- root_normalize + depths in anti_unifier/derive/recognize for AC1
- default enrichment no flag for AC2
- depth to pass_manager + assess for AC3
- graph_anti_unify helper for AC4
- direct tests + verif per plan
Aligned with exact recall, immutability, cognitive spine path.
- Add LexicalResolution dataclass + resolve_entry() in chat/pack_resolver.py
that returns language, root, morphology_id, gloss, semantic_domains from
he/grc/en packs (lru-cached, first-match, full depth support).
- Extend GraphNode (generate/graph_planner.py) with optional language/root/
morphology_id fields (defaults preserve all call sites). Update as_dict()
to include them conditionally. ground_graph() now propagates depth.
- Generalize enrichment in core/cognition/pipeline.py:
- Per-subject resolution map using depth packs.
- Enrich all matching nodes before ground (subject→node map).
- Pass depth alongside recalled_words to ground_graph().
- Consume depth on articulation side:
- realize_semantic() and render_semantic() now accept/use language+root
for etymological/Logos framing on Hebrew/Greek nodes (e.g. "אמת (Hebrew
root: א-מ-ן) is defined as..."). English unchanged.
- Enrich oov_geometric_context with node_depths for future geometric
anti-unification using roots.
- Extend recognition/connector.py to forward depth from EpistemicNode
paths into GraphNode.
- Add full Hebrew turn test under realizer_grounded_authority flag.
- Update related tests (semantic realizer, OOV context, surface resolution).
- Cleaned legacy type() hack immediately on discovery (hard-stop rule).
All targeted tests green (52+ in slices), broad relevant suite 581 passed.
Invariants preserved: versor only at owned boundaries, exact recall,
immutable updates, no new legacy parsers. 3 pillars upheld.
Work continues tomorrow from this checkpoint.
Mirrors the chain-gap pipeline (Phase 1.1+1.2) for vocabulary gaps:
the OOV invitation surface (P2.1) now emits structured signals that
operators can aggregate, rank, and auto-promote into reviewed
PackMutationProposal candidates — closing the OOV loop the same way
Phase 1 closed the chain loop.
Three new modules + two new CLI surfaces:
teaching/oov_sink.py.
OOVCandidate dataclass mirroring teaching.discovery.DiscoveryCandidate.
OOVBufferSink (in-memory) + OOVMonthlyFileSink (append-only JSONL
under <root>/<YYYY>/<YYYY-MM>.jsonl — same layout as discovery sink
so the aggregator reuses the file-walk machinery).
hash_oov_candidate_id(token, intent, trace_hash) — deterministic
32-char hex id matching DiscoveryCandidate's replay invariant.
format_oov_candidate_jsonl — sorted-keys compact JSONL line.
teaching/oov_gaps.py.
aggregate_oov_gaps(root, since, sample_limit) groups emitted
candidates by token, tracks intent-shape union (a token asked under
multiple intents is a stronger curriculum signal), splits
boundary_clean from boundary_tainted counts, supports --since
YYYY-MM filtering via the sink's file naming convention.
Pure reader; never mutates the sink. Deterministic ordering:
(count desc, token asc).
teaching/oov_promotion.py.
promote_oov_gaps(gaps, threshold, include_tainted, suggested_packs)
lifts threshold-crossing tokens to OOVPromotion records.
- boundary_clean_count gates promotion by default (tainted-only
tokens may indicate the prompt hit a safety axis rather than a
vocab gap).
- --include-tainted flag for operator override.
- threshold < 1 raises.
- queue_id deterministic: ``oov:<token>@<threshold>`` — diffable
across runs.
- suggested_packs lists mounted packs but does NOT recommend one
— domain inference is out of scope (would require a stochastic
classifier). Operator picks the destination.
Runtime wiring:
ChatRuntime.attach_oov_sink(sink) mirrors attach_discovery_sink.
Runtime emits one OOVCandidate JSONL line per turn whose
grounding_source == "oov", no-op when no sink is attached.
Intent classifier is now invoked when EITHER sink is attached
(was: only discovery sink) — both downstream paths need it.
CLI:
core teaching oov-gaps [--top N] [--since YYYY-MM] [--root PATH]
[--sample-limit N] [--json]
core teaching oov-queue [--threshold N] [--include-tainted]
[--root PATH] [--since YYYY-MM] [--json]
ADR-0065 documents the full design (five-tier honesty gradient,
P2.1-P2.4 architecture). README.md updated with the ADR-0065
index entry.
Verification:
tests/test_oov_pipeline.py 24 passed
Operator workflow round-trip verified live:
> rt.attach_oov_sink(sink); rt.chat("What is photosynthesis?")
→ sink receives:
{"boundary_clean":true,"candidate_id":"f51bf8...",
"intent":"definition","token":"photosynthesis","trigger":"unresolved_subject",
"source_turn_trace":"","review_state":"unreviewed"}
> core teaching oov-gaps --root /tmp/oov_demo
→ ranked table by count, intent-set per token
> core teaching oov-queue --root /tmp/oov_demo --threshold 2
→ promoted tokens + suggested mounted packs
Full lane: 2005 passed, 2 skipped, 0 failed in 2:34 (xdist).
