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core/docs/PROGRESS.md
Shay 632a69db40 feat(evals): monotonic-learning lane v1 — no regression across cycles 
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.
2026-05-16 11:56:34 -07:00

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Capability Roadmap — Progress Tracker

Tracks completion of the phased plan defined in docs/capability_roadmap.md (ADR-0016). Updated as work lands.

Phase 0 — Benchmark Methodology Lock-in

Status: Complete Started: 2026-05-15 Completed: 2026-05-16

  • Promote roadmap to ADR-0016
  • Extract docs/eval_methodology.md from roadmap Part I
  • Create progress tracker (docs/PROGRESS.md)
  • Implement evals/<lane>/ directory convention
  • Build generic eval framework (evals/framework.py)
  • Retrofit core eval cognition into new convention
    • Split 45 cases into dev (13) / public v1 (13) / holdout (19)
    • Write evals/cognition/contract.md
    • Migrate runner.py to use framework
    • Record v1 results under new layout
  • Generalize core eval <lane> CLI (dynamic lane discovery)
  • Implement holdout runner scaffold
  • Implement baseline runner scaffold
  • Exit gate: core eval cognition runs under new convention with v1 public + holdout + baseline

Methodology issues discovered (Phase 0 audit)

  1. Pipeline turn_log crash: CognitiveTurnPipeline.run() assumed turn_log was always populated after chat(), but the unknown-domain gate returns a stub without appending. Fixed with fallback to tokenizer output.
  2. Versor drift in multi-turn sessions: test_pipeline_preserves_versor_closure reveals that after 3 turns in the same session, "spirit breath" causes versor_condition = 1.12e-04 (threshold: 1e-6). Pre-existing; resolved by strict runtime closure enforcement (always unitize after sandwich product).
  3. Identity/drive bias shelved: Premature persona motor and drive bias introduced trajectory drift. Removed in favour of persona-neutral generic runtime; identity returns behind explicit IdentityProfile contract.

Phase 1 — Foundational Triple

Status: Complete ✓ Started: 2026-05-16 Completed: 2026-05-16 Depends on: Phase 0 exit

  • grammatical-coverage lane (v1 + v2 complete)
    • Enumerate English v1 constructions (13 constructions: C01-C13)
    • Write contract test pairs (PropositionGraph -> surface family)
    • Implement v1 dev/public (~41/36 items)
    • Implement holdout (52 items) — 100% pass
    • Engineer realizer.py to pass v1 (dev=100%, public=100%, holdout=100%)
    • Hebrew pack (he_core_cognition_v1 with binyanim support)
    • Koine Greek pack (grc_logos_cognition_v1 with Greek morphology)
    • Generate v2 on pass (deeper nesting, longer sentences, rarer vocabulary) — 36 cases (100% pass)
  • zero-code-domain-acquisition lane (v1 complete, zero engineering gaps)
    • Define 3 surprise domains (kinship, calendar, color)
    • Build pack-only authoring kits (vocabulary, relations, axioms, teaching examples, prompts)
    • Test: author brings CORE to >=80% without Python edits (100% achieved)
    • Log engineering gaps (ZERO — pack-only authoring contract is solid)
    • v1 dev (30/30), v1 public (18/18 across all 3 domains), v1 holdout (21/21) — all 100% pass
  • identity-divergence lane (v1 complete)
    • Define two identity axis sets (Axis A: Precision-first, Axis B: Generosity-first)
    • Curate shared curriculum (93 teaching events across color/kinship/reasoning/spatial)
    • Build divergence metric (>0.30 threshold): all pass (1.000)
    • Build coherence metric (>0.85 threshold for A and B): all pass (1.000)
    • Identity-stripped baseline with causal check: all pass (delta=1.000)
    • v1 dev (5/5), v1 public (5/5), v1 holdout (5/5) — all 100% pass
  • Exit gate: All three lanes pass v1 public + holdout ✓

Phase 2 — Structural Wins Made Visible

Status: In Progress Started: 2026-05-16 Depends on: Phase 1 exit

  • provenance lane (v1 complete)
    • Define Provenance dataclass + compute_provenance() (core/cognition/provenance.py)
    • Unit tests for provenance derivation (6/6 pass — tests/test_provenance.py)
    • Build pack-axiom / vault-recall / teaching / mixed case categories
    • v1 dev (10/10), v1 public (20/20), v1 holdouts (15/15) — all 100% pass
    • Sub-metrics: replay_determinism=1.0, source_attribution=1.0, source_validity=1.0, input_sensitivity=1.0
    • Fixed shape regression in generate/stream.py score-weighted recall (np.eye → multivector identity)
    • Replaced linear-blend rotor scaling with manifold-preserving rotor_power (algebra/rotor.py); 41 closure-preservation tests
    • Restored respond()/result.final_state identity contract after anchor pull
  • monotonic-learning lane (v1 complete)
    • Define contract: longitudinal regression check across ≥10 teaching cycles
    • Implement runner: shared session, sorted ops, per-(cycle, domain) accuracy table
    • Generator (scripts/generate_monotonic_cases.py) for cycle/probe corpora
    • v1 dev (10 cycles), v1 public (12 cycles, 3 domains), v1 holdouts (12 cycles, 2 distinct domains)
    • All splits: max_regression=0.00, floor_score=1.00, overall_pass=true
    • Structural win demonstrated: zero regression across 34 total cycles / 7 distinct domains
  • calibration lane
  • symbolic-logic lane
  • adversarial-identity lane
  • Frontier baselines computed for all lanes
  • Exit gate: All five v1+v2 with baselines; at least two have v3

Phase 3 — Reasoning Depth

Status: Not Started Depends on: Phase 2 exit

  • compositionality lane (construction-family splits, not sampling)
  • inference-closure lane
  • introspection lane
  • multi-step-reasoning lane
  • cross-domain-transfer lane
  • Pin agency scope decision (responsive vs. goal-directed)
  • Pin tool-use scope decision
  • Exit gate: All five v1 scored; at least two passing v1

Phase 4 — Scale and Efficiency

Status: Not Started Depends on: Phase 3 exit

  • sample-efficiency curves (>=10 concepts)
  • long-context-cost curves (10^3 to 10^6 vault entries)
  • multi-agent-composition (>=2 agents, replay preserved)
  • Vault indexing strategy decided
  • Exit gate: All curves published with confidence intervals

Phase 5 — Curriculum Era

Status: Not Started Depends on: Phase 4 exit

  • 5.1 English fluency (grammatical-coverage v5 OOD)
  • 5.2 Hebrew fluency
  • 5.3 Koine Greek fluency
  • 5.4 Elementary mathematics
  • 5.5 Foundational physics
  • 5.6 Foundational biology
  • 5.7 Classical literature
  • Phase 1-4 lanes re-run on every release (no regression)

Open Scope Decisions

Decision Status Deadline
Agency (responsive vs. goal-directed) Open Before Phase 3
Tool use (typed deterministic operators) Open Before Phase 3
Code generation (first-class target) Open Before Phase 5
Embodiment (sensorium gates) Open Phase 5