0c75be7c15
- 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.
191 lines
8.8 KiB
Python
191 lines
8.8 KiB
Python
"""
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CognitiveTurnResult — the complete record of one cognitive turn.
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This is the canonical output of CognitiveTurnPipeline.run(). It is
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frozen and slot-based so it can be passed safely across module boundaries
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without mutation risk.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from core.cognition.leeway import LeewayRecord
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from field.state import FieldState
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from generate.articulation import ArticulationPlan
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from generate.dialogue import DialogueRole
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from generate.graph_planner import ArticulationTarget, PropositionGraph
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from generate.intent import DialogueIntent
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from generate.proposition import Proposition
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from core.physics.identity import IdentityScore
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from recognition.carrier import EpistemicGraph
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from teaching.correction import CorrectionCandidate
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from teaching.review import ReviewedTeachingExample
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from teaching.store import PackMutationProposal
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from chat.dispatch_trace import DispatchTrace
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@dataclass(frozen=True, slots=True)
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class CognitiveTurnResult:
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"""Full observability record for a single pipeline turn.
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Includes the Shadow Coherence Gate evidence (authority_source +
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substrate_hazard) so that the migration from hybrid legacy spine to
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the unified PropositionGraph substrate is completely inspectable and
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replay-diagnosable without ever breaking determinism or the 74 invariants.
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"""
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# --- input layer ---
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input_text: str
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input_tokens: tuple[str, ...]
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filtered_tokens: tuple[str, ...]
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# --- field layer ---
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field_state_before: FieldState | None # None on the very first turn
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field_state_after: FieldState
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# --- understanding / recall layer ---
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proposition: Proposition
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articulation: ArticulationPlan
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# --- output surfaces ---
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surface: str # final voiced surface (what the user sees)
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walk_surface: str # sentence-assembled walk surface
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articulation_surface: str # bare articulation surface before assembly
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# --- dialogue ---
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dialogue_role: DialogueRole
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# --- identity telemetry ---
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identity_score: IdentityScore | None
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# --- vault / memory ---
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vault_hits: int
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recall_energy_class: str | None = None
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# --- intent / graph telemetry ---
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intent: DialogueIntent | None = None
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proposition_graph: PropositionGraph | None = None
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articulation_target: ArticulationTarget | None = None
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# --- teaching loop ---
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teaching_candidate: CorrectionCandidate | None = None
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reviewed_teaching_example: ReviewedTeachingExample | None = None
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pack_mutation_proposal: PackMutationProposal | None = None
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# --- inference operators (ADR-0018) ---
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# Deterministic serialisation of any typed operator invoked during the
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# turn (e.g. transitive_walk over the teaching-store typed-relation
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# graph). Empty string when no operator ran. Folded into trace_hash
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# so operator invocation is a load-bearing part of replay equality.
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operator_invocation: str = ""
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# --- forward semantic control evidence (ADR-0023) ---
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# ``admissibility_trace`` is the per-transition record produced by
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# ``generate()`` (empty tuple when no admissibility ran).
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# ``admissibility_trace_hash`` is its canonical SHA-256, folded
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# into ``trace_hash`` only when non-empty so pre-ADR-0023 turn
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# hashes are byte-preserved.
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# ``ratification_outcome`` is the enum value ("ratified" /
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# "demoted" / "passthrough") from the field ratifier; empty
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# string when no ratification ran.
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# ``region_was_unconstrained`` records whether forward semantic
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# control was active on this turn — observation only, no
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# production fail-closed yet (see ADR-0023 §Out of scope).
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admissibility_trace: tuple = ()
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admissibility_trace_hash: str = ""
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ratification_outcome: str = ""
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region_was_unconstrained: bool = True
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# --- inner-loop refusal evidence (ADR-0024 Phase 2) ---
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# ``refusal_reason`` is the stable string value of a
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# ``generate.exhaustion.RefusalReason`` when the walk refused this
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# turn, or the empty string otherwise. Empty-string default is
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# the contract for "no refusal materialised"; folding into
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# trace_hash is gated on non-emptiness so non-refused turns keep
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# byte-identical hashes relative to pre-Phase-2 (CLAUDE.md
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# determinism invariant). Phase 2 leaves the materialisation site
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# in chat/runtime.py untouched per the ADR-0024 Phase 2 scope
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# decision — this field exists so the trace contract is already
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# in place when a future ADR wires the materialisation path.
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refusal_reason: str = ""
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# --- recognition / epistemic carrier (ADR-0144) ---
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# None when no DerivedRecognizer is attached, when recognition refused,
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# or on the very first turn before any recognizer is configured.
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# Non-None only when recognition admitted (state == EVIDENCED).
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# NOT folded into trace_hash in Phase 1 (observability only).
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epistemic_graph: EpistemicGraph | None = None
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# --- grounding dispatcher trace (ADR-0142) ---
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# None when no dispatch trace is collected or on pre-dispatch-trace turns.
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# NOT folded into trace_hash (observability only).
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dispatch_trace: DispatchTrace | None = None
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# --- compound intent observability (ADR-0089 Phase C1) ---
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# Finding 4 (audit 2026-05-20). ``classify_compound_intent`` returns
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# multiple parts for inputs like "What is X and how does it relate
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# to Y?" but the pipeline still routes only the dominant clause
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# through the existing single-intent path. Pre-fix the secondary
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# clauses were silently dropped — no observability, no telemetry,
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# no trace evidence.
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#
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# Phase C1 surfaces the dropped clauses here so operators can see
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# the lost signal without changing any current behaviour. Phase
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# C2 (opt-in, flag-gated) will route the secondary clauses through
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# a multi-node graph; that wiring is deliberately scoped to a
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# separate PR per ADR-0089 because it widens
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# ``compute_trace_hash`` and the surface resolver contract.
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#
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# Empty tuple == this turn was single-clause OR the compound
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# classifier was not consulted; ``len > 0`` == this turn dropped
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# secondary clauses that were classified but not routed.
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dropped_compound_clauses: tuple[DialogueIntent, ...] = ()
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# --- invariant bookkeeping ---
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versor_condition: float = 0.0 # must be < 1e-6
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trace_hash: str = "" # SHA-256 over deterministic key fields
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# --- response-governance leeway evidence (B4; observational, not in trace_hash) ---
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leeway: LeewayRecord | None = None
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# --- Shadow Coherence Gate / substrate authority (Phase A) ---
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# ``authority_source`` is the value from SurfaceResolution.authority:
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# "runtime_canonical" | "runtime_pre_decoration" | "runtime" | "realizer" | "substrate_realizer".
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# It is the single source of truth for which spine actually spoke.
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#
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# ``substrate_hazard`` is the machine-readable list of reasons the
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# geometric substrate was *not* granted authority on this turn even
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# though a PropositionGraph was produced. Populated only on bypass
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# paths. Observational (not folded into trace_hash in Phase A) so that
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# every existing turn keeps byte-identical hashes while the hazard
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# ledger illuminates the exact work remaining for Layers 1-3.
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#
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# These two fields turn the "Authority Flip Cliff" into a controlled,
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# data-driven strangler migration.
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authority_source: str = ""
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substrate_hazard: tuple[str, ...] = ()
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# --- Phase C instrumentation: Geometric Anti-Unification hook for OOV (read-only telemetry) ---
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# When an OOV subject is encountered in the context of a PropositionGraph
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# (i.e. a "hole" in S-P-[OOV] or similar), this carries the discrete
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# structural context (unresolved topology + intent) plus a placeholder
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# for exact CGA neighbor probe results (via vault.recall + cga_inner on
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# surrounding realized facts).
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#
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# Today: purely structural (from effective_graph.get_unresolved_topology()
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# when grounding_source indicates oov or pending slots on OOV-shaped
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# intents). No vault call yet (keeps change atomic + zero side effects).
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#
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# Future: perform *exact* geometric anti-unification here (sub-graph
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# match on conformal space) to propose SPECULATIVE algebraic variable
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# or relation type for the hole, without ever affecting user surface,
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# trace_hash (observational), or durable state. Must emit SPECULATIVE,
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# respect teaching boundary for any promotion.
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#
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# Pillars: Mechanical Sympathy (cheap structural + optional exact recall),
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# Semantic Rigor (exact CGA only, no approx), Third Door (graph structure
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# as first-class for inference instead of lexical substring).
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#
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# Never folded into trace_hash in this phase. Never mutates field/vault.
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oov_geometric_context: dict | None = None
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