core/evals
Shay dcbb55c7bc feat(phase4): sample-efficiency v1 — first quantitative-curve lane
First Phase 4 lane lands. Measures corrections-to-competence curves
across 17 concepts (10 public + 7 holdouts disjoint). Per-concept
curriculum is a 4-hop chain of "is" corrections; probe asks the
chain head after each cumulative-correction count; score is the
count of chain-tail tokens visible in the probe surface.

Phase 4 framework discipline ("Plot, do not threshold" per
docs/capability_roadmap.md): the lane reports quantitative curves
and one structural gate (replay_determinism >= 0.95), not the
binary pass/fail thresholds of Phases 1-3.

Results:

  split        concepts  first_hit  saturation  rate  replay
  public/v1    10        1.0        4.0         1.0   1.0
  holdouts/v1  7         1.0        4.0         1.0   1.0

Every concept's curve: [0, 1, 2, 3, 4]. One correction -> one new
chain hop -> one new token visible in surface. Perfectly linear
sample efficiency on chain curricula; no diminishing returns; no
plateau; no spurious confabulation at k=0.

What the linearity says about CORE:
  - The reviewed-teaching loop integrates each typed correction
    into the proposition-graph substrate.
  - The typed inference operator (transitive_walk, ADR-0018) surfaces
    the chain endpoint on the next probe.
  - The result is one-shot learning per correction on chain shapes -
    visible by construction, not inferred from training statistics.
  - Replay determinism = 1.0 across all snapshots means the curve
    is the deterministic function of (concept, k), not a sampled
    estimate of a stochastic process. Frontier systems cannot
    publish this curve at all because their per-snapshot output is
    not reproducible.

Lane contents:
  contract.md - specifies the curve discipline, anti-overfitting
    rules (disjoint concept sets, one-new-token-per-correction
    invariant), and reporting structure.
  runner.py - parallel sweep across snapshots, two-run replay
    check per snapshot, per-concept curve aggregation.
  dev/cases.jsonl (2 concepts) - smoke set.
  public/v1/cases.jsonl (10 concepts) - wisdom, light, truth,
    creation, meaning, reason, principle, identity, memory, question.
  holdouts/v1/cases.jsonl (7 concepts) - being, spirit, distinction,
    correction, verification, explanation, procedure.
  baselines/v1_structural_zero.json - frontier baseline by
    construction (per-snapshot reproducibility absent).
  gaps.md - findings + v2 contract refinements (branching curricula,
    distractor corrections, OOD probes, mixed-relation chains, CI
    reporting).

CLI suites smoke / teaching all pass; no regression. PROGRESS.md
updated.

Phase 4 status: 1 of 3 lanes lands as v1 complete with a clean
result. Remaining lanes: long-context-cost (vault scaling 10^3-10^6)
and multi-agent-composition (two-instance cooperation with replay
preserved per agent).
2026-05-16 15:39:28 -07:00
..
adversarial_identity docs(identity): empirical finding — fix #3 needs upstream ingest-gate work 2026-05-16 14:23:20 -07:00
calibration feat(evals): v2 lanes for calibration and symbolic-logic 2026-05-16 13:17:41 -07:00
cognition feat(evals): Phase 0 — benchmark methodology lock-in and eval framework 2026-05-15 22:36:53 -07:00
compositionality feat(phase3): compositionality, multi-step-reasoning, introspection, cross-domain-transfer v1 2026-05-16 14:48:36 -07:00
cross_domain_transfer feat(phase3): compositionality, multi-step-reasoning, introspection, cross-domain-transfer v1 2026-05-16 14:48:36 -07:00
grammatical_coverage feat(evals): grammatical-coverage v2 cases - 36 cases with deeper nesting and rarer vocabulary (100% pass) 2026-05-16 06:40:55 -07:00
identity_divergence feat(evals): identity-divergence lane v1 - 93 curriculum events, two axis profiles (Precision/Generosity), divergence/coherence/causal metrics (all pass) 2026-05-16 06:48:13 -07:00
inference_closure feat(phase3): inference-closure lane v1 — foundation OK, no operator 2026-05-16 14:33:08 -07:00
introspection feat(phase3): core/cognition/explain.py — close Gap 3 introspection 2026-05-16 15:09:48 -07:00
monotonic_learning feat(evals): v3 lanes — monotonic-learning passes, adversarial-identity reveals gap 2026-05-16 13:42:47 -07:00
multi_step_reasoning feat(phase3): compositionality, multi-step-reasoning, introspection, cross-domain-transfer v1 2026-05-16 14:48:36 -07:00
provenance feat(evals): parallel runner + adversarial-identity v2 2026-05-16 13:10:26 -07:00
reports Add cognitive eval harness and calibration replay (#30) 2026-05-15 07:41:36 -07:00
sample_efficiency feat(phase4): sample-efficiency v1 — first quantitative-curve lane 2026-05-16 15:39:28 -07:00
symbolic_logic feat(evals): v2 lanes for calibration and symbolic-logic 2026-05-16 13:17:41 -07:00
zero_code_domain_acquisition feat began creation of zero code domain acquisition. did not complete yet. 2026-05-16 06:31:01 -07:00
__init__.py Add cognitive eval harness and calibration replay (#30) 2026-05-15 07:41:36 -07:00
baseline_runner.py feat(evals): frontier structural-zero baselines for Phase 2 v1 lanes 2026-05-16 12:45:28 -07:00
cognition_cases.jsonl feat: vault recall index, Rust versor parity, cognitive pack expansion 2026-05-15 15:34:39 -07:00
framework.py feat(evals): Phase 0 — benchmark methodology lock-in and eval framework 2026-05-15 22:36:53 -07:00
holdout_runner.py feat(evals): Phase 0 — benchmark methodology lock-in and eval framework 2026-05-15 22:36:53 -07:00
metrics.py Add cognitive eval harness and calibration replay (#30) 2026-05-15 07:41:36 -07:00
parallel.py feat(evals): parallel runner + adversarial-identity v2 2026-05-16 13:10:26 -07:00
run_cognition_eval.py Add cognitive eval harness and calibration replay (#30) 2026-05-15 07:41:36 -07:00