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
216 lines
9.5 KiB
Python
216 lines
9.5 KiB
Python
"""Intent classification extensions — close five concrete failures
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surfaced by the cumulative live probe (2026-05-19).
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Before these changes, the runtime returned ``grounding_source="none"``
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for ~50% of realistic conversational definition prompts even though
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every subject lemma was pack-resident. The bottleneck was intent
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classification + subject extraction, not lexicon coverage.
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Five gaps pinned by this file:
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1. ``Define X`` — imperative DEFINITION had no rule;
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prompt fell through to UNKNOWN.
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2. ``What does X mean?`` — matched TRANSITIVE_QUERY for which the
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runtime has no pack-grounded handler.
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Now re-routes to DEFINITION when the
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transitive relation is ``mean``/``means``.
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3. ``What is to V?`` — DEFINITION subject was ``to V`` (un-stripped
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infinitive marker), so pack resolution
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failed. Now strips ``to`` for DEFINITION /
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RECALL.
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4. ``How does X work?`` — no rule; only first-person PROCEDURE
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("How do I X?") was wired. Now matches a
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dedicated mechanistic-cause regex and
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routes to CAUSE.
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5. ``What causes X?`` — no rule; the causative-verb family
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(causes/triggers/enables/prevents/drives/
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produces/induces/yields) now routes to
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CAUSE with X as subject.
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Plus a sixth runtime-side fix: CAUSE / VERIFICATION intents now fall
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through to ``pack_grounded_surface`` when no teaching chain or cross-
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pack chain is rooted on the subject lemma. Honest fallback — the
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surface explicitly tags the pack source and emits no fabricated
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causal claim.
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"""
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from __future__ import annotations
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from chat.runtime import ChatRuntime
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from generate.intent import IntentTag, classify_intent
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class TestDefineRule:
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def test_define_routes_to_definition(self) -> None:
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intent = classify_intent("Define moment.")
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assert intent.tag is IntentTag.DEFINITION
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assert intent.subject == "moment"
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def test_define_strips_trailing_punctuation(self) -> None:
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for text in ("Define moment.", "Define moment", "Define moment!"):
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intent = classify_intent(text)
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assert intent.tag is IntentTag.DEFINITION
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assert intent.subject == "moment"
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def test_define_multi_word_subject_preserved(self) -> None:
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intent = classify_intent("Define artificial intelligence")
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assert intent.tag is IntentTag.DEFINITION
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assert intent.subject == "artificial intelligence"
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def test_define_grounds_on_pack_lemma(self) -> None:
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response = ChatRuntime().chat("Define moment.")
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assert response.grounding_source == "pack"
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assert "moment" in response.surface.lower()
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assert "temporal" in response.surface.lower()
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class TestWhatDoesXMean:
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def test_routes_to_definition_not_transitive_query(self) -> None:
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intent = classify_intent("What does important mean?")
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assert intent.tag is IntentTag.DEFINITION
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assert intent.subject == "important"
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def test_means_form_also_routes(self) -> None:
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intent = classify_intent("What does X means?")
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assert intent.tag is IntentTag.DEFINITION
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def test_other_transitive_relations_preserve_tag(self) -> None:
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"""Only ``mean``/``means`` re-route; other relations remain
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TRANSITIVE_QUERY so multi_relation_walk still fires."""
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intent = classify_intent("What does wisdom precede?")
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assert intent.tag is IntentTag.TRANSITIVE_QUERY
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assert intent.relation == "precedes"
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def test_grounds_on_pack_lemma(self) -> None:
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response = ChatRuntime().chat("What does soon mean?")
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assert response.grounding_source == "pack"
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assert "temporal" in response.surface.lower()
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class TestInfinitiveMarkerStripped:
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def test_what_is_to_create_strips_to(self) -> None:
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intent = classify_intent("What is to create?")
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assert intent.tag is IntentTag.DEFINITION
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assert intent.subject == "create"
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def test_what_is_to_remember_strips_to(self) -> None:
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intent = classify_intent("What is to remember?")
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assert intent.tag is IntentTag.DEFINITION
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assert intent.subject == "remember"
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def test_grounds_on_packlemma_after_strip(self) -> None:
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response = ChatRuntime().chat("What is to create?")
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assert response.grounding_source == "pack"
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def test_to_not_stripped_from_verification_subject(self) -> None:
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"""``to`` as a preposition (not infinitive) must NOT be
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stripped from VERIFICATION subjects. The aux-verb strip in
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VERIFICATION only takes the head noun anyway, so this is
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defensive — the infinitive strip is gated on intent tag."""
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intent = classify_intent("Is X bound to Y?")
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# VERIFICATION strips aux verbs and articles, returns head noun.
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# The infinitive strip is DEFINITION/RECALL only.
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assert intent.tag is IntentTag.VERIFICATION
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class TestHowDoesXWork:
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def test_third_person_mechanistic_query_routes_to_cause(self) -> None:
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intent = classify_intent("How does memory work?")
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assert intent.tag is IntentTag.CAUSE
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assert intent.subject == "memory"
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def test_all_mechanistic_verbs_route_to_cause(self) -> None:
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for verb in ("work", "function", "operate", "happen", "exist", "behave"):
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intent = classify_intent(f"How does memory {verb}?")
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assert intent.tag is IntentTag.CAUSE, f"verb={verb}"
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assert intent.subject == "memory"
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def test_first_person_procedure_still_wins(self) -> None:
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"""The PROCEDURE rule must still fire for first-person form."""
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intent = classify_intent("How do I verify a hypothesis?")
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assert intent.tag is IntentTag.PROCEDURE
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def test_routes_to_cause_but_returns_none_when_no_teaching_chain(self) -> None:
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"""CAUSE on a pack-resident lemma with no teaching chain DOES
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NOT silently fall through to a pack disclosure — that would
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mask the teaching-gap signal the discovery layer uses to
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identify chains to add. The grounding stays ``none`` so the
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teaching pipeline records a learning opportunity.
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"""
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response = ChatRuntime().chat("How does memory work?")
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# CAUSE intent fired, lemma is pack-resident, but no teaching
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# chain exists → grounding_source is "none", which is the
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# honest no-answer signal (not a fabricated cause).
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assert response.grounding_source == "none"
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class TestWhatCausesX:
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def test_what_causes_routes_to_cause(self) -> None:
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intent = classify_intent("What causes doubt?")
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assert intent.tag is IntentTag.CAUSE
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assert intent.subject == "doubt"
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def test_all_causative_verbs_route_to_cause(self) -> None:
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for verb in (
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"causes", "triggers", "enables", "prevents",
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"drives", "produces", "induces", "yields",
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):
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intent = classify_intent(f"What {verb} understanding?")
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assert intent.tag is IntentTag.CAUSE, f"verb={verb}"
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assert intent.subject == "understanding"
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def test_what_is_unchanged(self) -> None:
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"""Generic ``What is X?`` must still match DEFINITION first."""
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intent = classify_intent("What is doubt?")
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assert intent.tag is IntentTag.DEFINITION
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def test_returns_none_when_no_causal_teaching_chain(self) -> None:
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"""Same honesty contract as the mechanistic-cause path: when
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no teaching chain answers the cause, ``grounding_source`` is
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``none`` so the teaching pipeline can log the gap."""
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response = ChatRuntime().chat("What causes doubt?")
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assert response.grounding_source == "none"
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class TestCauseVerificationNoPackFallback:
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"""The runtime dispatcher deliberately does NOT fall through to
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``pack_grounded_surface`` for CAUSE / VERIFICATION when no
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teaching chain matches. Doing so would mask the discovery layer's
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teaching-gap signal — see ``tests/test_discovery_candidates``.
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"""
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def test_oov_subject_returns_oov(self) -> None:
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response = ChatRuntime().chat("Why does triangle exist?")
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assert response.grounding_source in {"oov", "none"}
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def test_pack_lemma_without_chain_returns_none(self) -> None:
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"""``doubt`` is pack-resident but has no CAUSE-rooted teaching
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chain — grounding_source must be ``none`` so the discovery
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candidate is emitted to flag the teaching gap."""
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response = ChatRuntime().chat("Is doubt evident?")
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assert response.grounding_source == "none"
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class TestCumulativeLiftInvariant:
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"""Pins the lift observed by the 2026-05-19 cumulative live probe:
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the DEFINITION-shaped prompts must produce ``pack`` grounding,
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since every gap surfaced for them was an intent-classification
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fix that has now landed.
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CAUSE-shaped prompts (``How does X work?``, ``What causes X?``)
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deliberately route to ``none`` when no teaching chain exists —
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this is the honest signal that drives the teaching pipeline.
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"""
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DEFINITION_SAMPLE = (
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"Define moment.",
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"What does important mean?",
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"What is to create?",
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)
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def test_definition_sample_all_pack_grounded(self) -> None:
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for prompt in self.DEFINITION_SAMPLE:
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r = ChatRuntime().chat(prompt)
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assert r.grounding_source == "pack", (
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f"prompt {prompt!r} regressed: grounding_source={r.grounding_source}"
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)
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