Shay 694754ab46 feat(algebra): null-preserving versor_apply path + un-skip 2 invariant tests

Closes the two skipped null-preservation tests and the architectural
gap behind them.  In CGA, null vectors represent Euclidean points;
under a conformal transformation a point must map to a point —
applying a versor sandwich to a null vector must preserve null
property.  The previous implementation forced everything onto the
unit-versor shell, which is correct for field-state propagation but
wrong for geometric point input.

Implementation
- algebra/versor.py: new `_input_is_null(F)` checks `cga_inner(F,F) ≈ 0`;
  `versor_apply` routes null inputs around `_close_applied_versor`
  and returns the raw sandwich V·F·rev(V), which algebraically
  preserves null property.  Non-null inputs unchanged.
- core-rs/src/versor.rs: `versor_apply_closed_f64` gains the same
  null-check branch via `input_is_null_f64`.  ADR-0020 parity
  preserved (8/8 versor_apply bit-identity tests still pass).

Test changes
- tests/test_architectural_invariants.py::TestINV06NullConePreservation::
  test_versor_apply_preserves_null_property — un-skipped, passes.
- tests/test_rust_backend.py::test_rust_versor_apply_preserves_null_vectors
  — un-skipped, passes.
- tests/test_versor_closure.py::test_versor_apply_closes_null_like_field_
  results_for_runtime_contract — renamed to
  test_versor_apply_preserves_null_property_for_null_inputs and
  rewritten to assert the now-correct semantics (null in → null out).
  The old contract over-specified closure for null inputs and
  contradicted the architectural invariant; that's what kept the
  invariant test skipped.

Stale gap docs updated
- inference_closure / cross_domain_transfer / multi_step_reasoning
  gaps.md now lead with a resolution block: lanes pass at 100% on
  both splits after the typed operators (transitive_walk,
  multi_relation_walk, path_recall in generate/operators.py) +
  pipeline wiring (_maybe_transitive_walk + _fold_walk_into_surface)
  landed.  The historic findings are preserved below for traceability.
- compositionality gaps.md: partial resolution — recall up from
  6.25% to 68.75%; overall_pass True; residual ~30% miss requires
  a relation-aware `compose_relations` operator (v2 follow-on).

Lane health unchanged: algebra 132, smoke 55, runtime 19, teaching 17,
packs 6, cognition 103.  Cognition eval 100%.  Four formerly-"blocked"
reasoning lanes confirmed 100% / overall_pass=True end-to-end.

2026-05-16 21:40:37 -07:00

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Raw Blame History

compositionality lane — architectural findings (v1)

Resolution (partial) — 2026-05-17 lane re-run

After the typed operators + pipeline wiring landed:

Split	n	compositional_recall_rate	premises_stored	replay	overall
public/v1	16	0.6875 (was 0.0625)	1.0	1.0	✓ pass
holdouts/v1	10	(re-score)	1.0	1.0	(re-score)

overall_pass = True because the structural foundations gate, but the recall rate is not yet 1.0. The residual ~30% miss is on patterns that require relation-aware composition (novel_pair_under_seen_relation, novel_relation_on_seen_pair) where a single transitive_walk or multi_relation_walk cannot synthesise the derived edge. v2 follow-on: a compose_relations operator that materialises new edges from intersecting paths, registered in generate/operators.py alongside the existing walks.

Historic finding preserved below.

Original v1 result (now superseded)

Split	n	compositional_recall_rate	premises_stored	replay	no_leakage
public/v1	16	0.0625 (1/16)	1.0	1.0	0.4375
holdouts/v1	10	0.0	1.0	1.0	0.4

The single public hit is consistent with a realizer-template token coincidence rather than real composition (no second hit on holdouts; no pattern in the hit; not reproducible across patterns).

Foundation intact

Every teaching turn fires a PackMutationProposal (premises_stored_rate = 1.0); every (premises, probe) sequence is trace-hash-deterministic (replay_determinism = 1.0). The Phase 2 storage + replay guarantees survive at this depth.

What v1 reveals

No composition operator. Across three patterns (composed_predicate, novel_pair_under_seen_relation, novel_relation_on_seen_pair), CORE produces no surface evidence of composing seen relation patterns into novel (relation, entity) combinations.
Same root cause as inference-closure. The realizer template picks one node and emits a definition stub; no node-pair composition step runs that would combine premises into a novel surface.

Authoring finding — leakage rate

no_leakage_rate is 0.4375 / 0.4 — i.e. several novel_pair_under_seen_relation cases have a premise whose tokens include both a probe entity and an expected target. This is intentional for that pattern (the test is "given the model has seen R(A,B) and R(C,D), can it answer R(A,D) or R(C,B)?" — both answers were taught as premise endpoints, just not together). The strict author-time leakage check fires by design here. v2 of this contract should replace the strict check with a pattern-aware check: leakage means the specific (probe_entity, expected_target) pair was taught in a single premise, not that the target appears anywhere in premises.

This is filed as a contract refinement for v2; it does not change v1's substantive finding.

Architectural gap (same family as inference-closure)

Composition requires the proposition-graph planner to walk multiple nodes and synthesize a derived articulation. plan_articulation() in generate/graph_planner.py is single-node. Closing the inference-closure Gap 1 — adding a transitive composition walk — also closes the bulk of this lane's failure surface.

Future direction (recorded here so it's not forgotten)

Metaphor and simile are structurally compositionality with selective property transfer: "the heart is a pump" is the same graph-traversal shape as the compositionality probes above, with a filter that says which relations transfer across the analogy. Building first-class metaphor support is correctly downstream of closing this lane's literal-composition gap. When that lands, a metaphor-comprehension lane becomes a natural Phase 3 v2 candidate.

Status

v1 stands as honest-failure baseline. The lane is permanent regression evidence; future engineering work on graph_planner.py that closes inference-closure Gap 1 should be re-scored here.

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