CORE served the affirmative of propositions the user denied. Measured live on
main @ 536d6e55 with realizer_grounded_authority=True:
"evidence does not support truth" -> 'Evidence is verified: what supports truth.'
"evidence supports truth" -> 'Evidence is verified: what supports truth.'
Byte-identical.
Phase 4 (#136) pinned "render_semantic has no negated parameter" as a defect.
Sizing the exposure before acting on it -- the step §7 requires -- showed the
pin was correct and INCOMPLETE. The defect is two drops in series, and the
first is in the graph:
intent.negated parsed from the user's words, always has been
GraphNode NO FIELD FOR IT <-- first drop
ground_graph rebuilds nodes field-by-field; nothing to carry
depth enrichment rebuilds nodes field-by-field; nothing to carry
plan_articulation cannot carry what the node does not hold
realize_semantic never read step.negated
render_semantic no parameter for it <-- second drop
Five separate constructors, each silently defaulting to the affirmative.
Why every gate was green
------------------------
On the default config the ungrounded realizer emits "...", _is_useful_surface
rejects it, and the runtime echo wins the resolver -- and the echo contains the
user's own "does not". The truth path was correct BY ACCIDENT. ADR-0088 Phase B
grounds the graph first, which is exactly when the realizer's surface becomes
useful enough to win. So the defect sat behind a shipped flag, invisible to
every property-of-one-surface test, because nothing compared a denial to its
assertion.
Delivered
---------
- GraphNode.negated, threaded intent -> graph -> ground -> enrich -> step ->
surface. Serialized ONLY when True, so every pre-existing as_dict and every
trace_hash folded from one stays byte-identical -- which is why no lane pin
moves.
- Clause grammar delegated to its one owner. Four of the eight intent
"templates" were never frames; they were plain clauses. They now call
render_step. Writing a second negation implementation in semantic_templates
was the tempting fix and is rejected: Phase 2A spent a unit giving every
linguistic fact one owner, and that design rebuilds the disease one level up.
- Frames that keep a finite verb (VERIFICATION, PROCEDURE, COMPARISON) get an
explicit negated form. RECALL is a speech act with no proposition to deny, so
it falls back to the clause path rather than drop the denial (ADR-0261 §5.1).
Measured
--------
delegation on affirmatives 192/192 byte-identical
serving realizer, all corpora 85/347 -> 109/347 (+24 = the denials)
feature-bearing bucket 49/214 -> 73/214
CONTROL (nothing droppable) 33/33 -> 33/33 unchanged
multi-node (clause joining) 3/100 -> 3/100 unchanged, out of scope
lane pins 11/11 byte-identical, none edited
The control staying 33/33 and multi-node staying 3/100 is the evidence that
this moved the denials and nothing else.
The exhaustive control earned its keep immediately
--------------------------------------------------
It found FIVE more intents serving a denial as its own assertion --
TRANSITIVE_QUERY, FRAME_TRANSFER, NARRATIVE, EXAMPLE, DEDUCTION -- because an
intent with no frame fell back to the UNKNOWN *template* (which cannot say
"not") rather than the UNKNOWN *clause* (which can). The default is now the
capable path, so the next intent added inherits correctness. Found by the
control, not by inspection.
Mutation -- every link reverted individually
--------------------------------------------
baseline 19 pass
graph_from_intent drops intent.negated 8 FAIL
plan_articulation drops node.negated 6 FAIL
ground_graph drops it on rebuild 2 FAIL
realize_semantic stops passing step.negated 6 FAIL
unframed intents fall back to the template 1 FAIL
pipeline depth-enrichment drops it 1 FAIL
The last row was GREEN on the first run -- my fix there was unguarded. That is
what added the structural invariant: every GraphNode(...) built on the serving
path must NAME `negated` or be recorded in an allowlist with a reason. The
defect was five constructors; a per-site test must be written per site, and a
site added without one is invisible. Only recognition/connector.py is exempt
(an EpistemicNode has no polarity to carry).
Four Phase 4 pins are revised, not relaxed -- #136's M2 mutation ("render_semantic
GAINS a negated parameter -> FAIL") has now happened for real, and forced the
deliberate revision it was built to force.
Registered in `smoke` in the same PR that creates it, per the #136 finding that
an unregistered pin runs nowhere.
Still unexpressed, deliberately: quantifier, tense, aspect. NO PRODUCER sets
them anywhere on the serving path, so threading them would be machinery with no
caller. render_step already handles all three the moment a producer exists.
[Verification]: in-worktree on CPython 3.12.13 with `uv sync --locked` --
smoke 641 (was 621), deductive 503, lane pins 11/11 with no pin edited.
- 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.
Bundle of 5 hot-path optimizations + 1 dead-code removal + 1 import
sweep + 1 helper fold, surfaced by a comb pass through the cognitive
spine starting from ``CognitiveTurnPipeline.run()`` and walking
outward through ChatRuntime, intent classification, the graph
planner, the realizer, and the vault. All eval lanes byte-identical
to MEMORY baseline; null-lift confirmed by ``core eval cognition``
across public / dev / holdout splits.
Hot-path fixes:
1. ``ChatRuntime._apply_oov_policy`` no longer rescans every
manifest per OOV token. Two precomputed booleans on
``self`` capture the FAIL_CLOSED-all and PROPOSE_VOCAB-any
aggregates at construction time. Manifests are immutable
post-construction so the cache is safe. Turns the path from
O(packs × OOV) to O(OOV).
2. ``CognitiveTurnPipeline.run`` calls ``classify_compound_intent``
once and takes its dominant ``compound.primary`` as the seeded
intent. Pre-fix the pipeline called both ``classify_intent``
and ``classify_compound_intent`` on every turn — and
``classify_compound_intent`` internally invokes
``classify_intent`` on the dominant fragment, so every non-
compound prompt walked the 15-regex cascade twice.
3. ``TeachingStore.triples()`` materializes once per turn.
Pre-fix ``_maybe_transitive_walk`` and ``_maybe_compose_relations``
each called ``self.teaching_store.triples()`` independently,
doubling the per-turn O(N) filter+tuple-build cost. Both
helpers now accept an optional ``triples`` arg; the pipeline
computes once and passes through.
5. ``realize_semantic`` and ``realize_target`` build a
``node_id → obj`` map once and look up each step in O(1)
instead of an O(N) linear scan of ``graph.nodes`` per step.
The cost was invisible on today's 1-2 node graphs but would
have become an O(N²) regression on the multi-node graphs
ADR-0089 Phase C2 plans to introduce.
Dead-code / cleanup:
- Removed dead ``CognitiveTurnPipeline._fold_compose_into_surface``
(no callers since PR #76 routed all surface composition
through ``resolve_surface``).
- Folded ``_serialize_walk`` + ``_serialize_compose`` (identical
bodies) into one ``_serialize_operator`` helper.
- Hoisted ``import json`` and ``RatifiedIntent`` from inside hot
method bodies to module top (same pattern PR #76 applied to
``_is_useful_surface``).
- Dead-defensiveness sweep on ``ChatResponse`` field reads in
``pipeline.run()``: ``getattr(response, "<field>", default)``
where the field always exists on the dataclass with a default
is replaced by direct attribute access (6 sites:
``realizer_grounded_authority``, ``recalled_words``,
``grounding_source``, ``register_canonical_surface``,
``pre_decoration_surface``, ``admissibility_trace``,
``region_was_unconstrained``). ``refusal_reason`` retains the
guarded read because ADR-0024 Phase 2 leaves its
materialisation site dormant.
Benchmark profiler:
- ``benchmarks/pipeline_profiler.py`` rebound from
``classify_intent`` to ``classify_compound_intent`` (the new
single-classification site). All other timing hooks unchanged.
Tests:
- 4 new tests in ``tests/test_comb_pass_hot_path.py`` pin: OOV
aggregates exist as bools; compound classifier runs exactly
once per turn; ``triples()`` materializes exactly once per
turn; realizer correctly resolves obj slots across an 8-node
graph.
- All existing tests pass. ``core eval cognition`` byte-identical:
public 100/100/91.7/100, dev 100/100/78.6/100, holdout
100/100/83.3/100.
- ``core test --suite cognition`` 120/0/1, ``smoke`` 67/0,
``runtime`` 19/0.
Closes the user-flagged scope gap: every previous fluency lane (Phase
5.1 + 5.4-5.7 + grammatical_coverage) operates on 3-word SVO probes.
These three pieces stress paragraph-scale generation, give per-stage
latency visibility, and expose the realizer's word-choice geometry —
all on top of the existing deterministic infrastructure.
# discourse_paragraph lane (paragraph-scale fluency)
Forces the realizer to emit multi-sentence paragraphs from a
multi-step ArticulationTarget with rhetorical moves (ASSERT, SEQUENCE,
ELABORATE, CONTRAST). Same realizer, much richer input — every case
is 3-5 sentences with deterministic discourse markers.
Public 12 cases / holdouts 5 / dev 1 across 12 + 5 topic chains
(epistemic, scientific method, creation arc, logical dependency,
ethical grounding, linguistic layers, mathematical chain, narrative,
biology, physics, two contrast-shaped, musical, social, computational,
psychological, economic).
Sub-metrics per case:
- sentence count (within min..max window)
- subject coverage rate
- discourse marker presence (next / furthermore / in contrast)
- sentence-initial capitalization
- replay determinism (run twice, surfaces match)
Result: 12/12 public + 5/5 holdouts at 100%, replay rate 100%, mean
sentence count 4.
# Realizer capitalization (G4, addresses user-flagged concern)
generate/realizer.py gains `_capitalize_sentence` + `_join_as_paragraph`
helpers. Sentence-initial alphabetic characters are now uppercased
(skipping leading whitespace/punctuation). Surfaces went from
"wisdom grounds knowledge. next, knowledge requires evidence."
to
"Wisdom grounds knowledge. Next, knowledge requires evidence."
The discourse_paragraph runner ships a strict per-sentence
capitalization check so future regressions get caught.
# Pipeline-stage profiler (benchmarks/pipeline_profiler.py)
External monkey-patch wrapper around CognitiveTurnPipeline.run() that
records per-stage ns budgets without editing any pipeline source.
Stages: intent, graph_planner, realize_semantic, runtime_chat,
maybe_transitive_walk, fold_walk_into_surface, run_teaching,
trace_hash.
API: `profile_turn(pipeline, text) -> ProfileReport` with
`.stages: dict`, `.total_ns: int`, `.as_dict()`.
Empirical: runtime_chat dominates >99% on the runtime hot path (which
is correct — that's where ingest + propagate + recall + articulate
all happen). Future optimisation work has a clear per-stage signal.
# Word-selection tracer (benchmarks/word_selection_tracer.py)
External wrapper around generate.articulation._resolve_slot that
records every nearest-neighbor lookup as a WordSelectionStep:
- slot (subject/predicate/object)
- input versor (32-d copy)
- top-K candidate words by CGA inner product
- chosen word + morphology
- output language
Top-K scoring uses the diagonal Cl(4,1) metric kernel from
algebra.backend (same vectorised path vault_recall uses), not a
per-word Python loop over cga_inner. No approximation, exact
deterministic ranking, bit-identical to a scalar scan.
API: `trace_realization(pipeline, text) -> RealizationTrace` with
`.steps`, `.realization_steps`, `.surface`, `.as_dict()`.
# CLI lane registration
Cognition suite now sweeps the benchmark profiler/tracer tests
(test_benchmarks_profiler.py) so any future regression in the
instrumentation surfaces immediately.
# Constraints honoured
- Zero edits to core/, chat/, vault/, teaching/, language_packs/, or
the algebra hot path. All instrumentation is external monkey-patch
with originals restored in finally.
- discourse_paragraph runner bypasses ChatRuntime grounding (named v2
gap) so paragraph capability is isolated to the realizer.
- No semantic changes; no hidden normalisation; no approximate
recall.
# Lane health
smoke 55, runtime 19, teaching 17, packs 6, cognition 105 (was 103),
algebra 132. All Phase 5 fluency lanes still 100% with the
capitalised surfaces (rubric is case-insensitive). discourse_paragraph
100%.
# What ships next (named v2)
- Round-trip: discourse_paragraph through ChatRuntime end-to-end,
not just realize_target.
- Per-sentence grammatical_coverage rubric on each emitted sentence.
- Longer chains (10/20/50 sentences) with per-sentence determinism
scaling curves.
- compose_relations operator to lift compositionality recall from
68.8% toward 100%.
Shay