core/docs/PROGRESS.md
Shay 819c8b81ac feat(phase3): compositionality, multi-step-reasoning, introspection, cross-domain-transfer v1
Spreads the four remaining Phase 3 lanes to map the full reasoning-
depth surface alongside inference-closure (already landed at e509e0d).
Each lane is a v1 honest probe per the roadmap; engineering work
follows once the full surface is visible.

Results across all five Phase 3 lanes:

  lane                      split        primary signal  foundation
  inference-closure         public/v1    0.0             1.0 / 1.0
  inference-closure         holdouts/v1  0.0             1.0 / 1.0
  compositionality          public/v1   0.0625 (1/16)   1.0 / 1.0
  compositionality          holdouts/v1  0.0             1.0 / 1.0
  multi-step-reasoning      public/v1    0.0             1.0 / 1.0
  multi-step-reasoning      holdouts/v1  0.0             1.0 / 1.0
  introspection             public/v1    0.0 (no api)    n/a
  introspection             holdouts/v1  0.0             n/a
  cross-domain-transfer     public/v1    0.0             1.0 / 1.0
  cross-domain-transfer     holdouts/v1  0.0             1.0 / 1.0

Foundation guarantees (storage + replay) intact across every lane
that has them. The reasoning-depth signal is uniformly zero. The
five lanes triangulate four architectural gaps:

  Gap 1. generate/graph_planner.py has no transitive composition.
  Gap 2. field/propagate.py has no derivable-but-not-asserted recall.
  Gap 3. core/cognition/explain.py module does not exist.
  Gap 4. no structural-pattern recogniser (cross-subdomain transfer).

Gaps 1, 2, 4 cluster on the same code surface and may close together
as a single bounded PR. Gap 3 is independent module-creation work.

Lane scaffolding mirrors inference-closure (contract.md, runner.py,
dev + public/v1 + holdouts/v1 cases.jsonl, baselines/v1_structural_zero.json,
gaps.md). All runners are parallel-safe and use the standard
run_lane(cases, *, config, workers) interface.

Per-lane gaps.md records the engineering shape for v2 plus future
directions worth not forgetting:
  - compositionality/gaps.md: metaphor is compositionality with
    selective property transfer; building it is correctly downstream
    of closing this lane.
  - cross-domain-transfer/gaps.md: metaphor + narrative as
    cross-domain operators; narrative requires the Agency open-scope
    decision to pin first.
  - introspection/gaps.md: explain API is also the substrate for
    first-person narrative self-account.

Recommended v2 sequence in docs/PROGRESS.md:
  1. Pin Agency + Tool-use open-scope decisions (deadline: before
     Phase 3 engineering).
  2. Engineer Gaps 1 + 2 as one bounded PR.
  3. Engineer Gap 3 independently.
  4. Re-author cross-domain-transfer v2 with matched-control
     contract refinement.

Phase 3 v1 exit: 0/5 lanes passing, which is the expected v1 floor.
CLI suites smoke / cognition / teaching pass; no regression on
Phase 2.
2026-05-16 14:48:36 -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
- [x] Promote roadmap to ADR-0016
- [x] Extract `docs/eval_methodology.md` from roadmap Part I
- [x] Create progress tracker (`docs/PROGRESS.md`)
- [x] Implement `evals/<lane>/` directory convention
- [x] Build generic eval framework (`evals/framework.py`)
- [x] Retrofit `core eval cognition` into new convention
- [x] Split 45 cases into dev (13) / public v1 (13) / holdout (19)
- [x] Write `evals/cognition/contract.md`
- [x] Migrate `runner.py` to use framework
- [x] Record v1 results under new layout
- [x] Generalize `core eval <lane>` CLI (dynamic lane discovery)
- [x] Implement holdout runner scaffold
- [x] Implement baseline runner scaffold
- [x] **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
- [x] **grammatical-coverage** lane (v1 + v2 complete)
- [x] Enumerate English v1 constructions (13 constructions: C01-C13)
- [x] Write contract test pairs (PropositionGraph -> surface family)
- [x] Implement v1 dev/public (~41/36 items)
- [x] Implement holdout (52 items) — 100% pass
- [x] Engineer `realizer.py` to pass v1 (dev=100%, public=100%, holdout=100%)
- [x] Hebrew pack (`he_core_cognition_v1` with binyanim support)
- [x] Koine Greek pack (`grc_logos_cognition_v1` with Greek morphology)
- [x] Generate v2 on pass (deeper nesting, longer sentences, rarer vocabulary) — 36 cases (100% pass)
- [x] **zero-code-domain-acquisition** lane (v1 complete, zero engineering gaps)
- [x] Define 3 surprise domains (kinship, calendar, color)
- [x] Build pack-only authoring kits (vocabulary, relations, axioms, teaching examples, prompts)
- [x] Test: author brings CORE to >=80% without Python edits (100% achieved)
- [x] Log engineering gaps (ZERO — pack-only authoring contract is solid)
- [x] v1 dev (30/30), v1 public (18/18 across all 3 domains), v1 holdout (21/21) — all 100% pass
- [x] **identity-divergence** lane (v1 complete)
- [x] Define two identity axis sets (Axis A: Precision-first, Axis B: Generosity-first)
- [x] Curate shared curriculum (93 teaching events across color/kinship/reasoning/spatial)
- [x] Build divergence metric (>0.30 threshold): all pass (1.000)
- [x] Build coherence metric (>0.85 threshold for A and B): all pass (1.000)
- [x] Identity-stripped baseline with causal check: all pass (delta=1.000)
- [x] v1 dev (5/5), v1 public (5/5), v1 holdout (5/5) — all 100% pass
- [x] **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
- [x] **provenance** lane (v1 complete)
- [x] Define Provenance dataclass + compute_provenance() (`core/cognition/provenance.py`)
- [x] Unit tests for provenance derivation (6/6 pass — `tests/test_provenance.py`)
- [x] Build pack-axiom / vault-recall / teaching / mixed case categories
- [x] v1 dev (10/10), v1 public (20/20), v1 holdouts (15/15) — all 100% pass
- [x] Sub-metrics: replay_determinism=1.0, source_attribution=1.0, source_validity=1.0, input_sensitivity=1.0
- [x] Fixed shape regression in `generate/stream.py` score-weighted recall (np.eye → multivector identity)
- [x] Replaced linear-blend rotor scaling with manifold-preserving `rotor_power` (`algebra/rotor.py`); 41 closure-preservation tests
- [x] Restored `respond()`/`result.final_state` identity contract after anchor pull
- [x] **monotonic-learning** lane (v1 complete)
- [x] Define contract: longitudinal regression check across ≥10 teaching cycles
- [x] Implement runner: shared session, sorted ops, per-(cycle, domain) accuracy table
- [x] Generator (`scripts/generate_monotonic_cases.py`) for cycle/probe corpora
- [x] v1 dev (10 cycles), v1 public (12 cycles, 3 domains), v1 holdouts (12 cycles, 2 distinct domains)
- [x] All splits: max_regression=0.00, floor_score=1.00, overall_pass=true
- [x] Structural win demonstrated: zero regression across 34 total cycles / 7 distinct domains
- [x] **calibration** lane (v1 complete)
- [x] Define contract: typed signals for no_grounding / coherent / correction_proposed
- [x] Classification from `CognitiveTurnResult` (vault_hits + pack_mutation_proposal)
- [x] Runner with per-case fresh pipeline (avoids cross-case field drift)
- [x] v1 dev (12/12), v1 public (24/24), v1 holdouts (18/18) — all 100% pass
- [x] Sub-metrics: no_grounding=1.0, coherent=1.0, correction_proposed=1.0
- [x] Architectural finding documented (`evals/calibration/gaps.md`): the
ingest gate is geometric, not semantic — 6/42 hand-chosen OOD
prompts fire the geometric gate. v1 measures recall-presence +
correction-firing signals (deterministic), not semantic OOD.
Pipeline override of gate's safety surface is a separate gap.
- [x] **symbolic-logic** lane (v1 complete)
- [x] Define contract: structural foundations for proposition-based inference
- [x] Patterns: modus_ponens_chain, modus_tollens_chain, syllogism, negation, chain_recall
- [x] Runner: per-case fresh pipeline + double-run replay check
- [x] Sub-metrics: premise_recall=1.0, replay_determinism=1.0, proposal_storage=1.0
- [x] v1 dev (8/8), v1 public (18/18), v1 holdouts (12/12) — all 100% pass
- [x] Architectural finding documented (`evals/symbolic_logic/gaps.md`): CORE
has no first-class inference operator yet. v1 measures the storage,
replay, and recall foundations on which a future inference engine
would be built. v2 would assert specific inference correctness
(transitive recall surface contents).
- [x] **adversarial-identity** lane (v1 complete)
- [x] Define contract: identity-override attacks rejected at review;
legitimate corrections still accepted
- [x] Cover all `_IDENTITY_MARKERS` families (you are / forget / pretend /
override / ignore / your name / act as / from now / character /
personality)
- [x] Per-case fresh pipeline; prior question primes the review surface
- [x] Sub-metrics: attack_rejection_rate=1.0, legitimate_acceptance_rate=1.0
- [x] v1 dev (10/10), v1 public (25/25), v1 holdouts (18/18) — all 100% pass
- [x] **All five Phase 2 v1 lanes passing**
- [x] Frontier baselines computed for all lanes (structural-zero floor)
- [x] `docs/frontier_baselines.md` — per-lane analysis: frontier LLMs do
not emit the typed signals CORE's rubrics score against
(provenance sources, pack_mutation_proposal, vault_hits,
REJECTED_IDENTITY outcome, deterministic trace_hash)
- [x] Per-lane structural-zero baseline JSON written under
`evals/<lane>/baselines/v1_structural_zero.json`
- [x] `StructuralZeroBaseline` adapter in `evals/baseline_runner.py`
— deterministic floor; live-API adapters can be added when
keys are configured
- [x] v2 lanes: all five at 100% pass
- monotonic-learning v2 — 20 cyc / 5 dom (public), 18 cyc / 4 dom (holdouts)
- provenance v2 — 30 + 20 cases, all sub-metrics 1.0
- adversarial-identity v2 — 35 + 22 cases, all 1.0
- calibration v2 — 33 + 24 cases, all class accuracies 1.0
- symbolic-logic v2 — 24 + 16 cases (chains up to 5 hops), all 1.0
- [x] **Exit gate:** v3 lanes for at least two of the five ✓
- monotonic-learning v3 — 30 cyc / 7 dom (public), 25 cyc / 6 dom (holdouts),
`max_regression=0.0`, `floor_score=1.0` on both splits
- adversarial-identity v3 — 30 + 20 paraphrased-attack cases.
Initial v3 result (pre-fix): `attack_rejection_rate=0.0`,
`legitimate_acceptance_rate=1.0`. v3 was a load-bearing finding
that exposed the marker-string defense as brittle to paraphrase.
### Identity-override defense — fix #2 + fix #3 (2026-05-16)
Triggered by the v3 finding above. Two-layer defense now active in
`teaching/review.py`:
- **Fix #2 (syntactic).** `_is_identity_override` applies four
deterministic rules: (a) legacy markers, (b) redirect-verb +
role-frame co-occurrence, (c) negating qualifier ±3 tokens from a
role-frame, (d) negating qualifier ±3 tokens from a redirect-verb.
- **Fix #3 (geometric).** `IdentityCheck.would_violate(score, manifold)`
predicate added to `core/physics/identity.py`; `review_correction`
now accepts `identity_score` / `identity_manifold` kwargs and is
wired in `CognitiveTurnPipeline._run_teaching` from
`response.identity_score`.
Lane results after both fixes:
| split | attacks | attack_rej | legit_acc |
|---|---|---|---|
| public/v1 | 15 | 1.0 | 1.0 |
| holdouts/v1 | 10 | 1.0 | 1.0 |
| public/v2 | 20 | 1.0 | 1.0 |
| holdouts/v2 | 12 | 1.0 | 1.0 |
| public/v3 | 20 | 1.0 | 1.0 |
| holdouts/v3 | 12 | 1.0 | 1.0 |
| public/v4 | 20 | 1.0 | 1.0 |
| holdouts/v4 | 12 | 1.0 | 1.0 |
| public/v5 | 20 | 1.0 | 1.0 |
| holdouts/v5 | 12 | 1.0 | 1.0 |
v4 is the regression gate for fix #2 — new attack vocabulary
combinations that exercise rules (b)/(c)/(d) without repeating v3's
specific surface. v5 is the regression gate for the normalization
layer — contractions (`you're`/`it's`/`let's`/`don't`), curly quotes
(U+2018/U+2019), em-dashes, and verb morphology (`becoming` /
`transformed` / `dropped` / `becomes`) — all now folded before rule
evaluation. All v1v5 splits pass at 100%; legitimate-correction
false-positive rate is 0% (including legitimates that themselves
use contractions: `wisdom's broader`, `knowledge isn't merely
collected`, etc.).
Honest finding: with the current default `IdentityManifold` (three
unit-axis ValueAxes), the geometric layer flags 0/32 of v3 attacks
independently of fix #2. The predicate and wiring are in place; the
manifold's axis design is the limiting factor and needs sharpening
before the geometric defense can carry weight on its own. See
`evals/adversarial_identity/gaps.md`.
### Geometric-axis sharpening investigation (2026-05-16)
A focused empirical investigation against v3 and v5 (preserved as
`evals/adversarial_identity/calibration/probe_field_signature.py`)
swept every candidate per-case discriminator derivable from the
existing CognitiveTurnResult — `identity_score.alignment`, field-delta
L2 norm, semantic-coord energy ratio, `vault_hits`, surface length,
intent tag. **No signal separated attack from legitimate at the
per-case level.** `identity_score.alignment` is 1.000 universally;
field-delta distributions overlap heavily; vault retrieval grounds
both kinds similarly.
The pipeline encodes identity-override attacks and legitimate
corrections into statistically indistinguishable field-state
geometries. No amount of axis-direction sharpening on the
IdentityManifold can recover a signal that isn't present in the
trajectory data being projected.
**Architectural conclusion:** fix #3 cannot be made load-bearing
in place. The required upstream work — encoding token semantic
categories into specific blade coordinates of the field versor at
the ingest gate, then redefining the IdentityManifold axes in the
32-dim Cl(4,1) basis with a real inner-product projection — is a
scoped multi-PR effort, not a single sharpening exercise. The
calibration probe stands as the empirical baseline that any future
ingest-gate change must beat before fix #3 can be claimed
load-bearing. See `evals/adversarial_identity/gaps.md` for the
full table of measured signals and the recommended path.
**What stands today as the load-bearing defense:** fix #2
(syntactic rules a/b/c/d) + the normalization layer reject 100% of
v1v5 attacks (n=121) with 0 false positives on 51 legitimate
corrections. Fix #3's predicate, unit tests, and wiring remain as
scaffolding for the upstream work above.
## Phase 2 — COMPLETE
All five Phase 2 v1+v2 lanes pass at 100%; frontier structural
baselines documented; v3 satisfies the exit-gate requirement (two
lanes, one demonstrating a passing structural-depth test and one
demonstrating an architectural vulnerability that the geometric
identity-check fix in `evals/adversarial_identity/gaps.md` would
close).
### Parallel eval infrastructure (2026-05-16)
- `evals/parallel.py``run_cases_parallel()` helper using
`multiprocessing.Pool` with the `"spawn"` start method (avoids
forking heavy parent state). Default workers = `min(cpu_count, 8)`.
- Wired into the four per-case lanes (provenance, calibration,
symbolic-logic, adversarial-identity). `run_lane(..., workers=N)`
controls parallelism; `workers=1` forces serial for debugging.
- Empirical speedup (adversarial-identity public/v1, 25 cases):
serial 14.1s → parallel 3.1s (~4.5x).
- Monotonic-learning intentionally stays serial within a split
(shared longitudinal session by design).
---
## Phase 3 — Reasoning Depth — IN PROGRESS
### inference-closure v1 (2026-05-16) — honest failure, gap filed
First Phase 3 lane built and run. Scores derivation of entailments
that were not directly asserted (transitive `is` / `precedes` /
`grounds` / `causes` / `belongs_to` chains) over the
`en_core_cognition_v1` relation vocabulary.
| split | n | derived_recall_rate | premises_stored_rate | replay_determinism | overall_pass |
|---|---|---|---|---|---|
| public/v1 | 20 | **0.0** | 1.0 | 1.0 | False |
| holdouts/v1 | 12 | **0.0** | 1.0 | 1.0 | False |
**v1 is the expected honest failure** per the roadmap. Foundation
guarantees from Phase 2 (storage and replay determinism) hold at this
depth: every premise emits a `PackMutationProposal`, every
(premises, probe) sequence is trace-hash-deterministic. The
inference-closure step itself does not yet exist in CORE.
**Architectural gaps filed
(`evals/inference_closure/gaps.md`):**
1. `generate/graph_planner.py` has no transitive composition — the
probe's articulation target picks a single node; no chained
relation walk produces the derived entailment.
2. `field/propagate.py` has no derivable-but-not-asserted recall —
vault retrieval scores direct CGA inner products; no path-recall
operator over relation-typed edges.
Both gaps are v2 engineering candidates and may share a single
implementation surface. Structural-zero frontier baseline recorded:
frontier LLMs do not emit the typed signals these sub-metrics score
by construction.
### Phase 3 v1 sweep complete (2026-05-16) — all five lanes scored
| Lane | split | primary signal | foundation (stored / replay) |
|---|---|---|---|
| inference-closure | public | derived_recall = **0.0** | 1.0 / 1.0 |
| inference-closure | holdouts | 0.0 | 1.0 / 1.0 |
| compositionality | public | compositional = **0.0625** (1/16, fluke) | 1.0 / 1.0 |
| compositionality | holdouts | 0.0 | 1.0 / 1.0 |
| multi-step-reasoning | public | endpoint = **0.0** | 1.0 / 1.0 |
| multi-step-reasoning | holdouts | 0.0 | 1.0 / 1.0 |
| introspection | public | explain_api_present = **0.0** | n/a |
| introspection | holdouts | 0.0 | n/a |
| cross-domain-transfer | public | transfer = **0.0** | 1.0 / 1.0 |
| cross-domain-transfer | holdouts | 0.0 | 1.0 / 1.0 |
**The signal across all five lanes is unanimous:** Phase 2 storage
+ replay guarantees hold at this depth (1.0 across the board); the
reasoning-depth signal is uniformly zero. The five lanes
triangulate the same architectural gap from five angles:
- **Gap 1: `generate/graph_planner.py` has no transitive
composition.** `plan_articulation` picks a single node; no
chained relation walk synthesizes derived nodes.
- **Gap 2: `field/propagate.py` has no derivable-but-not-asserted
recall.** Vault retrieval is direct CGA inner product; no
path-recall operator over relation-typed edges.
- **Gap 3: no `core/cognition/explain.py` module.** No primitive
exists to generate a natural-language account of a prior turn.
- **Gap 4: no structural-pattern recogniser.** Relation patterns
are not first-class entities; subdomain-A teaching does not shape
subdomain-B competence.
Gaps 1, 2, 4 cluster on the same code surface (graph planner +
field propagate) and may close together. Gap 3 is a distinct
module-creation work item.
### Phase 3 v2 work plan (recommended sequence)
1. **Pin the open scope decisions** flagged "Before Phase 3" in
the Open Scope Decisions table below — Agency (responsive vs.
goal-directed) and Tool use (typed deterministic operators).
Transitive composition under (2) is essentially a typed
deterministic operator, so the tool-use decision shapes how the
work below should be structured.
2. **Engineer Gaps 1 + 2** as one bounded PR: a typed
`transitive_walk(graph, head, relation, max_hops)` operator in
`graph_planner.py` + a `path_recall(vault, entity, relation_chain)`
operator in `field/propagate.py`. Both deterministic, both
exact-CGA. Re-run inference-closure, multi-step-reasoning,
compositionality, cross-domain-transfer to score the lift.
3. **Engineer Gap 3** independently: `core/cognition/explain.py`
producing deterministic natural-language accounts that round-trip.
4. **Re-author cross-domain-transfer v2** with the matched-control
comparison contract refinement once B-arm recall is non-zero.
### Phase 3 v1 — DONE
All five lanes have v1 results with honest scores. Each failure has
a documented architectural deferral (`gaps.md` per lane). Phase 3
exit requires ≥ 2 lanes passing v1 by phase exit; today 0 / 5 pass,
which is the expected v1 floor. Phase 3 exit is gated on the v2
engineering above.
## 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 |