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.
152 lines
4.7 KiB
Python
152 lines
4.7 KiB
Python
"""Typed deterministic operators over CORE's typed state (ADR-0018).
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Two operators land here as the Phase 3 v2 inference-depth bundle. Both
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are pure functions; both are bounded by a ``max_hops`` cap so they
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cannot diverge; both produce outputs that round-trip through the
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existing pipeline (entities, vault entries).
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Operator-invocation records are folded into ``trace_hash`` (see
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``core/cognition/trace.py``) so any turn that calls an operator stays
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bit-for-bit replay-deterministic.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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_DEFAULT_MAX_HOPS = 5
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@dataclass(frozen=True, slots=True)
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class WalkResult:
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"""A typed relation-walk result.
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``path`` is the sequence of entities visited, starting from the head
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and ending at the deepest entity reachable under the requested
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relation. Length 1 means no edges were found. Length > 1 means a
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chain was traversed.
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``relation`` and ``head`` are echoed back so the result is self-
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describing for downstream wiring and trace_hash inclusion.
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``truncated`` is True when the walk hit the max_hops bound before
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exhausting the path; consumers should treat that as a soft signal
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that a longer chain may exist in the underlying store.
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"""
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head: str
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relation: str
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path: tuple[str, ...]
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truncated: bool
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def as_dict(self) -> dict[str, object]:
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return {
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"head": self.head,
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"relation": self.relation,
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"path": list(self.path),
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"truncated": self.truncated,
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}
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def _normalize(token: str) -> str:
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return token.strip().lower()
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def transitive_walk(
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triples: tuple[tuple[str, str, str], ...],
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head: str,
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relation: str,
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*,
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max_hops: int = _DEFAULT_MAX_HOPS,
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) -> WalkResult:
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"""Deterministic traversal of typed (head, relation, tail) triples.
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Starting from ``head``, follow only edges labelled ``relation`` for
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up to ``max_hops`` steps. Returns a ``WalkResult`` whose ``path``
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is the chain of visited entities.
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The triple substrate is supplied directly (no global state); callers
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pass ``teaching_store.triples()`` or any equivalent. Comparisons are
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case-insensitive and whitespace-trimmed.
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Cycle handling: if a node would be revisited, the walk stops at the
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previous node. This keeps the operator total over arbitrary
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teaching-store contents.
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Determinism: pure function over its arguments; no hidden state.
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"""
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if max_hops < 1:
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return WalkResult(head=head, relation=relation, path=(head,), truncated=False)
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head_lc = _normalize(head)
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relation_lc = _normalize(relation)
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edges: dict[str, str] = {}
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for h, r, t in triples:
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if _normalize(r) != relation_lc:
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continue
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h_lc = _normalize(h)
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t_lc = _normalize(t)
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# First-write-wins keeps the operator deterministic when the same
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# head appears more than once under the same relation.
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edges.setdefault(h_lc, t_lc)
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path: list[str] = [head_lc]
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visited = {head_lc}
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cursor = head_lc
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truncated = False
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for _ in range(max_hops):
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nxt = edges.get(cursor)
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if nxt is None:
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break
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if nxt in visited:
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break
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path.append(nxt)
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visited.add(nxt)
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cursor = nxt
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else:
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# Loop exhausted without break; a deeper hop may exist.
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truncated = edges.get(cursor) is not None
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return WalkResult(
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head=head_lc,
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relation=relation_lc,
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path=tuple(path),
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truncated=truncated,
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)
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def path_recall(
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triples: tuple[tuple[str, str, str], ...],
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entity: str,
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relation_chain: tuple[str, ...],
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*,
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max_hops: int = _DEFAULT_MAX_HOPS,
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) -> tuple[str, ...]:
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"""Recall the sequence of entities along a named relation chain.
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A single-element ``relation_chain`` (e.g. ``("is",)``) reduces to
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``transitive_walk``. A multi-element chain walks one hop per element
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so callers can pose questions like "X is Y; Y precedes Z" by passing
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``("is", "precedes")``.
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Returns the path of entities visited. Empty chain returns just the
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starting entity. Determinism and case-insensitivity inherit from
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``transitive_walk``.
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"""
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cursor = entity
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path: list[str] = [_normalize(cursor)]
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visited = {_normalize(cursor)}
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hops_left = max_hops
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for relation in relation_chain:
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if hops_left <= 0:
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break
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result = transitive_walk(triples, cursor, relation, max_hops=1)
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if len(result.path) < 2:
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break
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next_entity = result.path[1]
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if next_entity in visited:
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break
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path.append(next_entity)
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visited.add(next_entity)
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cursor = next_entity
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hops_left -= 1
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return tuple(path)
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