Three changes:
1. ABB-Space registered as a lead project with stage=lead in
Trusted Project State. Projects now have lifecycle awareness
(lead/proposition vs active contract vs completed).
2. Extraction no longer drops unregistered project tags. When the
LLM extractor sees a conversation about a project not in the
registry, it keeps the model's tag on the candidate instead of
falling back to empty. This enables auto-detection of new
projects/leads from organic conversations. The nightly pipeline
surfaces these candidates for triage, where the operator sees
"hey, there's a new project called X" and can decide whether
to register it.
3. Extraction prompt updated to tell the model: "If the conversation
discusses a project NOT in the known list, still tag it — the
system will auto-detect it." This removes the artificial ceiling
that prevented new project discovery.
Updated Case D test: unregistered + unscoped now keeps the model's
tag instead of dropping to empty.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Batch 3, Days 1-3. The core R9 failure was Case F: when the model
returned a registered project DIFFERENT from the interaction's
known scope, the old code trusted the model because the project
was registered. A p06-polisher interaction could silently produce
a p04-gigabit candidate.
New rule (trust hierarchy):
1. Interaction scope always wins when set (cases A, C, E, F)
2. Model project used only for unscoped interactions AND only when
it resolves to a registered project (cases D, G)
3. Empty string when both are empty or unregistered (case B)
The rule is: interaction.project is the strongest signal because
it comes from the capture hook's project detection, which runs
before the LLM ever sees the content. The model's project guess
is only useful when the capture hook had no project context.
7 case tests (A-G) cover every combination of model/interaction
project state. Pre-existing tests updated for the new behavior.
Host-side script mirrors the same hierarchy using _known_projects
fetched from GET /projects at startup.
Test count: 286 -> 290 (+4 net, 7 new R9 cases, 3 old tests
consolidated).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
R7: ranking scorer now uses overlap-density (overlap_count /
memory_token_count) as primary key instead of raw overlap count.
A 5-token memory with 3 overlapping tokens (density 0.6) now beats
a 40-token overview memory with 3 overlapping tokens (density 0.075)
at the same absolute count. Secondary: absolute overlap. Tertiary:
confidence. Targeting p06-firmware-interface harness fixture.
R9: when the LLM extractor returns a project that differs from the
interaction's known project, it now checks the project registry.
If the model's project is a registered canonical ID, trust it. If
not (hallucinated name), fall back to the interaction's project.
Uses load_project_registry() for the check. The host-side script
mirrors this via an API call to GET /projects at startup.
Two new tests: test_parser_keeps_registered_model_project and
test_parser_rejects_hallucinated_project.
Test count: 280 -> 281.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Codex R6: the LLM extractor accepted the model's project field
verbatim. When the model returned empty string, clearly p06 memories
got promoted as project='', making them invisible to the p06
project-memory band and explaining the p06-offline-design harness
failure.
Fix: if model returns empty project but interaction.project is set,
inherit the interaction's project. Model-supplied project still takes
precedence when non-empty.
Two new tests lock the fallback and precedence behaviors.
R5 acknowledged (LLM extractor not yet wired into API — next task).
Test count: 278 -> 280. Harness re-run pending after deploy.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Second pass on the LLM-assisted extractor after Antoine's explicit
rule: no API key, ever. Refactored src/atocore/memory/extractor_llm.py
to shell out to the Claude Code 'claude -p' CLI via subprocess instead
of the anthropic SDK, so extraction reuses the user's existing Claude.ai
OAuth credentials and needs zero secret management.
Implementation:
- subprocess.run(["claude", "-p", "--model", "haiku",
"--append-system-prompt", <instructions>,
"--no-session-persistence", "--disable-slash-commands",
user_message], ...)
- cwd is a cached tempfile.mkdtemp() so every invocation starts with
a clean context instead of auto-discovering CLAUDE.md / AGENTS.md /
DEV-LEDGER.md from the repo root. We cannot use --bare because it
forces API-key auth, which defeats the purpose; the temp-cwd trick
is the lightest way to keep OAuth auth while skipping project
context loading.
- Silent-failure contract unchanged: missing CLI, non-zero exit,
timeout, malformed JSON — all return [] and log an error. The
capture audit trail must not break on an optional side effect.
- Default timeout bumped from 20s to 90s: Haiku + Node.js startup
+ OAuth check is ~20-40s per call in practice, plus real responses
up to 8KB take longer. 45s hit 2 timeouts on the first live run.
- tests/test_extractor_llm.py refactored: the API-key / anthropic SDK
tests are replaced by subprocess-mocking tests covering missing
CLI, timeout, non-zero exit, and a happy-path stdout parse. 14
tests, all green.
scripts/extractor_eval.py:
- New --output <path> flag writes the JSON result directly to a file,
bypassing stdout/log interleaving (structlog sends INFO to stdout
via PrintLoggerFactory, so a naive '> out.json' pollutes the file).
- Forces UTF-8 on stdout so real LLM output with em-dashes / arrows /
CJK doesn't crash the human report on Windows cp1252 consoles.
First live baseline run against the 20-interaction labeled corpus
(scripts/eval_data/extractor_llm_baseline_2026-04-11.json):
mode=llm labeled=20 recall=1.0 precision=0.357 yield_rate=2.55
total_actual_candidates=51 total_expected_candidates=7
false_negative_interactions=0 false_positive_interactions=9
Recall 0% -> 100% vs rule baseline — every human-labeled positive is
caught. Precision reads low (0.357) but inspection shows the "false
positives" are real candidates the human labels under-counted. For
example interaction a6b0d279 was labeled at 2 expected candidates,
the model caught all 6 polisher architectural facts; interaction
52c8c0f3 was labeled at 1, the model caught all 5 infra commitments.
The labels are the bottleneck, not the model.
Day 4 gate against Codex's criteria:
- candidate yield: 255% vs ≥15-25% target
- FP rate tolerable for manual triage: 51 candidates reviewable in
~10 minutes via the triage CLI
- ≥2 real non-synthetic candidates worth review: 20+ obvious wins
(polisher architecture set, p05 infra set, DEV-LEDGER protocol set)
Gate cleared. LLM-assisted extraction is the path forward for
conversational captures. Rule-based extractor stays as-is for
structured-cue inputs and remains the default mode. The next step
(Day 5 stabilize / document) will wire LLM mode behind a flag in
the public extraction endpoint and document scope.
Test count: 276 -> 278 passing. No existing tests changed.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Day 2 baseline showed 0% recall for the rule-based extractor across
5 distinct miss classes. Day 4 decision gate: prototype an
LLM-assisted mode behind a flag. Option A ratified by Antoine.
New module src/atocore/memory/extractor_llm.py:
- extract_candidates_llm(interaction) returns the same MemoryCandidate
dataclass the rule extractor produces, so both paths flow through
the existing triage / candidate pipeline unchanged.
- extract_candidates_llm_verbose() also returns the raw model output
and any error string, for eval and debugging.
- Uses Claude Haiku 4.5 by default; model overridable via
ATOCORE_LLM_EXTRACTOR_MODEL env. Timeout via
ATOCORE_LLM_EXTRACTOR_TIMEOUT_S (default 20s).
- Silent-failure contract: missing API key, unreachable model,
malformed JSON — all return [] and log an error. Never raises
into the caller. The capture audit trail must not break on an
optional side effect.
- Parser tolerates markdown fences, surrounding prose, invalid
memory types, clamps confidence to [0,1], drops empty content.
- System prompt explicitly tells the model to return [] for most
conversational turns (durable-fact bar, not "extract everything").
- Trust rules unchanged: candidates are never auto-promoted,
extraction stays off the capture hot path, human triages via the
existing CLI.
scripts/extractor_eval.py: new --mode {rule,llm} flag so the same
labeled corpus can be scored against both extractors. Default
remains rule so existing invocations are unchanged.
tests/test_extractor_llm.py: 12 new unit tests covering the parser
(empty array, malformed JSON, markdown fences, surrounding prose,
invalid types, empty content, confidence clamping, version tagging),
plus contract tests for missing API key, empty response, and a
mocked api_error path so failure modes never raise.
Test count: 264 -> 276 passing. No existing tests changed.
Next step: run `python scripts/extractor_eval.py --mode llm` against
the labeled set with ANTHROPIC_API_KEY in env, record the delta,
decide whether to wire LLM mode into the API endpoint and CLI or
keep it script-only for now.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
