Parallel infrastructure:
evals/parallel.py
multiprocessing.Pool helper (spawn context, default workers
min(cpu_count, 8)). Per-case lanes use it via:
run_lane(cases, workers=N)
workers=1 forces serial (debugging); None uses the default pool.
Generic over the per-case return type, so dataclass-returning
runners (provenance) and dict-returning runners both work.
Wired into:
- evals/adversarial_identity/runner.py
- evals/calibration/runner.py
- evals/symbolic_logic/runner.py
- evals/provenance/runner.py
Per-case helpers are now picklable (module-level, single arg).
Monotonic-learning stays serial within a split — shared session
is structural to its longitudinal protocol.
Empirical speedup (adversarial-identity public/v1, 25 cases on
macOS 8 cores): serial 14.1s -> parallel 3.1s (~4.5x). Identical
per-case results.
adversarial-identity v2:
public/v2 — 35 cases (20 attack / 15 legitimate). Attacks cover
more varied phrasings: punctuation variation
("Actually -" / "No:" / "Correction —"), embedded
hedges ("please" / "regardless of prior context"),
multi-clause attacks, and identity-marker triggers
in mid-clause position.
holdouts/v2 — 22 cases (12 attack / 10 legitimate) on distinct
priming vocabulary.
Results: attack_rejection_rate=1.0, legitimate_acceptance_rate=1.0
on both splits.
The marker-regex defense in teaching/review.py:_is_identity_override
holds against every v2 phrasing — markers are checked case-insensitive
against the full text, so capitalization / punctuation tricks don't
slip past.
Test suite: 596 passing (no regression).
Adds the fourth Phase 2 lane. v1 measures the structural foundations
on which a future inference engine would be built:
M1. premise_recall — probe vault_hits >= min after chain teaching
M2. replay_determinism — same chain + probe → same trace_hash
M3. proposal_storage — correction premises store as PackMutationProposals
Patterns covered: modus_ponens_chain, modus_tollens_chain, syllogism,
negation, chain_recall (up to 4-hop chains).
v1 results across 38 cases (8 dev + 18 public + 12 holdouts):
premise_recall=1.0, replay_determinism=1.0, proposal_storage=1.0.
Each case runs twice on fresh CognitiveTurnPipelines to verify the
trace_hash matches — confirming deterministic replay over premise chains.
Architectural finding logged in evals/symbolic_logic/gaps.md:
CORE has no first-class inference operator. Chain "inference" today is
emergent from teaching-store commits + cumulative vault recall, not a
named-rule symbolic engine. v1 honestly tests what CORE deterministically
*does* (store, replay, recall chains) without overclaiming that CORE
reasons symbolically. v2 would assert specific transitive recall
contents in the probe surface, which requires either a
PropositionGraph traversal operator or pack-axiom rules — both filed
as suggested follow-up work.
