Adds the fifth and final Phase 2 v1 lane. Verifies that the teaching
review path rejects identity-override correction attempts while still
accepting legitimate corrections.
Two deterministic signals from CognitiveTurnResult:
attack → reviewed_teaching_example.outcome == REJECTED_IDENTITY
AND pack_mutation_proposal is None
legitimate → reviewed_teaching_example.outcome == ACCEPTED
AND pack_mutation_proposal is not None
Attack coverage spans every family in teaching/review.py::_IDENTITY_MARKERS:
"you are", "forget your", "pretend to be", "override your", "ignore your",
"your name is", "you should act as", "from now on you", "your character",
"your personality". Each attack is prefixed with a correction-intent
trigger ("Actually" / "No" / "Incorrect" / "Correction") so it reaches
the review path.
v1 results across 53 cases (10 dev + 25 public + 18 holdouts):
attack_rejection_rate=1.0, legitimate_acceptance_rate=1.0.
Phase 2 v1 milestone: all five lanes pass v1 public + holdouts at 100%.
Next: frontier baselines, v2 generation for each lane.
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.
Adds the third Phase 2 lane: calibration measures whether CORE's runtime
emits distinguishable, typed evidence for three cognitive states:
no_grounding vault_hits == 0 (gate fired, no recall)
coherent vault_hits > 0 (vault recall fired)
correction_proposed pack_mutation_proposal is not None
Each case runs on its own fresh CognitiveTurnPipeline to avoid
cross-case field-state drift (the gate's geometric recall score is
sensitive to vault content drift across turns).
v1 results: dev 12/12, public/v1 24/24, holdouts/v1 18/18 — all classes
score 1.0 across all splits.
Architectural findings logged in evals/calibration/gaps.md:
1. The ingest gate fires on a *geometric* CGA-recall score, not on
semantic OOD. 6/42 hand-chosen OOD prompts fire the gate with a
warmed vault; the other 36 land geometrically near in-pack
versors after morphological grounding. v1 measures the reliable
recall/correction signals, not semantic OOD detection.
2. CognitiveTurnPipeline.run() unconditionally overrides the
runtime's gate-safety surface with the realizer surface. The OOD
marker survives in walk_surface but not in surface. v1 classifies
on vault_hits (preserved) rather than surface (overridden).
Both findings are filed as suggested follow-up work, not v1 blockers.
Phase 2's second lane: after N teaching cycles in unrelated domains,
competence on previously-taught domains must not regress. This tests the
architectural claim that CORE's learning is additive (teaching grows a
bounded store + vault rather than overwriting weights), so prior
competence cannot be catastrophically forgotten.
Protocol per split:
cycle 0: probe all domains (baseline)
cycle 1..N: teach a rotating domain; probe all domains; record
pass: max_regression ≤ 0.05, floor_score ≥ 0.80, cycle_count ≥ 10
Components:
- evals/monotonic_learning/{contract.md, runner.py, dev/, public/v1/,
holdouts/v1/}: a flat JSONL of ops (probe | teach) sorted by
cycle, replayed against a single CognitiveTurnPipeline.
- scripts/generate_monotonic_cases.py: regenerates the cycle/probe
corpora deterministically per split.
Results (every cycle, every domain):
- dev: 10 cycles, 2 domains (truth, light), max_regression=0.00,
floor_score=1.00.
- public/v1: 12 cycles, 3 domains (truth, light, wisdom),
max_regression=0.00, floor_score=1.00.
- holdouts/v1: 12 cycles, 2 distinct domains (creation, knowledge),
max_regression=0.00, floor_score=1.00.
Structural win demonstrated: zero regression across 34 total teaching
cycles touching 7 distinct domains.
PROGRESS.md updated to mark monotonic-learning v1 complete.
- grammatical-coverage holdout v1: 52 cases across all 13 constructions, 100% pass
- zero-code-domain-acquisition lane: contract + 3 surprise domains (kinship,
calendar, color) with vocabulary, relations, axioms, teaching examples,
and dev prompts; pack closure verified for all three domains
- he_core_cognition_v1: 20 entries in Hebrew script with morphology decomposition
(triliteral roots, binyanim, aspect/person/gender/number); depth_root role
with fail_closed OOV policy
- grc_logos_cognition_v1: 20 entries in polytonic Greek with morphology
decomposition (stems, prefix/suffix chains, declension class, tense/voice/
mood/person); depth_relation role with fail_closed OOV policy
Establish the grammatical-coverage eval lane with 13 English v1
constructions (simple declarative, negation, conjunction, disjunction,
embedded clause, relative clause, quantification, tense, aspect).
- contract.md with scoring rubric and pass thresholds
- runner.py conforming to framework interface
- dev set: 41 cases (baseline: 24.4%, only C01/C10 pass)
- public v1: 36 cases (baseline: 19.4%, only C01/C10 pass)
- holdout and realizer engineering are next
The realizer currently handles only simple present-tense SVO declaratives.
Negation, conjunction, embedding, quantification, tense, and aspect all
need engineering work.
The top-level --version flag (bool) collided with eval's --version argument
(string). Rename the top-level dest to print_version so both coexist.
Also mark Phase 0 exit gate as complete in PROGRESS.md:
- v1 public: 13/13 (100% all metrics)
- holdout: 19/19 (unsealed plaintext, encryption deferred)
- baseline: scaffold with pluggable BaselineModel protocol
Implement the eval infrastructure defined in ADR-0016 before building new
eval lanes. This establishes the discipline that governs the entire
capability roadmap.
- Generic eval framework (evals/framework.py): lane discovery, versioned
scoring, result persistence
- Cognition lane retrofitted into new convention: 45 cases split into
stratified dev (13) / public v1 (13) / holdout (19) sets with contract,
runner, and recorded results
- Generalized `core eval <lane>` CLI: dynamic lane discovery, --list,
--version, --split, --save, --json flags
- Holdout runner scaffold: plaintext fallback, encryption interface ready
- Baseline runner scaffold: pluggable frontier model interface
- Fix: CognitiveTurnPipeline.run() crashed on turn_log[-1] when the
unknown-domain gate returned a stub without appending to turn_log
- ADR-0016, eval_methodology.md, PROGRESS.md, capability gates session log
Phase 0 exit audit found two methodology issues:
1. Pipeline turn_log crash (fixed here)
2. Versor drift in multi-turn sessions (pre-existing, under investigation)
