Step 2 of the discourse-planner sequencing: add the presentation-depth
axis ResponseMode (brief / explain / walkthrough / paragraph / example)
as a sibling to IntentTag in generate/intent.py, with a deterministic
rule-based classify_response_mode classifier next to classify_intent.
ResponseMode previously lived in generate/discourse_planner.py; moved
to generate/intent.py so the dependency is one-way (planner imports
from intent, never reverse). discourse_planner.py now re-exports.
Additive-only invariant preserved:
* DialogueIntent fields unchanged (tag/subject/secondary_subject/
relation/frame). No equality breakage anywhere downstream.
* classify_intent branches untouched.
* Callers compose (classify_intent(t), classify_response_mode(t))
rather than threading mode through DialogueIntent.
41 new tests pin: placement (canonical home + re-export identity),
classifier behavior (parametrized over 25 prompts), priority ordering
(paragraph > explain, walkthrough > explain), purity (no clock/env/
filesystem), classify_intent invariance (definition / narrative /
example / cause / verification representative cases), and orthogonality
(intent and mode compose, neither shadows the other).
Verification:
* 96/96 existing intent tests pass.
* 69/69 new contract + characterization + classifier tests pass.
* smoke suite 67/67.
* cognition eval byte-identical: public 100/100/91.7/100,
holdout 100/100/83.3/100.
The 2026-05-19 cumulative live probe surfaced a stark gap: ~52% of
realistic conversational definition prompts ("Define X", "What does
X mean?", "What is to V?", "How does X work?", "What causes X?")
returned ``grounding_source="none"`` *even though every subject
lemma was pack-resident* across the 9 mounted English packs.
Root cause: the bottleneck was intent classification + subject
extraction, not lexicon coverage. Five patterns either had no rule
or routed to an intent the runtime dispatcher couldn't handle. The
fluency assessment at
``/Users/kaizenpro/.codex/worktrees/6533/core/notes/fluency_assessment_2026-05-19.md``
named these as Root Cause #1 ("public chat path does not use the
cognitive spine") and Root Cause #3 ("proposition graphs are too
thin"). This commit closes the surface-level half of that gap;
the deeper answer-plan layer (gloss propositions, P3 in the
assessment) is the next step.
Patterns fixed in ``generate/intent.py``:
1. ``Define X`` — added ``^define\s+`` rule mapping to
DEFINITION (placed after ``^what is/are``
so multi-word DEFINITION patterns still
prefer the question form).
2. ``What does X mean?`` — was matching TRANSITIVE_QUERY with
relation=``mean``. Now re-routes to
DEFINITION inside ``classify_intent`` so
``pack_grounded_surface`` fires on X.
Other transitive relations (precede,
ground, etc.) remain TRANSITIVE_QUERY.
3. ``What is to V?`` — added infinitive-marker strip to
``_normalize_subject`` for DEFINITION /
RECALL. ``to`` is gated on intent tag so
it never strips a transfer preposition
from CAUSE / VERIFICATION.
4. ``How does X work?`` — added ``_HOW_DOES_X_RE`` (third-person
mechanistic-cause). Distinct from the
first-person PROCEDURE rule ("How do I
X?"). Verbs: work / function / operate /
happen / exist / behave / act / emerge.
5. ``What causes X?`` — added causative-verb rule (causes /
triggers / enables / prevents / drives /
produces / induces / yields) routing to
CAUSE with X as subject.
Deliberate NON-fix: I considered adding a ``pack_grounded_surface``
fallback in the CAUSE / VERIFICATION dispatcher when no teaching
chain matches the subject. Reverted on review — that masks the
"would_have_grounded" discovery-candidate signal the teaching
pipeline uses to identify teaching-content gaps (see
``tests/test_discovery_candidates``). CAUSE on a pack-resident
lemma without a teaching chain stays ``grounding_source=='none'``
so the discovery layer can log the gap honestly.
``chat/pack_grounding.py``:
Extended ``_CORRECTION_TOPIC_STOPWORDS`` to include polarity
markers (no / yes / maybe / perhaps / hardly / indeed / surely /
definitely). Without this the CORRECTION composer would
short-circuit on ``no`` from "No, my parent disagrees" and miss
the topical lemma ``parent``.
Cumulative probe lift (44 realistic conversational prompts):
BEFORE: pack=16 none=23 oov=4 teaching=1 (52% NONE)
AFTER: pack=37 none=2 oov=4 teaching=1 ( 5% NONE)
The remaining 2 NONE responses are CAUSE-shaped prompts with no
teaching chain — deliberately preserved as the discovery-gap
signal described above.
Tests: tests/test_intent_classification_extensions.py — 23 new
tests covering each pattern + the lift invariant.
Verification:
Cognition eval byte-identical on both splits (100/100/91.7/100
public, 100/100/83.3/100 holdout).
All 111 intent-affected tests green:
test_intent_classification_extensions.py (23)
test_intent_proposition_graph.py / test_intent_ratifier.py /
test_intent_subject_extraction.py / test_narrative_example_intents.py
test_procedure_surface.py
test_correction_topic_lemma.py
test_cross_pack_grounding.py (including the polarity-stopword fix)
test_discovery_candidates.py
test_contemplation_wiring.py
test_en_core_polarity_v1_pack.py
Two new intent shapes + composers turn the runtime's corpus
density into operator-visible articulation. Both consult the
cross-corpus aggregator from ADR-0064; no new ratification needed.
P3.3 — chat/narrative_surface.py + IntentTag.NARRATIVE.
Classifier patterns (registered BEFORE generic DEFINITION):
^tell\s+me\s+about\s+
^describe\s+
^what\s+(?:can|do)\s+you\s+(?:say|know)\s+about\s+
narrative_grounded_surface(subject, max_clauses=4) walks every
reviewed chain rooted on subject across all registered teaching
corpora. Dedupes by (connective, object) — cause + verification
carrying the same predicate emit one clause, not two. Sorts by
(intent, connective, object) for replay stability.
Surface format:
"{X} — narrative-grounded ({corpus_ids}): {dX1}; {dX2}.
{X} {conn1} {O1} ({dO1}); {X} {conn2} {O2} ({dO2}).
No session evidence yet."
Cross-corpus subjects (e.g. mother in relations_v2) emit
narrative-grounded (relations_chains_v2) tag; cognition subjects
emit cognition_chains_v1 tag. Multi-corpus subjects (when
applicable) emit composite "corpus_a + corpus_b" tag.
P3.4 — chat/example_surface.py + IntentTag.EXAMPLE.
Classifier patterns:
^(?:give|show)\s+(?:me\s+)?an?\s+(?:example|instance)\s+of\s+
^example\s+of\s+
example_grounded_surface(object_lemma, max_examples=3) walks chains
where the lemma is the OBJECT — inverts the typical subject-keyed
access pattern. Dedupes by subject; sorts by (intent, subject,
connective).
Surface format:
"{X} — example-grounded ({corpus_ids}): {dX1}.
Example: {subj1} {conn1} {X}; {subj2} {conn2} {X}.
No session evidence yet."
Cross-cutting:
- Both intents added to _OOV_INTENT_TAGS — fall through to OOV
invitation when subject is unknown (Phase 2 gradient discipline).
- Both tagged grounding_source="teaching" (same provenance tier
as the existing teaching_grounded_surface).
- No prose generation, no new mutation surface.
Live verification:
> Tell me about truth.
[teaching] truth — narrative-grounded (cognition_chains_v1):
cognition.truth; logos.core. truth grounds knowledge
(cognition.knowledge); truth requires evidence (cognition.evidence).
> Give me an example of knowledge.
[teaching] knowledge — example-grounded (cognition_chains_v1):
cognition.knowledge. Example: truth grounds knowledge;
understanding requires knowledge; evidence grounds knowledge.
> Tell me about mother.
[teaching] mother — narrative-grounded (relations_chains_v2):
kinship.parent.female. mother precedes daughter (kinship.child.female).
> Describe photosynthesis.
[oov] I haven't learned 'photosynthesis' yet (intent: narrative). ...
ADR-0066 (this commit completes the ADR). 30 new tests passed.
Full lane: 2067 passed, 2 skipped, 0 failed in 2:32.
Add a deterministic, pack-agnostic post-processor in `generate/intent.py`
that runs after the `_RULES` table fires:
- DEFINITION / RECALL / PROCEDURE: strip trailing punctuation + leading
articles; preserve multi-word noun phrases
- CAUSE / VERIFICATION: additionally strip leading aux verbs; return
the head noun
Closed-set frozen sets (`_ARTICLES`, `_AUX_VERBS`) make the transform
inspectable. No pack load, no algebra change — touches only
`DialogueIntent.subject`.
Cognition eval (13-case public split):
surface_groundedness 46.2% → 61.5% (+15.3 pp)
term_capture_rate 33.3% → 50.0% (+16.7 pp)
intent_accuracy 100.0% (=)
versor_closure_rate 100.0% (=)
Two cases lift through the ADR-0048 pack path
(definition_procedure_023, definition_relation_026 — both
"What is a X?" → subject=X via article stripping). CAUSE / VERIFICATION
subjects are now clean head nouns, foundational for future COMPARISON
pack path / teaching-store inference.
Tests: tests/test_intent_subject_extraction.py (30 tests).
Lanes green: smoke (67), cognition (121), runtime (19), algebra (132),
teaching (17), packs (6).
Closes the mixed_relation_* (multi-step-reasoning) and composed_predicate
(compositionality) residuals with a single new operator plus a small
intent-classifier loosening. Both residuals shared an underlying shape:
walk any outgoing relation edge from the head, regardless of which
relation predicate appears at each step.
generate/operators.py:
multi_relation_walk(triples, head, *, max_hops=5) -> WalkResult
Walks any outgoing edge from head, accumulating a path across
mixed relation types. Returns WalkResult with relation="<mixed>"
so trace_hash records the cross-relation provenance explicitly.
Deterministic, cycle-safe, first-write-wins on duplicate heads
(across any relation).
generate/intent.py:
_TRANSITIVE_QUERY_RE relaxed from a closed verb enumeration to any
single verb-like word. "What does X (any verb)?" now routes to
TRANSITIVE_QUERY consistently; unrecognised relations are handled
by the pipeline's multi_relation_walk fallback rather than falling
through to UNKNOWN. Verified no regression on 30 intent / realizer
tests.
core/cognition/pipeline.py:
_maybe_transitive_walk now does precision-first dispatch on
TRANSITIVE_QUERY: try transitive_walk(relation) literal-match
first, fall back to multi_relation_walk only when the literal
walk returns a singleton. DEFINITION intents do not fall back
(would be too permissive for "What is X?").
tests/test_inference_operators.py: 6 new TestMultiRelationWalk
tests covering single-relation pass-through, cross-relation walks,
cycle termination, max_hops truncation, and determinism.
Phase 3 v1 re-score:
lane split v1 v2 v3 (now)
inference-closure public 0.0 1.0 1.0 pass
inference-closure holdouts 0.0 1.0 1.0 pass
multi-step-reasoning public 0.0 0.73 1.0 pass
multi-step-reasoning holdouts 0.0 0.80 1.0 pass
compositionality public 0.06 0.31 0.69 pass
compositionality holdouts 0.0 0.30 0.80 pass
cross-domain-transfer public 0.0 1.0 1.0 pass
cross-domain-transfer holdouts 0.0 1.0 1.0 pass
introspection public 0.0 1.0 1.0 pass
introspection holdouts 0.0 1.0 1.0 pass
PHASE 3 v1 IS COMPLETE: 10 of 10 splits passing. Phase 3 exit gate
(>= 2 lanes passing v1 by phase exit) is satisfied five times over.
Foundation guarantees (premises_stored_rate, replay_determinism)
remain 1.0 across all lanes. Trace_hash bit-stability preserved
with operator invocation records folded in per ADR-0018.
Compositionality public at 0.69 / holdouts at 0.80 - the residual
failures are the novel_pair_under_seen_relation / novel_relation_on_seen_pair
cases whose contract authoring is itself ambiguous (the leakage
check in the v1 contract fires by design on those patterns). Those
are contract-refinement candidates for v2 of that lane, not
engineering work. Overall_pass threshold (>= 0.50) is comfortably
met on both splits.
CLI suites smoke / cognition / teaching / packs all pass; 53
operator+teaching+pipeline tests green; no regression.
Implements the Phase 3 v2 inference-depth bundle per ADR-0018:
typed deterministic operators over CORE's typed state. Closes the
inference-closure / multi-step-reasoning / cross-domain-transfer
v1 gaps; partial close on compositionality.
New modules:
teaching/relation_parse.py - parse_triple(correction_text) lifts
a correction utterance into a typed (head, relation, tail) over
the en_core_cognition_v1 relation vocabulary. Pure regex,
deterministic, no learned classifier.
generate/operators.py - transitive_walk(triples, head, relation,
*, max_hops=5) walks single-relation chains. path_recall walks
a relation-chain tuple (e.g. ("is", "precedes")). Both bounded,
cycle-safe, case-insensitive, first-write-wins on duplicates.
Schema extensions:
teaching.store.PackMutationProposal gains optional triple field,
populated by TeachingStore.add via parse_triple. Plus new
TeachingStore.triples() helper returning all parsed triples.
generate.intent.IntentTag gains TRANSITIVE_QUERY plus a relation
field on DialogueIntent. New regex rules for "What does X R?"
and "Where does X belong?" forms with relation normalisation.
core.cognition.result.CognitiveTurnResult gains operator_invocation
field (deterministic serialisation of any operator that ran).
core.cognition.trace.compute_trace_hash gains operator_invocation
kwarg; trace_hash_from_result threads it through. Operator
invocation is now load-bearing for replay equality.
Pipeline wiring:
CognitiveTurnPipeline.run dispatches transitive_walk after
runtime.chat() when the intent is TRANSITIVE_QUERY (with the
parsed relation) or DEFINITION (implicit "is"). Non-trivial walks
fold the chain endpoint into surface and articulation_surface.
Verification:
tests/test_inference_operators.py - 27 unit tests covering
parser, transitive_walk (cycles, max_hops, case-insensitivity,
determinism, first-write-wins), path_recall, and WalkResult shape.
Re-score on Phase 3 v1 case sets:
lane split v1 after bundle
inference-closure public/v1 0.0 1.0 pass
inference-closure holdouts/v1 0.0 1.0 pass
multi-step-reasoning public/v1 0.0 0.7333 pass
multi-step-reasoning holdouts/v1 0.0 0.8 pass
cross-domain-transfer public/v1 0.0 1.0 pass
cross-domain-transfer holdouts/v1 0.0 1.0 pass
compositionality public/v1 0.0625 0.3125 partial
compositionality holdouts/v1 0.0 0.3 partial
Six of eight splits now pass v1. Foundation guarantees
(premises_stored, replay_determinism) remain 1.0 across all lanes.
Trace_hash determinism preserved (operator records fold in
deterministically).
Residuals (filed as Phase 3 v2 follow-up):
- multi-step-reasoning mixed_relation_3/4 patterns need path_recall
wired into the pipeline for multi-relation probes; the operator
exists but the pipeline only invokes transitive_walk today.
- compositionality novel-combination patterns need a genuinely
new operator shape (composed_relation_walk) - the literal
transitive walk does not synthesise novel pairs by construction.
CLI suites smoke / cognition / teaching pass; no regression. 47
pipeline + teaching + operator tests all green.
Shay