,""valid_json_rate"":1.0}"
+gemma3:4b-it-qat,calibration,default,20,0.9,83.4,93.3,249.2,266.2,116.6,132.5,6146,13376,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:33:19 UTC,,"{""correct"":18,""total"":20}"
+gemma3:1b-it-q4_K_M,room_classification,closed,100,0.22,172.6,179.2,155.3,434.5,209.8,232.1,1416,9610,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:33:21 UTC,,"{""correct"":22,""total"":100}"
+gemma3:1b-it-q4_K_M,room_classification,open,100,0.5484,165.6,181.7,171.1,317.8,195.3,235.9,1416,9610,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:33:52 UTC,,"{""mean_similarity"":0.5483980357684296,""exact_match_count"":1,""high_similarity_count"":5,""low_similarity_count"":57}"
+gemma3:1b-it-q4_K_M,entity_extraction,default,50,0.6084,154.1,162.1,277.9,289.1,707.4,1075.3,1416,9613,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:34:15 UTC,,"{""mean_f1"":0.6083696303696303,""mean_precision"":0.5546897546897547,""mean_recall"":0.7133174603174604,""valid_json_rate"":1.0}"
+gemma3:1b-it-q4_K_M,memory_extraction,default,39,0.7308,155.8,167.5,272.7,286.5,469.3,711.6,1416,9613,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:34:53 UTC,,"{""mean_coverage"":0.7307692307692307,""mean_hallucination_rate"":0.2239316239316239,""mean_type_accuracy"":0.6666666666666666,""valid_json_rate"":0.9743589743589743,""error_count"":1,""error_sample"":""Cannot reach http://localhost:11434/api/chat: timed out""}"
+gemma3:1b-it-q4_K_M,calibration,default,20,0.8,161.2,167.8,218.7,319.8,174.1,183.5,1416,5024,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:38:14 UTC,,"{""correct"":16,""total"":20}"
+llama3.2:3b-instruct-q4_K_M,room_classification,closed,100,0.27,91.4,98.9,269.5,296.0,119.3,132.0,7126,11221,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:38:18 UTC,,"{""correct"":27,""total"":100}"
+llama3.2:3b-instruct-q4_K_M,room_classification,open,100,0.5466,89.6,99.1,245.4,279.9,125.5,151.8,7126,11225,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:38:48 UTC,,"{""mean_similarity"":0.5466407337111652,""exact_match_count"":1,""high_similarity_count"":8,""low_similarity_count"":46}"
+llama3.2:3b-instruct-q4_K_M,entity_extraction,default,50,0.672,109.0,120.8,105.6,122.1,1786.6,2701.5,7126,11225,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:39:04 UTC,,"{""mean_f1"":0.671959706959707,""mean_precision"":0.7418571428571429,""mean_recall"":0.6348412698412698,""valid_json_rate"":1.0}"
+llama3.2:3b-instruct-q4_K_M,memory_extraction,default,40,0.8125,102.6,106.5,107.9,114.3,821.1,1596.3,7126,11201,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:40:39 UTC,,"{""mean_coverage"":0.8125,""mean_hallucination_rate"":0.05,""mean_type_accuracy"":0.9125,""valid_json_rate"":1.0}"
+llama3.2:3b-instruct-q4_K_M,calibration,default,20,0.95,81.2,5029.4,322.1,390.9,92.4,5050.2,7126,11199,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:41:18 UTC,,"{""correct"":19,""total"":20}"
+llama3.2:1b-instruct-q4_K_M,room_classification,closed,100,0.22,84.5,91.9,349.2,726.5,107.2,136.4,3914,12211,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:41:30 UTC,,"{""correct"":22,""total"":100}"
+llama3.2:1b-instruct-q4_K_M,room_classification,open,100,0.4858,96.4,103.8,219.0,297.0,127.1,154.1,3914,12213,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:41:46 UTC,,"{""mean_similarity"":0.4858285412611383,""exact_match_count"":0,""high_similarity_count"":6,""low_similarity_count"":71}"
+llama3.2:1b-instruct-q4_K_M,entity_extraction,default,50,0.3606,101.5,106.8,121.8,135.2,528.2,1882.1,3914,12206,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:42:03 UTC,,"{""mean_f1"":0.36056132756132764,""mean_precision"":0.8959999999999999,""mean_recall"":0.24044444444444443,""valid_json_rate"":1.0}"
+llama3.2:1b-instruct-q4_K_M,memory_extraction,default,40,0.7625,102.0,107.6,114.1,122.1,1164.6,1963.9,3914,12211,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:42:38 UTC,,"{""mean_coverage"":0.7625,""mean_hallucination_rate"":0.22291666666666665,""mean_type_accuracy"":0.55,""valid_json_rate"":1.0}"
+llama3.2:1b-instruct-q4_K_M,calibration,default,20,0.55,90.6,101.0,182.7,238.6,107.9,139.9,3914,12158,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:43:32 UTC,,"{""correct"":11,""total"":20}"
+phi3.5:3.8b-mini-instruct-q4_K_M,room_classification,closed,100,0.47,31.8,37.4,237.0,255.9,66.3,76.2,16564,17766,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:43:34 UTC,,"{""correct"":47,""total"":100}"
+phi3.5:3.8b-mini-instruct-q4_K_M,room_classification,open,100,0.5921,26.2,32.0,229.4,242.1,92.7,147.9,16564,18595,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:44:09 UTC,,"{""mean_similarity"":0.5920845427046642,""exact_match_count"":0,""high_similarity_count"":5,""low_similarity_count"":25}"
+phi3.5:3.8b-mini-instruct-q4_K_M,entity_extraction,default,49,0.6411,26.7,29.5,213.0,220.3,816.5,1211.2,16564,18594,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:44:24 UTC,,"{""mean_f1"":0.6410999697914463,""mean_precision"":0.6766682863621639,""mean_recall"":0.6375769355361192,""valid_json_rate"":1.0,""error_count"":1,""error_sample"":""Cannot reach http://localhost:11434/api/chat: timed out""}"
+phi3.5:3.8b-mini-instruct-q4_K_M,memory_extraction,default,40,0.9125,23.6,25.7,211.4,217.3,765.3,1158.6,16564,18583,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:48:05 UTC,,"{""mean_coverage"":0.9125,""mean_hallucination_rate"":0.17083333333333334,""mean_type_accuracy"":0.95,""valid_json_rate"":1.0}"
+phi3.5:3.8b-mini-instruct-q4_K_M,calibration,default,20,0.95,19.4,28.5,352.8,369.9,30.4,39.7,16564,18537,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:48:38 UTC,,"{""correct"":19,""total"":20}"
+qwen3:1.7b-q4_K_M,room_classification,closed,100,0.28,73.9,78.3,322.1,360.1,95.1,105.7,5024,23391,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:48:39 UTC,,"{""correct"":28,""total"":100}"
+qwen3:1.7b-q4_K_M,room_classification,open,100,0.5308,74.1,79.5,306.9,355.5,98.5,119.3,5024,23396,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:48:50 UTC,,"{""mean_similarity"":0.5307987141294052,""exact_match_count"":1,""high_similarity_count"":6,""low_similarity_count"":58}"
+qwen3:1.7b-q4_K_M,entity_extraction,default,50,0.313,71.9,76.1,299.6,315.8,198.6,460.7,5024,23417,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:49:04 UTC,,"{""mean_f1"":0.313,""mean_precision"":0.6722619047619048,""mean_recall"":0.22969841269841268,""valid_json_rate"":1.0}"
+qwen3:1.7b-q4_K_M,memory_extraction,default,40,0.8375,69.8,74.0,306.5,311.5,279.7,371.3,5024,23408,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:49:16 UTC,,"{""mean_coverage"":0.8375,""mean_hallucination_rate"":0.020833333333333332,""mean_type_accuracy"":0.825,""valid_json_rate"":1.0}"
+qwen3:1.7b-q4_K_M,calibration,default,20,0.95,67.5,72.7,472.9,502.6,75.3,79.9,5024,23365,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:49:30 UTC,,"{""correct"":19,""total"":20}"
+qwen3:0.6b-q4_K_M,room_classification,closed,100,0.25,71.0,77.5,429.0,587.4,84.0,103.6,4220,22691,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:49:32 UTC,,"{""correct"":25,""total"":100}"
+qwen3:0.6b-q4_K_M,room_classification,open,97,0.4551,73.6,80.6,177.6,265.0,108.8,135.0,4220,22707,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:49:42 UTC,,"{""mean_similarity"":0.45505210335290763,""exact_match_count"":0,""high_similarity_count"":3,""low_similarity_count"":79,""error_count"":3,""error_sample"":""Cannot reach http://localhost:11434/api/chat: timed out""}"
+qwen3:0.6b-q4_K_M,entity_extraction,default,50,0.4805,69.3,74.1,176.3,197.4,535.4,866.0,4220,7714,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:58:56 UTC,,"{""mean_f1"":0.48046049375461136,""mean_precision"":0.5650873015873016,""mean_recall"":0.47046031746031747,""valid_json_rate"":1.0}"
+qwen3:0.6b-q4_K_M,memory_extraction,default,40,0.7375,68.3,74.2,191.5,217.3,327.1,503.9,4220,7713,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:59:25 UTC,,"{""mean_coverage"":0.7375,""mean_hallucination_rate"":0.075,""mean_type_accuracy"":0.425,""valid_json_rate"":1.0}"
+qwen3:0.6b-q4_K_M,calibration,default,20,0.25,68.4,73.5,418.9,448.1,77.0,84.0,4220,7714,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:59:41 UTC,,"{""correct"":5,""total"":20}"
+qwen2.5:1.5b-instruct-q4_K_M,room_classification,closed,100,0.39,74.1,79.5,339.7,394.8,101.0,118.8,2668,10246,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:59:43 UTC,,"{""correct"":39,""total"":100}"
+qwen2.5:1.5b-instruct-q4_K_M,room_classification,open,100,0.5924,74.4,79.3,249.7,339.8,122.4,151.9,2668,10247,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 15:59:56 UTC,,"{""mean_similarity"":0.5924099697455907,""exact_match_count"":0,""high_similarity_count"":12,""low_similarity_count"":35}"
+qwen2.5:1.5b-instruct-q4_K_M,entity_extraction,default,50,0.3669,95.5,102.4,112.0,132.6,1428.6,2614.6,2668,10271,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:00:12 UTC,,"{""mean_f1"":0.3668792318792319,""mean_precision"":0.394984126984127,""mean_recall"":0.3517619047619048,""valid_json_rate"":1.0}"
+qwen2.5:1.5b-instruct-q4_K_M,memory_extraction,default,40,0.9125,91.5,95.8,103.8,114.6,1625.0,2018.4,2668,10248,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:01:22 UTC,,"{""mean_coverage"":0.9125,""mean_hallucination_rate"":0.30625,""mean_type_accuracy"":0.8875,""valid_json_rate"":1.0}"
+qwen2.5:1.5b-instruct-q4_K_M,calibration,default,20,0.9,78.6,82.9,192.2,222.6,98.1,105.6,2668,10204,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:02:31 UTC,,"{""correct"":18,""total"":20}"
+qwen2.5:0.5b-instruct-q4_K_M,room_classification,closed,100,0.22,71.2,78.2,522.9,668.3,91.3,110.6,1721,9096,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:02:33 UTC,,"{""correct"":22,""total"":100}"
+qwen2.5:0.5b-instruct-q4_K_M,room_classification,open,100,0.5748,75.6,82.1,285.9,570.0,111.4,144.1,1721,9094,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:02:45 UTC,,"{""mean_similarity"":0.5747817635421044,""exact_match_count"":1,""high_similarity_count"":16,""low_similarity_count"":42}"
+qwen2.5:0.5b-instruct-q4_K_M,entity_extraction,default,49,0.4603,93.4,99.2,91.4,248.3,2267.0,4724.6,1721,9126,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:03:00 UTC,,"{""mean_f1"":0.4603074472569639,""mean_precision"":0.4634191771946874,""mean_recall"":0.4984288953676709,""valid_json_rate"":1.0,""error_count"":1,""error_sample"":""Cannot reach http://localhost:11434/api/chat: timed out""}"
+qwen2.5:0.5b-instruct-q4_K_M,memory_extraction,default,40,0.1125,82.5,91.4,99.3,442.4,757.0,1173.8,1721,4642,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:08:08 UTC,,"{""mean_coverage"":0.1125,""mean_hallucination_rate"":0.025,""mean_type_accuracy"":0.0125,""valid_json_rate"":0.125}"
+qwen2.5:0.5b-instruct-q4_K_M,calibration,default,20,0.35,80.1,87.6,323.8,391.6,93.9,107.8,1721,4643,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:08:40 UTC,,"{""correct"":7,""total"":20}"
+gemma3:270m-it-q8_0,room_classification,closed,98,0.1224,86.6,97.8,304.7,524.9,153.7,215.1,742,5518,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:08:42 UTC,,"{""correct"":12,""total"":98,""error_count"":2,""error_sample"":""Empty response from Ollama (model=gemma3:270m-it-q8_0)""}"
+gemma3:270m-it-q8_0,room_classification,open,97,0.3809,83.1,88.1,193.8,409.3,199.8,243.6,742,5546,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:08:59 UTC,,"{""mean_similarity"":0.38094392235665736,""exact_match_count"":0,""high_similarity_count"":0,""low_similarity_count"":96,""error_count"":3,""error_sample"":""Cannot reach http://localhost:11434/api/chat: timed out""}"
+gemma3:270m-it-q8_0,entity_extraction,default,0,0.0,0,0,0,0,0,0,,,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:18:25 UTC,Warmup failed: Cannot reach http://localhost:11434/api/chat: timed out,{}
+gemma3:270m-it-q8_0,memory_extraction,default,40,0.025,83.5,88.2,170.6,250.9,358.3,440.7,742,4132,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:21:25 UTC,,"{""mean_coverage"":0.025,""mean_hallucination_rate"":0.975,""mean_type_accuracy"":0.0,""valid_json_rate"":1.0}"
+gemma3:270m-it-q8_0,calibration,default,20,0.05,78.5,83.5,254.7,382.9,108.6,144.6,742,4088,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:21:42 UTC,,"{""correct"":1,""total"":20}"
+qwen3:4b-instruct-2507-fp16,room_classification,closed,100,0.65,77.9,85.0,104.7,118.4,129.6,157.8,13215,16910,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:21:45 UTC,,"{""correct"":65,""total"":100}"
+qwen3:4b-instruct-2507-fp16,room_classification,open,100,0.5945,77.3,81.8,98.8,111.3,152.6,190.6,13215,16942,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:22:03 UTC,,"{""mean_similarity"":0.5945282821993989,""exact_match_count"":0,""high_similarity_count"":9,""low_similarity_count"":33}"
+qwen3:4b-instruct-2507-fp16,entity_extraction,default,50,0.7716,77.2,79.9,86.3,87.4,1063.1,1578.6,13215,16911,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:22:22 UTC,,"{""mean_f1"":0.7715648795648795,""mean_precision"":0.7852698412698413,""mean_recall"":0.7849841269841269,""valid_json_rate"":1.0}"
+qwen3:4b-instruct-2507-fp16,memory_extraction,default,40,0.9375,74.4,78.0,86.7,88.4,921.2,1189.6,13215,16910,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:23:18 UTC,,"{""mean_coverage"":0.9375,""mean_hallucination_rate"":0.3208333333333333,""mean_type_accuracy"":0.875,""valid_json_rate"":1.0}"
+qwen3:4b-instruct-2507-fp16,calibration,default,20,0.95,72.6,79.0,153.8,165.7,94.2,102.1,13215,16865,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-10 16:23:57 UTC,,"{""correct"":19,""total"":20}"

benchmarks/model_eval/results/2026-05-11-cloud-z690-ex-glacial.csv

@@ -0,0 +1,36 @@
+model_tag,task,mode,n_samples,accuracy,ttft_p50_ms,ttft_p95_ms,tps_p50,tps_p95,e2e_p50_ms,e2e_p95_ms,vram_resident_mb,vram_peak_mb,host,gpu,ollama_version,run_date,error,extras_json
+gpt-oss:20b-cloud,room_classification,closed,29,0.8966,0.0,0.0,0.0,0.0,1148.6,2517.1,,2841,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:00:45 UTC,,"{""correct"":26,""total"":29,""error_count"":1,""error_sample"":""Cannot reach http://localhost:11434/api/chat: timed out""}"
+gpt-oss:20b-cloud,room_classification,open,30,0.5545,0.0,0.0,0.0,0.0,1171.6,1954.4,,3629,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:04:30 UTC,,"{""mean_similarity"":0.5544974084559757,""exact_match_count"":0,""high_similarity_count"":3,""low_similarity_count"":13}"
+gpt-oss:20b-cloud,entity_extraction,default,29,0.7476,0.0,0.0,0.0,0.0,3285.5,15561.6,,3667,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:05:12 UTC,,"{""mean_f1"":0.747642879403315,""mean_precision"":0.7113300492610838,""mean_recall"":0.7994526546250684,""valid_json_rate"":1.0,""error_count"":1,""error_sample"":""Cannot reach http://localhost:11434/api/chat: timed out""}"
+gpt-oss:20b-cloud,memory_extraction,default,30,1.0,0.0,0.0,0.0,0.0,2249.9,3560.5,,3659,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:10:34 UTC,,"{""mean_coverage"":1.0,""mean_hallucination_rate"":0.5394444444444445,""mean_type_accuracy"":0.9166666666666666,""valid_json_rate"":1.0}"
+gpt-oss:20b-cloud,calibration,default,20,0.9,0.0,0.0,0.0,0.0,770.7,1246.7,,3605,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:11:54 UTC,,"{""correct"":18,""total"":20}"
+gpt-oss:120b-cloud,room_classification,closed,30,0.7667,0.0,0.0,0.0,0.0,1732.4,3274.9,,3607,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:12:12 UTC,,"{""correct"":23,""total"":30}"
+gpt-oss:120b-cloud,room_classification,open,30,0.5281,0.0,0.0,0.0,0.0,1590.7,2691.4,,3609,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:13:11 UTC,,"{""mean_similarity"":0.528105052326154,""exact_match_count"":0,""high_similarity_count"":2,""low_similarity_count"":15}"
+gpt-oss:120b-cloud,entity_extraction,default,30,0.831,0.0,0.0,0.0,0.0,3840.8,12103.2,,3625,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:14:07 UTC,,"{""mean_f1"":0.8309726433410645,""mean_precision"":0.798888888888889,""mean_recall"":0.877010582010582,""valid_json_rate"":0.9666666666666667}"
+gpt-oss:120b-cloud,memory_extraction,default,30,1.0,0.0,0.0,0.0,0.0,3222.6,6014.8,,3620,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:16:37 UTC,,"{""mean_coverage"":1.0,""mean_hallucination_rate"":0.4611111111111111,""mean_type_accuracy"":0.9666666666666667,""valid_json_rate"":1.0}"
+gpt-oss:120b-cloud,calibration,default,20,0.95,0.0,0.0,0.0,0.0,1224.8,3532.9,,3663,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:18:30 UTC,,"{""correct"":19,""total"":20}"
+qwen3-coder:480b-cloud,room_classification,closed,30,0.9,0.0,0.0,0.0,0.0,752.5,4329.4,,3626,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:19:01 UTC,,"{""correct"":27,""total"":30}"
+qwen3-coder:480b-cloud,room_classification,open,30,0.5789,0.0,0.0,0.0,0.0,935.0,2930.1,,3632,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:19:54 UTC,,"{""mean_similarity"":0.5789309327203288,""exact_match_count"":0,""high_similarity_count"":4,""low_similarity_count"":13}"
+qwen3-coder:480b-cloud,entity_extraction,default,30,0.8029,0.0,0.0,0.0,0.0,2615.8,10389.6,,3634,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:20:36 UTC,,"{""mean_f1"":0.8028804528804528,""mean_precision"":0.7752248677248678,""mean_recall"":0.8631746031746032,""valid_json_rate"":1.0}"
+qwen3-coder:480b-cloud,memory_extraction,default,30,0.9667,0.0,0.0,0.0,0.0,2877.4,7443.5,,3634,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:22:35 UTC,,"{""mean_coverage"":0.9666666666666667,""mean_hallucination_rate"":0.5305555555555556,""mean_type_accuracy"":0.9,""valid_json_rate"":0.9666666666666667}"
+qwen3-coder:480b-cloud,calibration,default,20,0.95,0.0,0.0,0.0,0.0,692.8,3699.5,,3632,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:24:32 UTC,,"{""correct"":19,""total"":20}"
+deepseek-v3.1:671b-cloud,room_classification,closed,30,0.8,0.0,0.0,0.0,0.0,708.2,1000.4,,3634,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:25:00 UTC,,"{""correct"":24,""total"":30}"
+deepseek-v3.1:671b-cloud,room_classification,open,30,0.5587,0.0,0.0,0.0,0.0,707.2,1133.4,,3634,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:25:24 UTC,,"{""mean_similarity"":0.5586936083647193,""exact_match_count"":0,""high_similarity_count"":3,""low_similarity_count"":15}"
+deepseek-v3.1:671b-cloud,entity_extraction,default,30,0.8297,0.0,0.0,0.0,0.0,1494.6,2286.5,,3632,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:25:50 UTC,,"{""mean_f1"":0.8297465497465498,""mean_precision"":0.825436507936508,""mean_recall"":0.862936507936508,""valid_json_rate"":1.0}"
+deepseek-v3.1:671b-cloud,memory_extraction,default,30,0.9667,0.0,0.0,0.0,0.0,1620.1,2548.4,,3632,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:26:37 UTC,,"{""mean_coverage"":0.9666666666666667,""mean_hallucination_rate"":0.29444444444444445,""mean_type_accuracy"":0.9666666666666667,""valid_json_rate"":1.0}"
+deepseek-v3.1:671b-cloud,calibration,default,20,0.95,0.0,0.0,0.0,0.0,770.8,1409.7,,3632,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:27:34 UTC,,"{""correct"":19,""total"":20}"
+deepseek-v4-flash:cloud,room_classification,closed,30,0.6333,0.0,0.0,0.0,0.0,723.2,12726.5,,3632,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:27:51 UTC,,"{""correct"":19,""total"":30}"
+deepseek-v4-flash:cloud,room_classification,open,30,0.6073,0.0,0.0,0.0,0.0,754.9,8022.6,,3635,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:29:34 UTC,,"{""mean_similarity"":0.6072711773623308,""exact_match_count"":0,""high_similarity_count"":6,""low_similarity_count"":8}"
+deepseek-v4-flash:cloud,entity_extraction,default,30,0.8373,0.0,0.0,0.0,0.0,2071.0,33930.1,,3673,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:30:42 UTC,,"{""mean_f1"":0.8373420147104357,""mean_precision"":0.8095238095238095,""mean_recall"":0.8936772486772486,""valid_json_rate"":1.0}"
+deepseek-v4-flash:cloud,memory_extraction,default,30,0.95,0.0,0.0,0.0,0.0,1298.3,9938.7,,3625,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:33:48 UTC,,"{""mean_coverage"":0.95,""mean_hallucination_rate"":0.28055555555555556,""mean_type_accuracy"":0.9833333333333333,""valid_json_rate"":1.0}"
+deepseek-v4-flash:cloud,calibration,default,20,0.95,0.0,0.0,0.0,0.0,1230.1,10325.4,,3623,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:35:50 UTC,,"{""correct"":19,""total"":20}"
+deepseek-v4-pro:cloud,room_classification,closed,30,0.8333,0.0,0.0,0.0,0.0,2426.0,7985.8,,3625,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:37:09 UTC,,"{""correct"":25,""total"":30}"
+deepseek-v4-pro:cloud,room_classification,open,30,0.605,0.0,0.0,0.0,0.0,2640.5,6000.4,,3625,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:39:25 UTC,,"{""mean_similarity"":0.6049819794495279,""exact_match_count"":0,""high_similarity_count"":3,""low_similarity_count"":8}"
+deepseek-v4-pro:cloud,entity_extraction,default,30,0.8266,0.0,0.0,0.0,0.0,5387.4,11041.1,,3619,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:40:58 UTC,,"{""mean_f1"":0.8266264924159662,""mean_precision"":0.7784523809523809,""mean_recall"":0.904047619047619,""valid_json_rate"":1.0}"
+deepseek-v4-pro:cloud,memory_extraction,default,30,1.0,0.0,0.0,0.0,0.0,2625.8,14915.3,,3705,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:44:02 UTC,,"{""mean_coverage"":1.0,""mean_hallucination_rate"":0.425,""mean_type_accuracy"":0.95,""valid_json_rate"":1.0}"
+deepseek-v4-pro:cloud,calibration,default,20,0.95,0.0,0.0,0.0,0.0,4429.5,9251.6,,3811,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:46:58 UTC,,"{""correct"":19,""total"":20}"
+kimi-k2.6:cloud,room_classification,closed,30,0.8,0.0,0.0,0.0,0.0,1042.3,2059.5,,3703,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:48:37 UTC,,"{""correct"":24,""total"":30}"
+kimi-k2.6:cloud,room_classification,open,30,0.5928,0.0,0.0,0.0,0.0,2278.1,4616.4,,3772,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:49:16 UTC,,"{""mean_similarity"":0.5927715202253424,""exact_match_count"":0,""high_similarity_count"":3,""low_similarity_count"":10}"
+kimi-k2.6:cloud,entity_extraction,default,30,0.7699,0.0,0.0,0.0,0.0,2347.9,7465.8,,3806,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:50:31 UTC,,"{""mean_f1"":0.7698523406418143,""mean_precision"":0.7407539682539682,""mean_recall"":0.8282539682539682,""valid_json_rate"":0.9}"
+kimi-k2.6:cloud,memory_extraction,default,30,0.3667,0.0,0.0,0.0,0.0,2742.4,5233.8,,3837,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:52:05 UTC,,"{""mean_coverage"":0.36666666666666664,""mean_hallucination_rate"":0.21833333333333332,""mean_type_accuracy"":0.36666666666666664,""valid_json_rate"":0.36666666666666664}"
+kimi-k2.6:cloud,calibration,default,20,0.9,0.0,0.0,0.0,0.0,824.1,1267.2,,3699,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 04:53:57 UTC,,"{""correct"":18,""total"":20}"

benchmarks/model_eval/results/2026-05-11-modern-z690-ex-glacial.csv

@@ -0,0 +1,26 @@
+model_tag,task,mode,n_samples,accuracy,ttft_p50_ms,ttft_p95_ms,tps_p50,tps_p95,e2e_p50_ms,e2e_p95_ms,vram_resident_mb,vram_peak_mb,host,gpu,ollama_version,run_date,error,extras_json
+qwen3.5:4b-q4_K_M,room_classification,closed,101,0.5941,185.9,204.1,148.2,198.9,224.7,252.6,6998,9816,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:34:51 UTC,,"{""correct"":60,""total"":101}"
+qwen3.5:4b-q4_K_M,room_classification,open,101,0.6148,182.5,208.1,136.0,150.5,236.6,266.0,6998,10667,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:35:19 UTC,,"{""mean_similarity"":0.614754134757975,""exact_match_count"":4,""high_similarity_count"":20,""low_similarity_count"":32}"
+qwen3.5:4b-q4_K_M,entity_extraction,default,50,0.7866,177.4,193.4,126.1,130.3,883.5,1361.5,6998,10643,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:35:47 UTC,,"{""mean_f1"":0.7866371231108072,""mean_precision"":0.7762142857142856,""mean_recall"":0.8216190476190477,""valid_json_rate"":1.0}"
+qwen3.5:4b-q4_K_M,memory_extraction,default,40,0.85,189.1,208.6,129.0,131.4,781.7,1008.8,6998,10580,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:36:35 UTC,,"{""mean_coverage"":0.85,""mean_hallucination_rate"":0.2708333333333333,""mean_type_accuracy"":0.775,""valid_json_rate"":0.875}"
+qwen3.5:4b-q4_K_M,calibration,default,20,0.9,168.2,176.0,169.7,200.7,188.2,196.4,6998,10537,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:37:10 UTC,,"{""correct"":18,""total"":20}"
+gemma4:e2b-it-q4_K_M,room_classification,closed,101,0.5149,173.1,181.7,137.5,148.8,224.9,239.3,7636,18190,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:37:14 UTC,,"{""correct"":52,""total"":101}"
+gemma4:e2b-it-q4_K_M,room_classification,open,101,0.6337,168.5,179.1,138.5,205.2,212.4,236.0,7636,18188,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:37:44 UTC,,"{""mean_similarity"":0.6337146680947495,""exact_match_count"":2,""high_similarity_count"":23,""low_similarity_count"":31}"
+gemma4:e2b-it-q4_K_M,entity_extraction,default,50,0.7106,161.8,170.7,182.9,188.2,674.2,903.9,7636,18171,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:38:10 UTC,,"{""mean_f1"":0.7105557775557776,""mean_precision"":0.7971190476190476,""mean_recall"":0.6658412698412698,""valid_json_rate"":1.0}"
+gemma4:e2b-it-q4_K_M,memory_extraction,default,40,0.9125,158.0,167.5,183.1,189.2,552.5,843.7,7636,18165,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:38:44 UTC,,"{""mean_coverage"":0.9125,""mean_hallucination_rate"":0.18541666666666665,""mean_type_accuracy"":0.9375,""valid_json_rate"":1.0}"
+gemma4:e2b-it-q4_K_M,calibration,default,20,0.95,163.2,171.9,209.7,307.4,176.8,188.3,7636,18125,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:39:10 UTC,,"{""correct"":19,""total"":20}"
+gemma4:e4b-it-q4_K_M,room_classification,closed,101,0.6238,177.9,196.1,139.0,159.4,229.7,259.0,10626,21095,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:39:14 UTC,,"{""correct"":63,""total"":101}"
+gemma4:e4b-it-q4_K_M,room_classification,open,101,0.6534,174.2,190.5,137.3,155.7,219.6,243.3,10626,21100,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:40:00 UTC,,"{""mean_similarity"":0.6534019611054593,""exact_match_count"":8,""high_similarity_count"":29,""low_similarity_count"":32}"
+gemma4:e4b-it-q4_K_M,entity_extraction,default,50,0.7505,171.6,182.2,125.6,129.0,796.7,1066.6,10626,21286,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:40:26 UTC,,"{""mean_f1"":0.7505468975468975,""mean_precision"":0.845952380952381,""mean_recall"":0.6925873015873016,""valid_json_rate"":1.0}"
+gemma4:e4b-it-q4_K_M,memory_extraction,default,40,0.9125,168.9,182.0,124.0,128.0,788.8,1090.6,10626,21270,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:41:08 UTC,,"{""mean_coverage"":0.9125,""mean_hallucination_rate"":0.23541666666666666,""mean_type_accuracy"":0.9625,""valid_json_rate"":1.0}"
+gemma4:e4b-it-q4_K_M,calibration,default,20,0.95,170.8,175.7,155.1,211.8,187.0,200.0,10626,21092,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:41:43 UTC,,"{""correct"":19,""total"":20}"
+granite4.1:3b-q4_K_M,room_classification,closed,101,0.495,60.4,65.3,236.1,305.9,88.4,100.1,6692,15731,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:41:47 UTC,,"{""correct"":50,""total"":101}"
+granite4.1:3b-q4_K_M,room_classification,open,101,0.6079,57.9,65.4,228.3,260.1,87.0,105.3,6692,15920,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:41:58 UTC,,"{""mean_similarity"":0.6078546389495884,""exact_match_count"":5,""high_similarity_count"":20,""low_similarity_count"":38}"
+granite4.1:3b-q4_K_M,entity_extraction,default,50,0.7384,70.1,82.1,120.6,164.5,1401.4,2293.4,6692,15793,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:42:10 UTC,,"{""mean_f1"":0.7383747363747364,""mean_precision"":0.796047619047619,""mean_recall"":0.7112380952380952,""valid_json_rate"":1.0}"
+granite4.1:3b-q4_K_M,memory_extraction,default,40,0.8625,66.0,68.1,119.3,156.4,1144.3,1892.5,6692,9071,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:43:27 UTC,,"{""mean_coverage"":0.8625,""mean_hallucination_rate"":0.1625,""mean_type_accuracy"":0.875,""valid_json_rate"":1.0}"
+granite4.1:3b-q4_K_M,calibration,default,20,0.95,58.3,72.3,189.5,213.9,77.4,86.1,6692,8988,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:44:18 UTC,,"{""correct"":19,""total"":20}"
+ministral-3:3b,room_classification,closed,101,0.4851,96.7,104.3,258.4,332.8,122.5,139.2,7646,16657,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:44:20 UTC,,"{""correct"":49,""total"":101}"
+ministral-3:3b,room_classification,open,101,0.5586,97.0,106.0,238.5,263.3,138.1,160.7,7646,17217,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:44:35 UTC,,"{""mean_similarity"":0.5585968656244248,""exact_match_count"":0,""high_similarity_count"":5,""low_similarity_count"":40}"
+ministral-3:3b,entity_extraction,default,50,0.6331,98.5,150.2,209.9,226.5,571.9,950.7,7646,17817,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:44:52 UTC,,"{""mean_f1"":0.6330946830946831,""mean_precision"":0.7190476190476189,""mean_recall"":0.5836031746031746,""valid_json_rate"":1.0}"
+ministral-3:3b,memory_extraction,default,40,0.9875,92.0,98.4,217.4,223.4,632.2,835.4,7646,17865,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:45:23 UTC,,"{""mean_coverage"":0.9875,""mean_hallucination_rate"":0.43666666666666665,""mean_type_accuracy"":0.8,""valid_json_rate"":1.0}"
+ministral-3:3b,calibration,default,20,0.95,96.0,109.7,174.7,303.1,121.8,154.6,7646,17521,z690-ex-glacial,NVIDIA GeForce RTX 3090,ollama version is 0.23.2,2026-05-11 05:45:52 UTC,,"{""correct"":19,""total"":20}"

benchmarks/model_eval/results/README.md

@@ -0,0 +1,5 @@
+# Results directory
+
+Raw CSV outputs go here (gitignored by default — they're per-machine and per-run).
+
+If you want to publish a result for posterity (e.g. attach to a PR or commit a baseline), name it descriptively (`2026-05-10-igor-rtx4090.csv`) and add an explicit exception to `.gitignore`.

benchmarks/model_eval/runner.py

@@ -0,0 +1,509 @@
+"""Run one (model, task, mode) triple. Output a single result row.
+
+Usage:
+    python -m benchmarks.model_eval.runner \\
+        --model qwen3:4b-instruct-2507-q4_K_M \\
+        --task room_classification \\
+        --mode closed \\
+        --dataset-dir benchmarks/model_eval/datasets
+
+Designed to be called by orchestrator.py for matrix runs, but standalone
+runnable for one-off debugging.
+"""
+from __future__ import annotations
+
+import argparse
+import dataclasses
+import json
+import os
+import re
+import subprocess
+import sys
+import time
+from dataclasses import asdict, dataclass, field
+from pathlib import Path
+from typing import Any, Callable, Optional
+
+from mempalace.llm_client import LLMError, LLMResponse, get_provider
+
+from .metrics import (
+    HostInfo,
+    TimingAggregate,
+    TimingSample,
+    VRAMPoller,
+    aggregate_timings,
+    embed_text,
+    extract_timing,
+    gather_host_info,
+    strip_thinking_tokens,
+    vram_resident_mb,
+)
+from .tasks.calibration import prompts as cal_prompts, score as cal_score
+from .tasks.entity_extraction import prompts as ent_prompts, score as ent_score
+from .tasks.memory_extraction import prompts as mem_prompts, score as mem_score
+from .tasks.room_classification import prompts as rc_prompts, score as rc_score
+
+
+@dataclass
+class Result:
+    model_tag: str
+    task: str
+    mode: str
+    accuracy: float
+    extras: dict
+    timing: TimingAggregate
+    vram_resident_mb: Optional[int]
+    vram_peak_mb: Optional[int]
+    host: HostInfo
+    run_date: str
+    n_samples: int
+    error: Optional[str] = None
+    language: str = "en"
+
+
+_EMBED_MODEL = "embeddinggemma"
+
+# Language code validator. Accepts ISO-639-style codes with optional region
+# subtag (en, pt-BR, zh-CN, fr_CA, etc.). Strict pattern is required because
+# `language` is interpolated into the dataset filename — without validation a
+# caller could pass values containing path separators or `..` and the loader
+# would read files outside the task directory.
+_LANGUAGE_RE = re.compile(r"^[A-Za-z][A-Za-z0-9]*(?:[_-][A-Za-z0-9]+)?$")
+
+# Tasks that score via semantic similarity and require an embedding model.
+_EMBED_TASKS: set[tuple[str, str]] = {
+    ("memory_extraction", "default"),
+    ("room_classification", "open"),
+}
+
+
+def _ensure_embed_model(endpoint: str, model: str = _EMBED_MODEL) -> None:
+    """Verify the embedding model is available; pull it automatically if not.
+
+    Raises RuntimeError with a clear message if the model cannot be made available.
+    """
+    if embed_text("ping", model=model, endpoint=endpoint) is not None:
+        return
+
+    print(f"  Embedding model '{model}' not found — pulling automatically...", file=sys.stderr, flush=True)
+    # Pass OLLAMA_HOST so the pull targets the same endpoint being benchmarked,
+    # not the default localhost:11434.
+    env = {**os.environ, "OLLAMA_HOST": endpoint}
+    try:
+        result = subprocess.run(
+            ["ollama", "pull", model],
+            env=env,
+            capture_output=True,
+            text=True,
+            timeout=300,
+        )
+    except FileNotFoundError:
+        raise RuntimeError(
+            f"'ollama' not found on PATH. Install Ollama and ensure it is on PATH, "
+            f"then run 'ollama pull {model}' manually."
+        )
+
+    if result.returncode != 0:
+        raise RuntimeError(
+            f"Embedding model '{model}' is required for this task but could not be pulled. "
+            f"Run 'ollama pull {model}' manually and retry.\n"
+            f"ollama stderr: {result.stderr.strip()}"
+        )
+
+    if embed_text("ping", model=model, endpoint=endpoint) is None:
+        raise RuntimeError(
+            f"Embedding model '{model}' was pulled but is still not responding on {endpoint}. "
+            f"Check Ollama logs."
+        )
+
+    print(f"  Embedding model '{model}' ready.", file=sys.stderr, flush=True)
+
+
+def load_jsonl(path: Path) -> list[dict]:
+    with open(path, "r", encoding="utf-8") as f:
+        return [json.loads(line) for line in f if line.strip()]
+
+
+def _classify_with_timing(
+    provider, system: str, user: str, json_mode: bool
+) -> tuple[Optional[LLMResponse], TimingSample, Optional[str]]:
+    """Run one classify call. Always disables thinking on hybrid models.
+
+    MemPalace classification tasks (room, entity, memory) never benefit
+    from extended reasoning. Forcing think=False keeps hybrid Qwen 3
+    models in fast-instruct mode, gives pure-instruct models a no-op,
+    and ensures the benchmark measures the real production code path.
+    """
+    t0 = time.perf_counter()
+    try:
+        response = provider.classify(system=system, user=user, json_mode=json_mode, think=False)
+    except LLMError as e:
+        return None, TimingSample(0, 0, 0, 0, 0), str(e)
+    elapsed = time.perf_counter() - t0
+    timing = extract_timing(response.raw, elapsed)
+    return response, timing, None
+
+
+def _build_prompt(task: str, mode: str, sample: dict, label: dict) -> tuple[str, str, bool]:
+    """Return (system, user, json_mode)."""
+    if task == "calibration":
+        return cal_prompts.SYSTEM, cal_prompts.build_user_prompt(sample["text"], sample["classes"]), False
+    if task == "room_classification":
+        if mode == "closed":
+            return (
+                rc_prompts.CLOSED_SYSTEM,
+                rc_prompts.build_closed_user(sample["agent"], sample["session_summary"], sample["__rooms__"]),
+                False,
+            )
+        return (
+            rc_prompts.OPEN_SYSTEM,
+            rc_prompts.build_open_user(sample["agent"], sample["session_summary"]),
+            False,
+        )
+    if task == "entity_extraction":
+        return ent_prompts.SYSTEM, ent_prompts.build_user(sample["text"]), True
+    if task == "memory_extraction":
+        return mem_prompts.SYSTEM, mem_prompts.build_user(sample["text"]), True
+    raise ValueError(f"Unknown task: {task}")
+
+
+def _score_one(task: str, mode: str, predicted: str, sample: dict, label: dict, endpoint: str, embed_model: str = _EMBED_MODEL) -> dict:
+    if task == "calibration":
+        return cal_score.score(predicted, label["label"], sample["classes"])
+    if task == "room_classification":
+        if mode == "closed":
+            return rc_score.score_closed(predicted, label["closed_set_label"], sample["__rooms__"])
+        return rc_score.score_open(predicted, label["preferred_open_label"], embed_model=embed_model, endpoint=endpoint)
+    if task == "entity_extraction":
+        return ent_score.score(predicted, label["entities"])
+    if task == "memory_extraction":
+        return mem_score.score(predicted, label["memories"], embed_model=embed_model, endpoint=endpoint)
+    raise ValueError(f"Unknown task: {task}")
+
+
+def _aggregate_accuracy(task: str, mode: str, scores: list[dict]) -> tuple[float, dict]:
+    """Return (primary_accuracy, extras_dict)."""
+    if not scores:
+        return 0.0, {}
+
+    if task == "calibration" or (task == "room_classification" and mode == "closed"):
+        correct = sum(1 for s in scores if s.get("correct"))
+        return correct / len(scores), {
+            "correct": correct,
+            "total": len(scores),
+        }
+
+    if task == "room_classification" and mode == "open":
+        sims = [s.get("similarity", 0.0) for s in scores]
+        exacts = sum(1 for s in scores if s.get("exact_match"))
+        mean_sim = sum(sims) / len(sims)
+        return mean_sim, {
+            "mean_similarity": mean_sim,
+            "exact_match_count": exacts,
+            "high_similarity_count": sum(1 for s in sims if s >= 0.8),
+            "low_similarity_count": sum(1 for s in sims if s < 0.5),
+        }
+
+    if task == "entity_extraction":
+        f1 = sum(s.get("f1", 0.0) for s in scores) / len(scores)
+        precision = sum(s.get("precision", 0.0) for s in scores) / len(scores)
+        recall = sum(s.get("recall", 0.0) for s in scores) / len(scores)
+        valid_json_rate = sum(1 for s in scores if s.get("valid_json")) / len(scores)
+        return f1, {
+            "mean_f1": f1,
+            "mean_precision": precision,
+            "mean_recall": recall,
+            "valid_json_rate": valid_json_rate,
+        }
+
+    if task == "memory_extraction":
+        coverage = sum(s.get("coverage", 0.0) for s in scores) / len(scores)
+        hallucination = sum(s.get("hallucination_rate", 0.0) for s in scores) / len(scores)
+        type_accuracy = sum(s.get("type_accuracy", 0.0) for s in scores) / len(scores)
+        valid_json_rate = sum(1 for s in scores if s.get("valid_json")) / len(scores)
+        return coverage, {
+            "mean_coverage": coverage,
+            "mean_hallucination_rate": hallucination,
+            "mean_type_accuracy": type_accuracy,
+            "valid_json_rate": valid_json_rate,
+        }
+
+    return 0.0, {}
+
+
+def run(
+    model_tag: str,
+    task: str,
+    mode: str,
+    dataset_dir: Path,
+    endpoint: str = "http://localhost:11434",
+    warmup: int = 1,
+    n_samples: Optional[int] = None,
+    strip_thinking: bool = True,
+    llm_provider: str = "ollama",
+    embed_endpoint: str = "http://localhost:11434",
+    embed_model: str = _EMBED_MODEL,
+    language: str = "en",
+    num_ctx: Optional[int] = None,
+) -> Result:
+    """Run one (model, task, mode) triple. Returns a Result.
+
+    When language != "en", loads `dataset.{language}.jsonl` instead of `dataset.jsonl`.
+    Labels and room_lists are always loaded from the English files — non-English inputs
+    are scored against the same English ground truth (cross-lingual mapping test).
+    """
+    host = gather_host_info()
+    run_date = time.strftime("%Y-%m-%d %H:%M:%S UTC", time.gmtime())
+
+    if not _LANGUAGE_RE.match(language):
+        return Result(
+            model_tag=model_tag, task=task, mode=mode,
+            accuracy=0.0, extras={}, timing=aggregate_timings([]),
+            vram_resident_mb=None, vram_peak_mb=None, host=host,
+            run_date=run_date, n_samples=0, language=language,
+            error=f"Invalid language code: {language!r}. Expected pattern like 'en', 'pt-BR', 'zh-CN'.",
+        )
+
+    task_dir = dataset_dir / task
+    dataset_file = "dataset.jsonl" if language == "en" else f"dataset.{language}.jsonl"
+    dataset_path = task_dir / dataset_file
+    # Belt-and-suspenders: even with the regex guard above, confirm the resolved
+    # path lives inside task_dir before opening anything on disk.
+    try:
+        if not dataset_path.resolve().is_relative_to(task_dir.resolve()):
+            raise ValueError("resolved path escapes task_dir")
+    except (ValueError, OSError) as e:
+        return Result(
+            model_tag=model_tag, task=task, mode=mode,
+            accuracy=0.0, extras={}, timing=aggregate_timings([]),
+            vram_resident_mb=None, vram_peak_mb=None, host=host,
+            run_date=run_date, n_samples=0, language=language,
+            error=f"Refused to load dataset outside task_dir: {dataset_path} ({e})",
+        )
+    if not dataset_path.exists():
+        return Result(
+            model_tag=model_tag, task=task, mode=mode,
+            accuracy=0.0, extras={}, timing=aggregate_timings([]),
+            vram_resident_mb=None, vram_peak_mb=None, host=host,
+            run_date=run_date, n_samples=0, language=language,
+            error=f"Dataset not found: {dataset_path}",
+        )
+    samples = load_jsonl(dataset_path)
+
+    # Prefer language-specific labels when present (memory_extraction scoring
+    # compares model output against ground truth — when the model extracts in
+    # the input language but the ground truth stays English, cosine similarity
+    # collapses to noise). Fall back to English with an explicit log so the
+    # source of any score gap is visible to whoever reads results.
+    lang_labels = task_dir / f"labels.{language}.jsonl"
+    if language != "en" and lang_labels.exists():
+        labels_path = lang_labels
+    else:
+        labels_path = task_dir / "labels.jsonl"
+        if language != "en":
+            print(
+                f"  info: language={language} labels=labels.jsonl "
+                f"(no labels.{language}.jsonl found — scoring against English ground truth)",
+                file=sys.stderr, flush=True,
+            )
+    labels = load_jsonl(labels_path)
+    if len(samples) != len(labels):
+        return Result(
+            model_tag=model_tag, task=task, mode=mode,
+            accuracy=0.0, extras={}, timing=aggregate_timings([]),
+            vram_resident_mb=None, vram_peak_mb=None, host=host,
+            run_date=run_date, n_samples=0, language=language,
+            error=f"Sample/label count mismatch: {len(samples)} vs {len(labels)}",
+        )
+
+    if task == "room_classification":
+        room_lists = load_jsonl(task_dir / "room_lists.jsonl")
+        if len(room_lists) != len(samples):
+            return Result(
+                model_tag=model_tag, task=task, mode=mode,
+                accuracy=0.0, extras={}, timing=aggregate_timings([]),
+                vram_resident_mb=None, vram_peak_mb=None, host=host,
+                run_date=run_date, n_samples=0, language=language,
+                error=f"Room-list count mismatch: {len(room_lists)} vs {len(samples)}",
+            )
+        for s, rl in zip(samples, room_lists):
+            s["__rooms__"] = rl["rooms"]
+
+    if n_samples is not None:
+        samples = samples[:n_samples]
+        labels = labels[:n_samples]
+
+    if (task, mode) in _EMBED_TASKS:
+        try:
+            _ensure_embed_model(embed_endpoint, model=embed_model)
+        except (RuntimeError, subprocess.TimeoutExpired, FileNotFoundError) as e:
+            return Result(
+                model_tag=model_tag, task=task, mode=mode,
+                accuracy=0.0, extras={}, timing=aggregate_timings([]),
+                vram_resident_mb=None, vram_peak_mb=None, host=host,
+                run_date=run_date, n_samples=0, language=language,
+                error=f"Embedding model unavailable: {e}",
+            )
+
+    try:
+        provider_kwargs: dict = {}
+        if num_ctx is not None:
+            provider_kwargs["num_ctx"] = num_ctx
+        provider = get_provider(llm_provider, model=model_tag, endpoint=endpoint, timeout=180, **provider_kwargs)
+    except LLMError as e:
+        return Result(
+            model_tag=model_tag, task=task, mode=mode,
+            accuracy=0.0, extras={}, timing=aggregate_timings([]),
+            vram_resident_mb=None, vram_peak_mb=None, host=host,
+            run_date=run_date, n_samples=0, language=language,
+            error=f"Provider init failed: {e}",
+        )
+
+    if warmup > 0 and samples:
+        s0, l0 = samples[0], labels[0]
+        try:
+            sys_p, user_p, json_mode = _build_prompt(task, mode, s0, l0)
+            for _ in range(warmup):
+                provider.classify(system=sys_p, user=user_p, json_mode=json_mode, think=False)
+        except LLMError as e:
+            return Result(
+                model_tag=model_tag, task=task, mode=mode,
+                accuracy=0.0, extras={}, timing=aggregate_timings([]),
+                vram_resident_mb=None, vram_peak_mb=None, host=host,
+                run_date=run_date, n_samples=0, language=language,
+                error=f"Warmup failed: {e}",
+            )
+
+    poller = VRAMPoller()
+    poller.start()
+
+    timings: list[TimingSample] = []
+    scores: list[dict] = []
+    errors: list[str] = []
+
+    for sample, label in zip(samples, labels):
+        sys_p, user_p, json_mode = _build_prompt(task, mode, sample, label)
+        response, timing, err = _classify_with_timing(provider, sys_p, user_p, json_mode)
+        if err is not None:
+            errors.append(err)
+            continue
+        text = response.text
+        if strip_thinking:
+            text = strip_thinking_tokens(text, response.raw)
+        timings.append(timing)
+        try:
+            score_result = _score_one(task, mode, text, sample, label, embed_endpoint, embed_model=embed_model)
+            scores.append(score_result)
+        except Exception as e:
+            errors.append(f"score error: {e}")
+
+    peak_vram = poller.stop()
+    # vram_resident_mb queries Ollama's /api/ps; meaningless for non-Ollama providers.
+    if llm_provider == "ollama":
+        resident_vram = vram_resident_mb(model_tag, endpoint=endpoint)
+    else:
+        resident_vram = None
+
+    accuracy, extras = _aggregate_accuracy(task, mode, scores)
+    if errors:
+        extras["error_count"] = len(errors)
+        extras["error_sample"] = errors[0]
+
+    return Result(
+        model_tag=model_tag,
+        task=task,
+        mode=mode,
+        accuracy=accuracy,
+        extras=extras,
+        timing=aggregate_timings(timings),
+        vram_resident_mb=resident_vram,
+        vram_peak_mb=peak_vram,
+        host=host,
+        run_date=run_date,
+        n_samples=len(scores),
+        language=language,
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Run one (model, task, mode) benchmark triple")
+    parser.add_argument("--model", required=True, help="Ollama model tag")
+    parser.add_argument("--task", required=True, choices=["room_classification", "entity_extraction", "memory_extraction", "calibration"])
+    parser.add_argument("--mode", default="closed", choices=["closed", "open", "default"], help="Mode: closed/open for room_classification, 'default' otherwise")
+    parser.add_argument("--dataset-dir", required=True, type=Path, help="Path to the bench dataset root")
+    parser.add_argument("--endpoint", default="http://localhost:11434")
+    parser.add_argument("--warmup", type=int, default=1)
+    parser.add_argument("--n", type=int, default=None, help="Limit to first N samples (for debugging)")
+    parser.add_argument("--no-strip-thinking", action="store_true")
+    parser.add_argument("--llm-provider", default="ollama",
+                        choices=["ollama", "openai-compat", "anthropic"],
+                        help="LLM provider (default: ollama)")
+    parser.add_argument("--embed-endpoint", default=None,
+                        help="Endpoint for the embedding model (always Ollama). "
+                             "When omitted: defaults to --endpoint if --llm-provider=ollama, "
+                             "else http://localhost:11434.")
+    parser.add_argument("--language", default="en",
+                        help="Dataset language suffix. 'en' loads dataset.jsonl; "
+                             "other values load dataset.{lang}.jsonl (e.g. pt-BR, es, zh).")
+    parser.add_argument("--embed-model", default=_EMBED_MODEL,
+                        help=f"Embedding model for semantic-similarity scoring "
+                             f"(memory_extraction, room_classification:open). "
+                             f"Default: {_EMBED_MODEL}.")
+    parser.add_argument("--num-ctx", type=int, default=4096,
+                        help="Ollama context window per request (sent as options.num_ctx). "
+                             "Defaults to 4096 so every candidate runs at the same window regardless "
+                             "of its Modelfile default — without this, a 32k-default model pre-allocates "
+                             "KV cache that a 4k-default model doesn't, and accuracy/latency/VRAM stop "
+                             "being comparable. Pass a larger value if a task prompt exceeds 4k tokens.")
+    args = parser.parse_args()
+
+    if args.task != "room_classification" and args.mode in ("closed", "open"):
+        args.mode = "default"
+
+    # Resolve --embed-endpoint default: it lives on Ollama always, but should
+    # follow --endpoint when the LLM provider is Ollama so a remote benchmark
+    # run scores against the same host.
+    if args.embed_endpoint is None:
+        args.embed_endpoint = args.endpoint if args.llm_provider == "ollama" else "http://localhost:11434"
+
+    result = run(
+        model_tag=args.model,
+        task=args.task,
+        mode=args.mode,
+        dataset_dir=args.dataset_dir,
+        endpoint=args.endpoint,
+        warmup=args.warmup,
+        n_samples=args.n,
+        strip_thinking=not args.no_strip_thinking,
+        llm_provider=args.llm_provider,
+        embed_endpoint=args.embed_endpoint,
+        language=args.language,
+        embed_model=args.embed_model,
+        num_ctx=args.num_ctx,
+    )
+
+    print(json.dumps(_result_to_dict(result), indent=2))
+    if result.error:
+        sys.exit(1)
+
+
+def _result_to_dict(r: Result) -> dict:
+    return {
+        "model_tag": r.model_tag,
+        "task": r.task,
+        "mode": r.mode,
+        "language": r.language,
+        "n_samples": r.n_samples,
+        "accuracy": round(r.accuracy, 4),
+        "extras": r.extras,
+        "timing": asdict(r.timing),
+        "vram_resident_mb": r.vram_resident_mb,
+        "vram_peak_mb": r.vram_peak_mb,
+        "host": asdict(r.host),
+        "run_date": r.run_date,
+        "error": r.error,
+    }
+
+
+if __name__ == "__main__":
+    main()

benchmarks/model_eval/summarize.py

@@ -0,0 +1,335 @@
+"""Render a benchmark CSV as a readable markdown report.
+
+Usage:
+    python -m benchmarks.model_eval.summarize \\
+        --csv benchmarks/model_eval/results/2026-05-10-host.csv \\
+        --output benchmarks/model_eval/reports/2026-05-10-host.md
+
+The report includes:
+- Per-task accuracy rankings
+- Per-task speed rankings (e2e p50, TPS p50)
+- VRAM consumption table
+- Combined production recommendation (accuracy ≥ 0.8 AND e2e_p50 < 500ms)
+- Open-set viability (does any model meet the discover-mode threshold?)
+- Instruct vs reasoning comparison for qwen3:4b pair
+"""
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+from collections import defaultdict
+from pathlib import Path
+from typing import Optional
+
+
+def load_rows(path: Path) -> list[dict]:
+    with open(path, "r", encoding="utf-8", newline="") as f:
+        return list(csv.DictReader(f))
+
+
+def fmt(v, decimals: int = 2, default: str = "—") -> str:
+    if v is None or v == "":
+        return default
+    try:
+        f = float(v)
+        return f"{f:.{decimals}f}"
+    except (ValueError, TypeError):
+        return str(v)
+
+
+def rank_by(rows: list[dict], task: str, mode: str, key: str, reverse: bool = True) -> list[dict]:
+    filtered = [r for r in rows if r["task"] == task and r["mode"] == mode and not r.get("error")]
+    return sorted(filtered, key=lambda r: float(r.get(key) or 0), reverse=reverse)
+
+
+def render_accuracy_table(rows: list[dict], task: str, mode: str, primary_key: str = "accuracy") -> str:
+    ranked = rank_by(rows, task, mode, primary_key, reverse=True)
+    if not ranked:
+        return f"_No successful runs for {task}/{mode}._\n"
+
+    lines = []
+    extras_keys = sorted({k for r in ranked for k in json.loads(r.get("extras_json") or "{}").keys()})
+    extras_keys = [k for k in extras_keys if k not in {"correct", "total", "error_count", "error_sample"}][:4]
+
+    header = ["Rank", "Model", primary_key]
+    if extras_keys:
+        header.extend(extras_keys)
+    header.extend(["e2e p50 ms", "TPS p50", "VRAM resident MB"])
+    lines.append("| " + " | ".join(header) + " |")
+    lines.append("|" + "|".join(["---"] * len(header)) + "|")
+
+    for i, r in enumerate(ranked, 1):
+        extras = json.loads(r.get("extras_json") or "{}")
+        row = [str(i), r["model_tag"], fmt(r[primary_key], 3)]
+        for k in extras_keys:
+            row.append(fmt(extras.get(k), 2))
+        row.extend([fmt(r.get("e2e_p50_ms"), 1), fmt(r.get("tps_p50"), 1), r.get("vram_resident_mb") or "—"])
+        lines.append("| " + " | ".join(row) + " |")
+    return "\n".join(lines) + "\n"
+
+
+def render_speed_table(rows: list[dict], task: str, mode: str) -> str:
+    valid = [r for r in rows if r["task"] == task and r["mode"] == mode and not r.get("error") and r.get("e2e_p50_ms")]
+    ranked = sorted(valid, key=lambda r: float(r.get("e2e_p50_ms") or 99999))
+    if not ranked:
+        return ""
+
+    lines = ["| Rank | Model | e2e p50 ms | e2e p95 ms | TTFT p50 ms | TPS p50 | TPS p95 |",
+             "|---|---|---|---|---|---|---|"]
+    for i, r in enumerate(ranked, 1):
+        lines.append(
+            f"| {i} | {r['model_tag']} | "
+            f"{fmt(r['e2e_p50_ms'], 1)} | {fmt(r['e2e_p95_ms'], 1)} | "
+            f"{fmt(r['ttft_p50_ms'], 1)} | {fmt(r['tps_p50'], 1)} | {fmt(r['tps_p95'], 1)} |"
+        )
+    return "\n".join(lines) + "\n"
+
+
+def render_vram_table(rows: list[dict]) -> str:
+    by_model: dict[str, dict] = {}
+    for r in rows:
+        if r.get("error"):
+            continue
+        tag = r["model_tag"]
+        if tag not in by_model:
+            by_model[tag] = {"resident": r.get("vram_resident_mb"), "peak": r.get("vram_peak_mb")}
+        else:
+            cur_peak = by_model[tag]["peak"]
+            new_peak = r.get("vram_peak_mb")
+            if new_peak and (not cur_peak or int(new_peak) > int(cur_peak)):
+                by_model[tag]["peak"] = new_peak
+            if not by_model[tag]["resident"] and r.get("vram_resident_mb"):
+                by_model[tag]["resident"] = r.get("vram_resident_mb")
+
+    rows_sorted = sorted(
+        by_model.items(),
+        key=lambda kv: int(kv[1]["resident"] or 0),
+    )
+    lines = ["| Model | Resident MB | Peak MB | Delta MB |", "|---|---|---|---|"]
+    for tag, vram in rows_sorted:
+        resident = vram["resident"]
+        peak = vram["peak"]
+        try:
+            delta = int(peak) - int(resident) if resident and peak else None
+        except (TypeError, ValueError):
+            delta = None
+        lines.append(f"| {tag} | {resident or '—'} | {peak or '—'} | {delta if delta is not None else '—'} |")
+    return "\n".join(lines) + "\n"
+
+
+def render_production_picks(rows: list[dict], min_acc: float = 0.80, max_e2e_ms: float = 500) -> str:
+    """Models that meet a quality threshold AND a speed threshold across all tasks."""
+    by_model: dict[str, list[dict]] = defaultdict(list)
+    for r in rows:
+        if r.get("error"):
+            continue
+        by_model[r["model_tag"]].append(r)
+
+    picks = []
+    for tag, model_rows in by_model.items():
+        primary_metrics = []
+        e2e_max = 0.0
+        for r in model_rows:
+            if r["task"] == "memory_extraction":
+                # coverage is the primary; mean_coverage is in extras
+                extras = json.loads(r.get("extras_json") or "{}")
+                primary_metrics.append(extras.get("mean_coverage", 0.0))
+            elif r["task"] == "entity_extraction":
+                primary_metrics.append(float(r.get("accuracy") or 0))
+            elif r["task"] == "room_classification" and r["mode"] == "open":
+                # similarity is the primary
+                primary_metrics.append(float(r.get("accuracy") or 0))
+            else:
+                primary_metrics.append(float(r.get("accuracy") or 0))
+            e2e = float(r.get("e2e_p50_ms") or 0)
+            if e2e > e2e_max:
+                e2e_max = e2e
+        if not primary_metrics:
+            continue
+        avg_metric = sum(primary_metrics) / len(primary_metrics)
+        meets_acc = all(m >= min_acc for m in primary_metrics)
+        meets_speed = e2e_max <= max_e2e_ms
+        if meets_acc and meets_speed:
+            picks.append((tag, avg_metric, e2e_max))
+
+    picks.sort(key=lambda x: -x[1])
+
+    if not picks:
+        return f"_No model met both thresholds (min_acc={min_acc}, max_e2e={max_e2e_ms}ms across all tasks)._\n"
+
+    lines = [f"Models meeting min_accuracy ≥ {min_acc} on every task AND e2e p50 ≤ {max_e2e_ms}ms:\n"]
+    lines.append("| Rank | Model | Avg primary metric | Worst e2e p50 ms |")
+    lines.append("|---|---|---|---|")
+    for i, (tag, avg, e2e) in enumerate(picks, 1):
+        lines.append(f"| {i} | {tag} | {avg:.3f} | {e2e:.1f} |")
+    return "\n".join(lines) + "\n"
+
+
+def render_instruct_vs_reasoning(rows: list[dict]) -> str:
+    instruct = [r for r in rows if r["model_tag"].startswith("qwen3:4b-instruct-2507") and not r.get("error")]
+    reasoning = [r for r in rows if r["model_tag"].startswith("qwen3:4b-thinking") and not r.get("error")]
+
+    if not instruct or not reasoning:
+        return "_Not enough qwen3:4b paired results to compare._\n"
+
+    inst_q4 = next((r for r in instruct if r["model_tag"] == "qwen3:4b-instruct-2507-q4_K_M"), None)
+    reas_q4 = next((r for r in reasoning if r["model_tag"] == "qwen3:4b-thinking-2507-q4_K_M"), None)
+
+    if not inst_q4 or not reas_q4:
+        return "_qwen3:4b-instruct-2507-q4_K_M vs qwen3:4b-thinking-2507-q4_K_M comparison unavailable._\n"
+
+    by_pair: dict[tuple[str, str], dict[str, dict]] = defaultdict(dict)
+    for r in instruct + reasoning:
+        if r["model_tag"] not in {"qwen3:4b-instruct-2507-q4_K_M", "qwen3:4b-thinking-2507-q4_K_M"}:
+            continue
+        by_pair[(r["task"], r["mode"])][r["model_tag"]] = r
+
+    lines = ["Direct comparison: qwen3:4b instruct vs reasoning at q4_K_M.\n"]
+    lines.append("| Task | Mode | Instruct accuracy | Reasoning accuracy | Instruct e2e p50 | Reasoning e2e p50 |")
+    lines.append("|---|---|---|---|---|---|")
+    for (task, mode), pair in sorted(by_pair.items()):
+        inst = pair.get("qwen3:4b-instruct-2507-q4_K_M")
+        reas = pair.get("qwen3:4b-thinking-2507-q4_K_M")
+        if not inst or not reas:
+            continue
+        lines.append(
+            f"| {task} | {mode} | "
+            f"{fmt(inst['accuracy'], 3)} | {fmt(reas['accuracy'], 3)} | "
+            f"{fmt(inst['e2e_p50_ms'], 1)} | {fmt(reas['e2e_p50_ms'], 1)} |"
+        )
+    return "\n".join(lines) + "\n"
+
+
+def render_open_set_viability(rows: list[dict], min_similarity: float = 0.7) -> str:
+    open_runs = [r for r in rows if r["task"] == "room_classification" and r["mode"] == "open" and not r.get("error")]
+    if not open_runs:
+        return "_No open-set runs available._\n"
+
+    qualified = [r for r in open_runs if float(r.get("accuracy") or 0) >= min_similarity]
+    lines = [f"Open-set discovery viability. Threshold: mean cosine similarity ≥ {min_similarity}.\n"]
+
+    if not qualified:
+        lines.append(f"**No model met the threshold.** Best score: {max(float(r.get('accuracy') or 0) for r in open_runs):.3f}.")
+        lines.append("\nRecommendation: do NOT ship `mempalace mine --mode discover`. Closed-set classification stays required.\n")
+        return "\n".join(lines)
+
+    lines.append(f"**{len(qualified)} model(s) met the threshold.**\n")
+    lines.append("| Model | Mean similarity | Exact match count | High-sim (≥0.8) | Low-sim (<0.5) |")
+    lines.append("|---|---|---|---|---|")
+    for r in sorted(qualified, key=lambda r: -float(r.get("accuracy") or 0)):
+        extras = json.loads(r.get("extras_json") or "{}")
+        lines.append(
+            f"| {r['model_tag']} | "
+            f"{fmt(r['accuracy'], 3)} | "
+            f"{extras.get('exact_match_count', '—')} | "
+            f"{extras.get('high_similarity_count', '—')} | "
+            f"{extras.get('low_similarity_count', '—')} |"
+        )
+    lines.append(f"\nRecommendation: ship `mempalace mine --mode discover` with the top model as default.\n")
+    return "\n".join(lines)
+
+
+def render_report(rows: list[dict]) -> str:
+    if not rows:
+        return "# Empty results\n\nNo rows in the CSV.\n"
+
+    sample = next((r for r in rows if not r.get("error")), rows[0])
+    host = sample.get("host", "unknown")
+    gpu = sample.get("gpu", "unknown")
+    ollama_v = sample.get("ollama_version", "unknown")
+    run_date = sample.get("run_date", "unknown")
+
+    n_models = len({r["model_tag"] for r in rows})
+    n_runs = len(rows)
+    n_errors = sum(1 for r in rows if r.get("error"))
+
+    sections = [
+        f"# Model evaluation report",
+        "",
+        f"- Host: `{host}`",
+        f"- GPU: `{gpu}`",
+        f"- Ollama: `{ollama_v}`",
+        f"- Run date: {run_date}",
+        f"- Runs: {n_runs} ({n_models} models × task/mode pairs); errors: {n_errors}",
+        "",
+        "## Production picks",
+        "",
+        render_production_picks(rows),
+        "",
+        "## Open-set discovery viability",
+        "",
+        render_open_set_viability(rows),
+        "",
+        "## Instruct vs reasoning (qwen3:4b)",
+        "",
+        render_instruct_vs_reasoning(rows),
+        "",
+        "## Per-task rankings",
+        "",
+        "### Calibration (sentence-type, exact match)",
+        "",
+        render_accuracy_table(rows, "calibration", "default"),
+        "",
+        "### Room classification — closed-set (exact match)",
+        "",
+        render_accuracy_table(rows, "room_classification", "closed"),
+        "",
+        "### Room classification — open-set (cosine similarity)",
+        "",
+        render_accuracy_table(rows, "room_classification", "open"),
+        "",
+        "### Entity extraction (mean F1)",
+        "",
+        render_accuracy_table(rows, "entity_extraction", "default"),
+        "",
+        "### Memory extraction (mean coverage)",
+        "",
+        render_accuracy_table(rows, "memory_extraction", "default"),
+        "",
+        "## Speed (calibration, smallest task — most stable timing)",
+        "",
+        render_speed_table(rows, "calibration", "default"),
+        "",
+        "## VRAM",
+        "",
+        render_vram_table(rows),
+        "",
+    ]
+
+    errored = [r for r in rows if r.get("error")]
+    if errored:
+        sections.extend([
+            "## Errors",
+            "",
+            "| Model | Task | Mode | Error |",
+            "|---|---|---|---|",
+        ])
+        for r in errored:
+            err_short = r["error"][:120].replace("|", "\\|")
+            sections.append(f"| {r['model_tag']} | {r['task']} | {r['mode']} | {err_short} |")
+        sections.append("")
+
+    return "\n".join(sections)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Render benchmark CSV as a markdown report")
+    parser.add_argument("--csv", required=True, type=Path)
+    parser.add_argument("--output", type=Path, default=None)
+    args = parser.parse_args()
+
+    rows = load_rows(args.csv)
+    report = render_report(rows)
+
+    if args.output:
+        args.output.parent.mkdir(parents=True, exist_ok=True)
+        with open(args.output, "w", encoding="utf-8", newline="\n") as f:
+            f.write(report)
+        print(f"Wrote {args.output}")
+    else:
+        print(report)
+
+
+if __name__ == "__main__":
+    main()

benchmarks/model_eval/tasks/calibration/prompts.py

@@ -0,0 +1,18 @@
+"""Calibration task: 5-class sentence-type classification.
+
+Used as a sanity check for the harness. Any decent ≤4B instruct model
+should ace this; if accuracy is poor, the harness is broken before the
+real tasks even run.
+"""
+from __future__ import annotations
+
+
+SYSTEM = """You are a sentence classifier. Respond with exactly one word from the provided class list. No explanation, no punctuation, no quotes."""
+
+
+def build_user_prompt(text: str, classes: list[str]) -> str:
+    return f"""Classes: {", ".join(classes)}
+
+Sentence: {text}
+
+Class:"""

benchmarks/model_eval/tasks/calibration/score.py

@@ -0,0 +1,16 @@
+"""Calibration scoring: exact-match against single-class label."""
+from __future__ import annotations
+
+
+def score(predicted: str, label: str, classes: list[str]) -> dict:
+    """Return {"correct": bool, "predicted_normalized": str}."""
+    p = predicted.strip().lower().strip(".,;:'\"")
+    target = label.strip().lower()
+    correct = p == target
+    if not correct:
+        for c in classes:
+            if p == c.strip().lower():
+                # Predicted a valid class, just not the target.
+                return {"correct": False, "predicted_normalized": p}
+        return {"correct": False, "predicted_normalized": p}
+    return {"correct": True, "predicted_normalized": p}

benchmarks/model_eval/tasks/entity_extraction/prompts.py

@@ -0,0 +1,18 @@
+"""Entity extraction prompts. Output is JSON; we use json_mode=True."""
+from __future__ import annotations
+
+
+SYSTEM = """You extract entities from text. Return ONLY valid JSON in this exact shape:
+
+{"entities": [{"name": "...", "type": "..."}]}
+
+Entity types are limited to: person, project, place, organization.
+
+Do not invent entities not in the text. Do not include common nouns or generic concepts. Only proper-noun entities."""
+
+
+def build_user(text: str) -> str:
+    return f"""Text:
+{text}
+
+Extract all entities."""

benchmarks/model_eval/tasks/entity_extraction/score.py

@@ -0,0 +1,63 @@
+"""Entity extraction scoring: F1 over (name, type) pairs.
+
+Strict matching: predicted (name, type) must exactly equal a ground-truth
+(name, type) pair to count as true positive. Case-insensitive on both
+name and type. Names are normalized for whitespace.
+"""
+from __future__ import annotations
+
+import json
+
+
+def _norm(name: str) -> str:
+    return " ".join(name.split()).lower()
+
+
+def _parse(predicted_text: str) -> list[tuple[str, str]]:
+    try:
+        data = json.loads(predicted_text)
+    except json.JSONDecodeError:
+        return []
+    entities = data.get("entities", []) if isinstance(data, dict) else []
+    out = []
+    for e in entities:
+        if not isinstance(e, dict):
+            continue
+        name = e.get("name", "")
+        etype = e.get("type", "")
+        if isinstance(name, str) and isinstance(etype, str) and name and etype:
+            out.append((_norm(name), etype.strip().lower()))
+    return out
+
+
+def score(predicted_text: str, ground_truth: list[dict]) -> dict:
+    """Return F1 metrics. ground_truth is a list of {"name": ..., "type": ...}."""
+    pred = set(_parse(predicted_text))
+    truth = {(_norm(e["name"]), e["type"].strip().lower()) for e in ground_truth}
+
+    tp = len(pred & truth)
+    fp = len(pred - truth)
+    fn = len(truth - pred)
+
+    precision = tp / (tp + fp) if (tp + fp) else 0.0
+    recall = tp / (tp + fn) if (tp + fn) else 0.0
+    f1 = (2 * precision * recall / (precision + recall)) if (precision + recall) else 0.0
+
+    return {
+        "f1": f1,
+        "precision": precision,
+        "recall": recall,
+        "true_positives": tp,
+        "false_positives": fp,
+        "false_negatives": fn,
+        "valid_json": len(pred) > 0 or _is_valid_empty(predicted_text),
+    }
+
+
+def _is_valid_empty(text: str) -> bool:
+    """Empty entity list is still valid output if JSON parses."""
+    try:
+        data = json.loads(text)
+    except json.JSONDecodeError:
+        return False
+    return isinstance(data, dict) and isinstance(data.get("entities", None), list)

benchmarks/model_eval/tasks/memory_extraction/prompts.py

@@ -0,0 +1,24 @@
+"""Memory extraction prompts. JSON output."""
+from __future__ import annotations
+
+
+SYSTEM = """You extract memory-worthy items from agent conversations. Return ONLY valid JSON:
+
+{"memories": [{"type": "...", "content": "..."}]}
+
+Memory types: decision, preference, fact, opinion, commitment.
+
+- decision: a choice made between alternatives
+- preference: a stated like/dislike or way-of-doing
+- fact: an objective piece of information about a person/project/world
+- opinion: a subjective judgment
+- commitment: a promise, plan, or scheduled action
+
+Only include items that would be useful to remember. Skip pleasantries, greetings, and generic discussion."""
+
+
+def build_user(text: str) -> str:
+    return f"""Text:
+{text}
+
+Extract memory items."""

benchmarks/model_eval/tasks/memory_extraction/score.py

@@ -0,0 +1,112 @@
+"""Memory extraction scoring.
+
+Looser than entity F1 because content paraphrasing is expected. We score:
+- Type accuracy: per-item, did the model assign the right memory type?
+- Coverage: percentage of ground-truth items that have a corresponding
+  predicted item with semantic match (cosine similarity > 0.6 on content)
+- Hallucination rate: predicted items with no semantic match in truth
+"""
+from __future__ import annotations
+
+import json
+
+from ...metrics import cosine_similarity, embed_text
+
+
+def _parse(predicted_text: str) -> list[dict]:
+    try:
+        data = json.loads(predicted_text)
+    except json.JSONDecodeError:
+        return []
+    items = data.get("memories", []) if isinstance(data, dict) else []
+    return [m for m in items if isinstance(m, dict)]
+
+
+def score(
+    predicted_text: str,
+    ground_truth: list[dict],
+    similarity_threshold: float = 0.6,
+    embed_model: str = "nomic-embed-text",
+    endpoint: str = "http://localhost:11434",
+) -> dict:
+    pred = _parse(predicted_text)
+    truth = [m for m in ground_truth if isinstance(m, dict)]
+
+    if not truth:
+        return {
+            "coverage": 0.0,
+            "hallucination_rate": 1.0 if pred else 0.0,
+            "type_accuracy": 0.0,
+            "predicted_count": len(pred),
+            "truth_count": 0,
+            "valid_json": _is_valid(predicted_text),
+        }
+
+    # Pre-compute embeddings once per content string. Cuts API calls from
+    # O(P*T) to O(P+T) by separating the embedding step from the pairwise
+    # comparison. Important for cloud endpoints where each embed call is
+    # an HTTP round-trip.
+    truth_embs: list[list[float] | None] = []
+    for t in truth:
+        t_content = t.get("content", "")
+        if isinstance(t_content, str) and t_content:
+            truth_embs.append(embed_text(t_content, model=embed_model, endpoint=endpoint))
+        else:
+            truth_embs.append(None)
+
+    pred_embs: list[list[float] | None] = []
+    for p in pred:
+        p_content = p.get("content", "")
+        if isinstance(p_content, str) and p_content:
+            pred_embs.append(embed_text(p_content, model=embed_model, endpoint=endpoint))
+        else:
+            pred_embs.append(None)
+
+    matched_truth_indices: set[int] = set()
+    matched_pred_indices: set[int] = set()
+    type_correct = 0
+    type_total = 0
+
+    for pi, p in enumerate(pred):
+        p_emb = pred_embs[pi]
+        if p_emb is None:
+            continue
+        p_type = p.get("type", "").strip().lower()
+        best_sim = 0.0
+        best_ti = None
+        for ti in range(len(truth)):
+            if ti in matched_truth_indices or truth_embs[ti] is None:
+                continue
+            sim = cosine_similarity(p_emb, truth_embs[ti])
+            if sim > best_sim:
+                best_sim = sim
+                best_ti = ti
+        if best_ti is not None and best_sim >= similarity_threshold:
+            matched_truth_indices.add(best_ti)
+            matched_pred_indices.add(pi)
+            type_total += 1
+            t_type = truth[best_ti].get("type", "").strip().lower()
+            if p_type == t_type:
+                type_correct += 1
+
+    coverage = len(matched_truth_indices) / len(truth)
+    hallucination_rate = (len(pred) - len(matched_pred_indices)) / max(1, len(pred))
+    type_accuracy = type_correct / type_total if type_total else 0.0
+
+    return {
+        "coverage": coverage,
+        "hallucination_rate": hallucination_rate,
+        "type_accuracy": type_accuracy,
+        "predicted_count": len(pred),
+        "truth_count": len(truth),
+        "matched_count": len(matched_pred_indices),
+        "valid_json": _is_valid(predicted_text),
+    }
+
+
+def _is_valid(text: str) -> bool:
+    try:
+        data = json.loads(text)
+    except json.JSONDecodeError:
+        return False
+    return isinstance(data, dict) and isinstance(data.get("memories", None), list)

benchmarks/model_eval/tasks/room_classification/prompts.py

@@ -0,0 +1,38 @@
+"""Room classification prompts. Two modes:
+
+- closed: room list provided, model picks one or "other"
+- open: no list, model invents a slug
+"""
+from __future__ import annotations
+
+
+CLOSED_SYSTEM = """You are a room classifier for an AI agent's memory palace. Given a session excerpt and a list of rooms, choose the best-fitting room.
+
+Respond with EXACTLY one room slug from the list, copied verbatim (including any "/" or "-" characters), or the literal word "other" if no room fits well. No explanation, no quotes, no extra text."""
+
+
+OPEN_SYSTEM = """You are a room classifier for an AI agent's memory palace. Given a session excerpt and the agent's identity, INVENT a short room slug that captures the session's topic.
+
+Respond with EXACTLY one slug: lowercase, hyphenated, no spaces, no punctuation, no quotes. Example formats: "project-alpha", "code-review", "daily-logs"."""
+
+
+def build_closed_user(agent: str, session_summary: str, rooms: list[str]) -> str:
+    rooms_block = "\n".join(f"- {r}" for r in rooms)
+    return f"""Agent: {agent}
+
+Available rooms:
+{rooms_block}
+
+Session excerpt:
+{session_summary}
+
+Room:"""
+
+
+def build_open_user(agent: str, session_summary: str) -> str:
+    return f"""Agent: {agent}
+
+Session excerpt:
+{session_summary}
+
+Room slug:"""

benchmarks/model_eval/tasks/room_classification/score.py

@@ -0,0 +1,74 @@
+"""Room classification scoring.
+
+Closed-set: exact match against the labeled room slug, with "other" treated
+as a valid answer when ground truth is "other".
+
+Open-set: cosine similarity between the embedding of the predicted slug
+and the embedding of the preferred-open label. Scored 0-1.
+"""
+from __future__ import annotations
+
+from ...metrics import label_similarity
+
+
+def score_closed(predicted: str, target_label: str, rooms: list[str]) -> dict:
+    p = predicted.strip().lower().strip(".,;:'\"")
+    t = target_label.strip().lower()
+    correct = p == t
+    in_room_list = p == "other" or any(p == r.strip().lower() for r in rooms)
+    return {
+        "correct": correct,
+        "predicted_normalized": p,
+        "in_room_list": in_room_list,
+    }
+
+
+def score_open(
+    predicted: str,
+    preferred_label: str,
+    embed_model: str = "nomic-embed-text",
+    endpoint: str = "http://localhost:11434",
+) -> dict:
+    p = predicted.strip().lower().strip(".,;:'\"")
+    sim = label_similarity(p, preferred_label, embed_model=embed_model, endpoint=endpoint)
+    return {
+        "predicted_normalized": p,
+        "similarity": sim,
+        "exact_match": p == preferred_label.strip().lower(),
+    }
+
+
+def score_clustering(
+    predicted_labels: list[str], ground_truth_themes: list[str]
+) -> dict:
+    """Compute clustering quality across the corpus.
+
+    Reports unique-label count, label collision rate, and a simple
+    homogeneity proxy (samples sharing a ground-truth theme should share
+    a predicted label).
+    """
+    if len(predicted_labels) != len(ground_truth_themes):
+        raise ValueError("predicted_labels and ground_truth_themes must have the same length")
+
+    pred_norm = [p.strip().lower() for p in predicted_labels]
+    distinct_predicted = len(set(pred_norm))
+    distinct_themes = len(set(ground_truth_themes))
+
+    by_theme: dict[str, list[str]] = {}
+    for theme, pred in zip(ground_truth_themes, pred_norm):
+        by_theme.setdefault(theme, []).append(pred)
+
+    homogeneity_scores = []
+    for theme, preds in by_theme.items():
+        if len(preds) <= 1:
+            continue
+        most_common_count = max(preds.count(p) for p in set(preds))
+        homogeneity_scores.append(most_common_count / len(preds))
+    homogeneity = sum(homogeneity_scores) / len(homogeneity_scores) if homogeneity_scores else 0.0
+
+    return {
+        "distinct_predicted_labels": distinct_predicted,
+        "distinct_ground_truth_themes": distinct_themes,
+        "label_compression_ratio": distinct_themes / max(1, distinct_predicted),
+        "theme_homogeneity": homogeneity,
+    }

benchmarks/model_eval/translate_datasets.py

@@ -0,0 +1,213 @@
+"""Translate benchmark datasets to additional languages using an Ollama model.
+
+Usage:
+    uv run python -m benchmarks.model_eval.translate_datasets \\
+        --languages de,fr,it,ru,ko,hi \\
+        --model kimi-k2.6:cloud \\
+        --dataset-dir benchmarks/model_eval/datasets
+
+Only the natural-language input text is translated. Labels, IDs, class lists,
+room slugs, entity types, and memory types stay in English — they are system
+identifiers the model must emit regardless of input language.
+
+Proper nouns (fictional names, places, orgs, projects, tech terms, code blocks)
+are preserved verbatim in the translated output.
+
+⚠️  PRIVACY NOTE: The default model (`kimi-k2.6:cloud`) sends the prose to a
+remote Ollama-hosted endpoint. This is fine for the synthetic benchmark
+fixtures in this repo, but DO NOT run this script over real user data
+(diary entries, conversation transcripts, palace drawers). MemPalace is
+local-first by design — for real data, pass `--model` pointing to a
+locally-hosted model (e.g. `qwen3:4b-instruct-2507-q8_0`).
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from pathlib import Path
+
+import requests
+
+LANGUAGE_NAMES = {
+    "de": "German",
+    "fr": "French",
+    "it": "Italian",
+    "ru": "Russian",
+    "ko": "Korean",
+    "hi": "Hindi",
+    "es": "Spanish",
+    "pt-BR": "Brazilian Portuguese",
+    "zh": "Simplified Chinese",
+    "ja": "Japanese",
+    "ar": "Arabic",
+}
+
+# Which field in each task's dataset.jsonl contains the text to translate.
+TASK_TEXT_FIELD = {
+    "calibration": "text",
+    "entity_extraction": "text",
+    "memory_extraction": "text",
+    "room_classification": "session_summary",
+}
+
+_SYSTEM_PROMPT = """\
+You are a professional translator. Translate user-provided text accurately \
+and naturally into {language_name}.
+
+Strict rules — never violate these:
+- Preserve ALL of the following exactly as-is: personal names (Aria, Solas, \
+Bramble, Fenra, Thresh, Gera Vossen, Brennan Lyle, Mette Olafsen, Pol Krisat, \
+Ren Solanke, Ivora Tinn, Bek Halloran, Pell Halloran, Karis Tornau, \
+Yumara Felk, Doreth Ainsleigh, Iset Karadzic, Ralf Ginder, Saela, \
+Hellis Mar, and any other proper names); fictional place names (Crestmoor, \
+Wendelsea, Hollowmounts, Bridgewater, Aerwyn, Bryn-iili, Vroth-Karadz, \
+Krast-Endel, Salt Flats, Tartine); organization names (Tartine Lab, \
+Crestmoor Systems, Aerwyn Labs, Aerwyn Capital, Bridgewater Studio, \
+Hollowmounts Institute, Wendelsea Audit Partners, etc.); project names \
+(Embedding Spaces, Topic Clustering, Distributed Tracing, Type Systems, \
+Parser Combinators, Invoice Parsing, Cashflow Models, Pollinator Paths, \
+Native Species, Soil Microbes, Meta-Cognition, Faithful Chains, \
+Dialogue Engine, etc.); technical terms and acronyms (LLVM, OCaml, Python, \
+LaTeX, HDBSCAN, UMAP, t-SNE, IFRS, MACRS, GAAP, OCR, PR, CI, OTLP, gRPC, \
+OpenTelemetry, PaddleOCR, Tesseract, ZUGFeRD, MACRS, Section 179, etc.); \
+code blocks (```...``` or inline code); tracebacks and error messages.
+- Translate only the surrounding natural-language prose.
+- Return ONLY the translated text. No explanations, no labels, no quotes \
+around the result."""
+
+
+def translate(text: str, language_name: str, model: str, endpoint: str) -> str:
+    """Send one text to Ollama and return the translated string.
+
+    Uses streaming so cloud models with high latency don't hit the connection
+    timeout — tokens arrive incrementally instead of in one big response.
+    """
+    payload = {
+        "model": model,
+        "system": _SYSTEM_PROMPT.format(language_name=language_name),
+        "prompt": text,
+        "stream": True,
+        "options": {"temperature": 0.1},
+    }
+    # stream=True: connect timeout 30s, read timeout 120s per chunk.
+    # The read timeout resets on every received chunk, so long responses
+    # from slow cloud models don't abort mid-stream.
+    with requests.post(
+        f"{endpoint}/api/generate",
+        json=payload,
+        stream=True,
+        timeout=(30, 120),
+    ) as resp:
+        resp.raise_for_status()
+        parts = []
+        for line in resp.iter_lines():
+            if not line:
+                continue
+            chunk = json.loads(line)
+            parts.append(chunk.get("response", ""))
+            if chunk.get("done"):
+                break
+    return "".join(parts).strip()
+
+
+def _translate_one(args: tuple) -> tuple[int, str, str]:
+    """Worker: translate one sample. Returns (index, sample_id, translated_text).
+
+    Retries up to 3 times; on persistent failure returns the English source
+    so downstream code always has a string to work with (the sweep in
+    `translate_file` will warn about identical pairs).
+    """
+    idx, sample_id, text, language_name, model, endpoint = args
+    last_error: Exception | None = None
+    for _ in range(3):
+        try:
+            return idx, sample_id, translate(text, language_name, model, endpoint)
+        except Exception as e:
+            last_error = e
+            time.sleep(2)
+    print(f"    ERROR on {sample_id}: {last_error}", file=sys.stderr, flush=True)
+    return idx, sample_id, text  # fall back to English source
+
+
+def translate_file(
+    src: Path,
+    dst: Path,
+    field: str,
+    language_name: str,
+    model: str,
+    endpoint: str,
+    force: bool,
+    workers: int = 8,
+) -> None:
+    if dst.exists() and not force:
+        print(f"  skip (exists): {dst.name}", flush=True)
+        return
+
+    samples = [json.loads(line) for line in src.read_text(encoding="utf-8").splitlines() if line.strip()]
+    results = [None] * len(samples)
+
+    work = [
+        (i, s["id"], s.get(field, ""), language_name, model, endpoint)
+        for i, s in enumerate(samples)
+        if s.get(field)
+    ]
+
+    completed = 0
+    with ThreadPoolExecutor(max_workers=workers) as pool:
+        futures = {pool.submit(_translate_one, item): item[0] for item in work}
+        for future in as_completed(futures):
+            idx, sample_id, translated = future.result()
+            out = dict(samples[idx])
+            out[field] = translated
+            results[idx] = json.dumps(out, ensure_ascii=False)
+            completed += 1
+            print(f"  [{completed}/{len(samples)}] {sample_id}", flush=True)
+
+    # samples with no text field (shouldn't happen but be safe)
+    for i, s in enumerate(samples):
+        if results[i] is None:
+            results[i] = json.dumps(s, ensure_ascii=False)
+
+    dst.write_text("\n".join(results) + "\n", encoding="utf-8")
+    print(f"  wrote {dst}", flush=True)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Translate benchmark datasets to new languages")
+    parser.add_argument("--languages", required=True, help="Comma-separated language codes, e.g. de,fr,it,ru,ko,hi")
+    parser.add_argument("--model", default="kimi-k2.6:cloud", help="Ollama model tag to use for translation")
+    parser.add_argument("--endpoint", default="http://localhost:11434")
+    parser.add_argument("--dataset-dir", type=Path, default=Path(__file__).parent / "datasets")
+    parser.add_argument("--tasks", default="all", help="all or comma-separated: calibration,entity_extraction,memory_extraction,room_classification")
+    parser.add_argument("--force", action="store_true", help="Overwrite existing output files")
+    args = parser.parse_args()
+
+    langs = [code.strip() for code in args.languages.split(",") if code.strip()]
+    tasks = list(TASK_TEXT_FIELD.keys()) if args.tasks == "all" else [t.strip() for t in args.tasks.split(",")]
+
+    unknown_langs = [code for code in langs if code not in LANGUAGE_NAMES]
+    if unknown_langs:
+        print(f"Unknown language codes: {unknown_langs}. Add them to LANGUAGE_NAMES in this script.", file=sys.stderr)
+        sys.exit(1)
+
+    for lang in langs:
+        lang_name = LANGUAGE_NAMES[lang]
+        print(f"\n=== {lang_name} ({lang}) ===", flush=True)
+        for task in tasks:
+            field = TASK_TEXT_FIELD[task]
+            src = args.dataset_dir / task / "dataset.jsonl"
+            dst = args.dataset_dir / task / f"dataset.{lang}.jsonl"
+            if not src.exists():
+                print(f"  source missing: {src}", file=sys.stderr)
+                continue
+            print(f"\n  {task} [{field}] → {dst.name}", flush=True)
+            translate_file(src, dst, field, lang_name, args.model, args.endpoint, args.force)
+
+    print("\nDone.", flush=True)
+
+
+if __name__ == "__main__":
+    main()