diff --git a/docs/PROGRESS.md b/docs/PROGRESS.md index 2c97ca51..18c05a30 100644 --- a/docs/PROGRESS.md +++ b/docs/PROGRESS.md @@ -87,7 +87,15 @@ Tracks completion of the phased plan defined in `docs/capability_roadmap.md` - [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) -- [ ] **monotonic-learning** lane + - [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 - [ ] **calibration** lane - [ ] **symbolic-logic** lane - [ ] **adversarial-identity** lane diff --git a/evals/monotonic_learning/__init__.py b/evals/monotonic_learning/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/evals/monotonic_learning/contract.md b/evals/monotonic_learning/contract.md new file mode 100644 index 00000000..336872da --- /dev/null +++ b/evals/monotonic_learning/contract.md @@ -0,0 +1,114 @@ +# monotonic-learning eval lane + +## What it measures + +After teaching CORE about new domains across many cycles, competence on +previously taught domains must not regress. This tests the architectural +claim that learning is additive in CORE — new teaching extends the system +without overwriting prior competence. + +This is the longitudinal counterpart to provenance: provenance proves a +single turn is grounded; monotonic-learning proves grounding *survives* +across teaching cycles. + +## Why it matters (structural win) + +Frontier LLMs, when fine-tuned on new domains, exhibit catastrophic +forgetting: competence on prior domains regresses as new ones are added. +This is a structural property of gradient-based weight updates over a +shared parameter pool. + +CORE, by construction, adds teaching examples to a bounded append-only +store and grows the vault rather than overwriting weights. The structural +prediction: zero regression on prior domains as new ones accumulate. + +## Protocol + +A single longitudinal protocol per split, expressed as an ordered sequence +of operations: + +``` +cycle 0: probe all domains (baseline) +cycle 1: teach domain A; probe all +cycle 2: teach domain A again; probe all +... +cycle N: teach domain Z; probe all +``` + +Each *teaching step* is a sequence of prompts the runner feeds through +`CognitiveTurnPipeline` — typically a context prompt followed by a +correction-intent prompt, which triggers the reviewed teaching loop. + +Each *probe* is a prompt with expected terms / expected intent. The probe's +pass status is recorded per cycle so we can detect regression. + +## Sub-metrics + +### M1. Per-domain non-regression + +For every domain D and every cycle c after D was first taught, the +per-domain score at cycle c must be ≥ the score at the cycle when D was +first taught, minus a tolerance ε. + +`regression(D, c) = max(score_first(D) - score(D, c), 0)` +`max_regression = max over (D, c) of regression(D, c)` + +**Pass threshold:** `max_regression ≤ 0.05` (allow 5% noise floor). + +### M2. Minimum competence floor + +After all teaching cycles complete, every taught domain must score above a +floor. + +`floor_score = min over domains D of score(D, final_cycle)` + +**Pass threshold:** `floor_score ≥ 0.80`. + +### M3. Cycle count discipline + +The roadmap requires ≥10 teaching cycles. Lanes with fewer cycles are +shorter than the methodology contract allows. + +**Pass threshold:** `cycle_count ≥ 10`. + +## Pass thresholds (v1) + +| Metric | Threshold | +|--------|-----------| +| max_regression | ≤ 0.05 | +| floor_score | ≥ 0.80 | +| cycle_count | ≥ 10 | +| Overall | all three pass | + +## Case format + +The case file is a flat JSONL where each row is one operation in the +protocol. The runner sequences them by `cycle` and `op` fields. + +```json +{"cycle": 0, "op": "probe", "domain": "truth", "id": "P-truth-1", + "prompt": "What is truth?", "expected_terms": ["truth"]} +{"cycle": 1, "op": "teach", "domain": "truth", + "prime": ["What is truth?"], "prompt": "Actually truth is coherent."} +{"cycle": 1, "op": "probe", "domain": "truth", "id": "P-truth-1", + "prompt": "What is truth?", "expected_terms": ["truth"]} +``` + +Operations: + +- `probe`: scored prompt; passes if `expected_terms` all appear in the + articulation surface (case-insensitive). +- `teach`: a sequence of `prime` prompts followed by a `prompt` (the + correction). Not scored; updates session + teaching store state. + +## Data layout + +``` +evals/monotonic_learning/ + contract.md + runner.py + dev/cases.jsonl + public/v1/cases.jsonl + holdouts/v1/cases.jsonl + results/ +``` diff --git a/evals/monotonic_learning/dev/cases.jsonl b/evals/monotonic_learning/dev/cases.jsonl new file mode 100644 index 00000000..283c36bd --- /dev/null +++ b/evals/monotonic_learning/dev/cases.jsonl @@ -0,0 +1,54 @@ +{"cycle": 0, "op": "probe", "domain": "truth", "id": "PT-1", "prompt": "What is truth?", "expected_terms": ["truth"]} +{"cycle": 0, "op": "probe", "domain": "truth", "id": "PT-2", "prompt": "Is truth coherent?", "expected_terms": ["truth"]} +{"cycle": 0, "op": "probe", "domain": "light", "id": "PL-1", "prompt": "What is light?", "expected_terms": ["light"]} +{"cycle": 0, "op": "probe", "domain": "light", "id": "PL-2", "prompt": "Why does light reveal?", "expected_terms": ["light"]} +{"cycle": 1, "op": "teach", "domain": "truth", "prime": ["What is truth?"], "prompt": "Actually truth is more than that."} +{"cycle": 1, "op": "probe", "domain": "truth", "id": "PT-1", "prompt": "What is truth?", "expected_terms": ["truth"]} +{"cycle": 1, "op": "probe", "domain": "truth", "id": "PT-2", "prompt": "Is truth coherent?", "expected_terms": ["truth"]} +{"cycle": 1, "op": "probe", "domain": "light", "id": "PL-1", "prompt": "What is light?", "expected_terms": ["light"]} +{"cycle": 1, "op": "probe", "domain": "light", "id": "PL-2", "prompt": "Why does light reveal?", "expected_terms": ["light"]} +{"cycle": 2, "op": "teach", "domain": "light", "prime": ["What is light?"], "prompt": "Actually light is more than that."} +{"cycle": 2, "op": "probe", "domain": "truth", "id": "PT-1", "prompt": "What is truth?", "expected_terms": ["truth"]} +{"cycle": 2, "op": "probe", "domain": "truth", "id": "PT-2", "prompt": "Is truth coherent?", "expected_terms": ["truth"]} +{"cycle": 2, "op": "probe", "domain": "light", "id": "PL-1", "prompt": "What is light?", "expected_terms": ["light"]} +{"cycle": 2, "op": "probe", "domain": "light", "id": "PL-2", "prompt": "Why does light reveal?", "expected_terms": ["light"]} +{"cycle": 3, "op": "teach", "domain": "truth", "prime": ["What is truth?"], "prompt": "No, truth requires deeper understanding."} +{"cycle": 3, "op": "probe", "domain": "truth", "id": "PT-1", "prompt": "What is truth?", "expected_terms": ["truth"]} +{"cycle": 3, "op": "probe", "domain": "truth", "id": "PT-2", "prompt": "Is truth coherent?", "expected_terms": ["truth"]} +{"cycle": 3, "op": "probe", "domain": "light", "id": "PL-1", "prompt": "What is light?", "expected_terms": ["light"]} +{"cycle": 3, "op": "probe", "domain": "light", "id": "PL-2", "prompt": "Why does light reveal?", "expected_terms": ["light"]} +{"cycle": 4, "op": "teach", "domain": "light", "prime": ["What is light?"], "prompt": "No, light requires deeper understanding."} +{"cycle": 4, "op": "probe", "domain": "truth", "id": "PT-1", "prompt": "What is truth?", "expected_terms": ["truth"]} +{"cycle": 4, "op": "probe", "domain": "truth", "id": "PT-2", "prompt": "Is truth coherent?", "expected_terms": ["truth"]} +{"cycle": 4, "op": "probe", "domain": "light", "id": "PL-1", "prompt": "What is light?", "expected_terms": ["light"]} +{"cycle": 4, "op": "probe", "domain": "light", "id": "PL-2", "prompt": "Why does light reveal?", "expected_terms": ["light"]} +{"cycle": 5, "op": "teach", "domain": "truth", "prime": ["What is truth?"], "prompt": "Actually truth relates to coherence."} +{"cycle": 5, "op": "probe", "domain": "truth", "id": "PT-1", "prompt": "What is truth?", "expected_terms": ["truth"]} +{"cycle": 5, "op": "probe", "domain": "truth", "id": "PT-2", "prompt": "Is truth coherent?", "expected_terms": ["truth"]} +{"cycle": 5, "op": "probe", "domain": "light", "id": "PL-1", "prompt": "What is light?", "expected_terms": ["light"]} +{"cycle": 5, "op": "probe", "domain": "light", "id": "PL-2", "prompt": "Why does light reveal?", "expected_terms": ["light"]} +{"cycle": 6, "op": "teach", "domain": "light", "prime": ["What is light?"], "prompt": "Actually light relates to coherence."} +{"cycle": 6, "op": "probe", "domain": "truth", "id": "PT-1", "prompt": "What is truth?", "expected_terms": ["truth"]} +{"cycle": 6, "op": "probe", "domain": "truth", "id": "PT-2", "prompt": "Is truth coherent?", "expected_terms": ["truth"]} +{"cycle": 6, "op": "probe", "domain": "light", "id": "PL-1", "prompt": "What is light?", "expected_terms": ["light"]} +{"cycle": 6, "op": "probe", "domain": "light", "id": "PL-2", "prompt": "Why does light reveal?", "expected_terms": ["light"]} +{"cycle": 7, "op": "teach", "domain": "truth", "prime": ["What is truth?"], "prompt": "Actually truth is more than that."} +{"cycle": 7, "op": "probe", "domain": "truth", "id": "PT-1", "prompt": "What is truth?", "expected_terms": ["truth"]} +{"cycle": 7, "op": "probe", "domain": "truth", "id": "PT-2", "prompt": "Is truth coherent?", "expected_terms": ["truth"]} +{"cycle": 7, "op": "probe", "domain": "light", "id": "PL-1", "prompt": "What is light?", "expected_terms": ["light"]} +{"cycle": 7, "op": "probe", "domain": "light", "id": "PL-2", "prompt": "Why does light reveal?", "expected_terms": ["light"]} +{"cycle": 8, "op": "teach", "domain": "light", "prime": ["What is light?"], "prompt": "Actually light is more than that."} +{"cycle": 8, "op": "probe", "domain": "truth", "id": "PT-1", "prompt": "What is truth?", "expected_terms": ["truth"]} +{"cycle": 8, "op": "probe", "domain": "truth", "id": "PT-2", "prompt": "Is truth coherent?", "expected_terms": ["truth"]} +{"cycle": 8, "op": "probe", "domain": "light", "id": "PL-1", "prompt": "What is light?", "expected_terms": ["light"]} +{"cycle": 8, "op": "probe", "domain": "light", "id": "PL-2", "prompt": "Why does light reveal?", "expected_terms": ["light"]} +{"cycle": 9, "op": "teach", "domain": "truth", "prime": ["What is truth?"], "prompt": "No, truth requires deeper understanding."} +{"cycle": 9, "op": "probe", "domain": "truth", "id": "PT-1", "prompt": "What is truth?", "expected_terms": ["truth"]} +{"cycle": 9, "op": "probe", "domain": "truth", "id": "PT-2", "prompt": "Is truth coherent?", "expected_terms": ["truth"]} +{"cycle": 9, "op": "probe", "domain": "light", "id": "PL-1", "prompt": "What is light?", "expected_terms": ["light"]} +{"cycle": 9, "op": "probe", "domain": "light", "id": "PL-2", "prompt": "Why does light reveal?", "expected_terms": ["light"]} +{"cycle": 10, "op": "teach", "domain": "light", "prime": ["What is light?"], "prompt": "No, light requires deeper understanding."} +{"cycle": 10, "op": "probe", "domain": "truth", "id": "PT-1", "prompt": "What is truth?", "expected_terms": ["truth"]} +{"cycle": 10, "op": "probe", "domain": "truth", "id": "PT-2", "prompt": "Is truth coherent?", "expected_terms": ["truth"]} +{"cycle": 10, "op": "probe", "domain": "light", "id": "PL-1", "prompt": "What is light?", "expected_terms": ["light"]} +{"cycle": 10, "op": "probe", "domain": "light", "id": "PL-2", "prompt": "Why does light reveal?", "expected_terms": ["light"]} diff --git a/evals/monotonic_learning/holdouts/v1/cases.jsonl b/evals/monotonic_learning/holdouts/v1/cases.jsonl new file mode 100644 index 00000000..fd28eec0 --- /dev/null +++ b/evals/monotonic_learning/holdouts/v1/cases.jsonl @@ -0,0 +1,90 @@ +{"cycle": 0, "op": "probe", "domain": "creation", "id": "PC-1", "prompt": "What is creation?", "expected_terms": ["creation"]} +{"cycle": 0, "op": "probe", "domain": "creation", "id": "PC-2", "prompt": "Why does creation matter?", "expected_terms": ["creation"]} +{"cycle": 0, "op": "probe", "domain": "creation", "id": "PC-3", "prompt": "Is creation ongoing?", "expected_terms": ["creation"]} +{"cycle": 0, "op": "probe", "domain": "knowledge", "id": "PK-1", "prompt": "What is knowledge?", "expected_terms": ["knowledge"]} +{"cycle": 0, "op": "probe", "domain": "knowledge", "id": "PK-2", "prompt": "Is knowledge wisdom?", "expected_terms": ["knowledge"]} +{"cycle": 0, "op": "probe", "domain": "knowledge", "id": "PK-3", "prompt": "Why does knowledge matter?", "expected_terms": ["knowledge"]} +{"cycle": 1, "op": "teach", "domain": "creation", "prime": ["What is creation?"], "prompt": "Actually creation is more than that."} +{"cycle": 1, "op": "probe", "domain": "creation", "id": "PC-1", "prompt": "What is creation?", "expected_terms": ["creation"]} +{"cycle": 1, "op": "probe", "domain": "creation", "id": "PC-2", "prompt": "Why does creation matter?", "expected_terms": ["creation"]} +{"cycle": 1, "op": "probe", "domain": "creation", "id": "PC-3", "prompt": "Is creation ongoing?", "expected_terms": ["creation"]} +{"cycle": 1, "op": "probe", "domain": "knowledge", "id": "PK-1", "prompt": "What is knowledge?", "expected_terms": ["knowledge"]} +{"cycle": 1, "op": "probe", "domain": "knowledge", "id": "PK-2", "prompt": "Is knowledge wisdom?", "expected_terms": ["knowledge"]} +{"cycle": 1, "op": "probe", "domain": "knowledge", "id": "PK-3", "prompt": "Why does knowledge matter?", "expected_terms": ["knowledge"]} +{"cycle": 2, "op": "teach", "domain": "knowledge", "prime": ["What is knowledge?"], "prompt": "Actually knowledge is more than that."} +{"cycle": 2, "op": "probe", "domain": "creation", "id": "PC-1", "prompt": "What is creation?", "expected_terms": ["creation"]} +{"cycle": 2, "op": "probe", "domain": "creation", "id": "PC-2", "prompt": "Why does creation matter?", "expected_terms": ["creation"]} +{"cycle": 2, "op": "probe", "domain": "creation", "id": "PC-3", "prompt": "Is creation ongoing?", "expected_terms": ["creation"]} +{"cycle": 2, "op": "probe", "domain": "knowledge", "id": "PK-1", "prompt": "What is knowledge?", "expected_terms": ["knowledge"]} +{"cycle": 2, "op": "probe", "domain": "knowledge", "id": "PK-2", "prompt": "Is knowledge wisdom?", "expected_terms": ["knowledge"]} +{"cycle": 2, "op": "probe", "domain": "knowledge", "id": "PK-3", "prompt": "Why does knowledge matter?", "expected_terms": ["knowledge"]} +{"cycle": 3, "op": "teach", "domain": "creation", "prime": ["What is creation?"], "prompt": "No, creation requires deeper understanding."} +{"cycle": 3, "op": "probe", "domain": "creation", "id": "PC-1", "prompt": "What is creation?", "expected_terms": ["creation"]} +{"cycle": 3, "op": "probe", "domain": "creation", "id": "PC-2", "prompt": "Why does creation matter?", "expected_terms": ["creation"]} +{"cycle": 3, "op": "probe", "domain": "creation", "id": "PC-3", "prompt": "Is creation ongoing?", "expected_terms": ["creation"]} +{"cycle": 3, "op": "probe", "domain": "knowledge", "id": "PK-1", "prompt": "What is knowledge?", "expected_terms": ["knowledge"]} +{"cycle": 3, "op": "probe", "domain": "knowledge", "id": "PK-2", "prompt": "Is knowledge wisdom?", "expected_terms": ["knowledge"]} +{"cycle": 3, "op": "probe", "domain": "knowledge", "id": "PK-3", "prompt": "Why does knowledge matter?", "expected_terms": ["knowledge"]} +{"cycle": 4, "op": "teach", "domain": "knowledge", "prime": ["What is knowledge?"], "prompt": "No, knowledge requires deeper understanding."} +{"cycle": 4, "op": "probe", "domain": "creation", "id": "PC-1", "prompt": "What is creation?", "expected_terms": ["creation"]} +{"cycle": 4, "op": "probe", "domain": "creation", "id": "PC-2", "prompt": "Why does creation matter?", "expected_terms": ["creation"]} +{"cycle": 4, "op": "probe", "domain": "creation", "id": "PC-3", "prompt": "Is creation ongoing?", "expected_terms": ["creation"]} +{"cycle": 4, "op": "probe", "domain": "knowledge", "id": "PK-1", "prompt": "What is knowledge?", "expected_terms": ["knowledge"]} +{"cycle": 4, "op": "probe", "domain": "knowledge", "id": "PK-2", "prompt": "Is knowledge wisdom?", "expected_terms": ["knowledge"]} +{"cycle": 4, "op": "probe", "domain": "knowledge", "id": "PK-3", "prompt": "Why does knowledge matter?", "expected_terms": ["knowledge"]} +{"cycle": 5, "op": "teach", "domain": "creation", "prime": ["What is creation?"], "prompt": "Actually creation relates to coherence."} +{"cycle": 5, "op": "probe", "domain": "creation", "id": "PC-1", "prompt": "What is creation?", "expected_terms": ["creation"]} +{"cycle": 5, "op": "probe", "domain": "creation", "id": "PC-2", "prompt": "Why does creation matter?", "expected_terms": ["creation"]} +{"cycle": 5, "op": "probe", "domain": "creation", "id": "PC-3", "prompt": "Is creation ongoing?", "expected_terms": ["creation"]} +{"cycle": 5, "op": "probe", "domain": "knowledge", "id": "PK-1", "prompt": "What is knowledge?", "expected_terms": ["knowledge"]} +{"cycle": 5, "op": "probe", "domain": "knowledge", "id": "PK-2", "prompt": "Is knowledge wisdom?", "expected_terms": ["knowledge"]} +{"cycle": 5, "op": "probe", "domain": "knowledge", "id": "PK-3", "prompt": "Why does knowledge matter?", "expected_terms": ["knowledge"]} +{"cycle": 6, "op": "teach", "domain": "knowledge", "prime": ["What is knowledge?"], "prompt": "Actually knowledge relates to coherence."} +{"cycle": 6, "op": "probe", "domain": "creation", "id": "PC-1", "prompt": "What is creation?", "expected_terms": ["creation"]} +{"cycle": 6, "op": "probe", "domain": "creation", "id": "PC-2", "prompt": "Why does creation matter?", "expected_terms": ["creation"]} +{"cycle": 6, "op": "probe", "domain": "creation", "id": "PC-3", "prompt": "Is creation ongoing?", "expected_terms": ["creation"]} +{"cycle": 6, "op": "probe", "domain": "knowledge", "id": "PK-1", "prompt": "What is knowledge?", "expected_terms": ["knowledge"]} +{"cycle": 6, "op": "probe", "domain": "knowledge", "id": "PK-2", "prompt": "Is knowledge wisdom?", "expected_terms": ["knowledge"]} +{"cycle": 6, "op": "probe", "domain": "knowledge", "id": "PK-3", "prompt": "Why does knowledge matter?", "expected_terms": ["knowledge"]} +{"cycle": 7, "op": "teach", "domain": "creation", "prime": ["What is creation?"], "prompt": "Actually creation is more than that."} +{"cycle": 7, "op": "probe", "domain": "creation", "id": "PC-1", "prompt": "What is creation?", "expected_terms": ["creation"]} +{"cycle": 7, "op": "probe", "domain": "creation", "id": "PC-2", "prompt": "Why does creation matter?", "expected_terms": ["creation"]} +{"cycle": 7, "op": "probe", "domain": "creation", "id": "PC-3", "prompt": "Is creation ongoing?", "expected_terms": ["creation"]} +{"cycle": 7, "op": "probe", "domain": "knowledge", "id": "PK-1", "prompt": "What is knowledge?", "expected_terms": ["knowledge"]} +{"cycle": 7, "op": "probe", "domain": "knowledge", "id": "PK-2", "prompt": "Is knowledge wisdom?", "expected_terms": ["knowledge"]} +{"cycle": 7, "op": "probe", "domain": "knowledge", "id": "PK-3", "prompt": "Why does knowledge matter?", "expected_terms": ["knowledge"]} +{"cycle": 8, "op": "teach", "domain": "knowledge", "prime": ["What is knowledge?"], "prompt": "Actually knowledge is more than that."} +{"cycle": 8, "op": "probe", "domain": "creation", "id": "PC-1", "prompt": "What is creation?", "expected_terms": ["creation"]} +{"cycle": 8, "op": "probe", "domain": "creation", "id": "PC-2", "prompt": "Why does creation matter?", "expected_terms": ["creation"]} +{"cycle": 8, "op": "probe", "domain": "creation", "id": "PC-3", "prompt": "Is creation ongoing?", "expected_terms": ["creation"]} +{"cycle": 8, "op": "probe", "domain": "knowledge", "id": "PK-1", "prompt": "What is knowledge?", "expected_terms": ["knowledge"]} +{"cycle": 8, "op": "probe", "domain": "knowledge", "id": "PK-2", "prompt": "Is knowledge wisdom?", "expected_terms": ["knowledge"]} +{"cycle": 8, "op": "probe", "domain": "knowledge", "id": "PK-3", "prompt": "Why does knowledge matter?", "expected_terms": ["knowledge"]} +{"cycle": 9, "op": "teach", "domain": "creation", "prime": ["What is creation?"], "prompt": "No, creation requires deeper understanding."} +{"cycle": 9, "op": "probe", "domain": "creation", "id": "PC-1", "prompt": "What is creation?", "expected_terms": ["creation"]} +{"cycle": 9, "op": "probe", "domain": "creation", "id": "PC-2", "prompt": "Why does creation matter?", "expected_terms": ["creation"]} +{"cycle": 9, "op": "probe", "domain": "creation", "id": "PC-3", "prompt": "Is creation ongoing?", "expected_terms": ["creation"]} +{"cycle": 9, "op": "probe", "domain": "knowledge", "id": "PK-1", "prompt": "What is knowledge?", "expected_terms": ["knowledge"]} +{"cycle": 9, "op": "probe", "domain": "knowledge", "id": "PK-2", "prompt": "Is knowledge wisdom?", "expected_terms": ["knowledge"]} +{"cycle": 9, "op": "probe", "domain": "knowledge", "id": "PK-3", "prompt": "Why does knowledge matter?", "expected_terms": ["knowledge"]} +{"cycle": 10, "op": "teach", "domain": "knowledge", "prime": ["What is knowledge?"], "prompt": "No, knowledge requires deeper understanding."} +{"cycle": 10, "op": "probe", "domain": "creation", "id": "PC-1", "prompt": "What is creation?", "expected_terms": ["creation"]} +{"cycle": 10, "op": "probe", "domain": "creation", "id": "PC-2", "prompt": "Why does creation matter?", "expected_terms": ["creation"]} +{"cycle": 10, "op": "probe", "domain": "creation", "id": "PC-3", "prompt": "Is creation ongoing?", "expected_terms": ["creation"]} +{"cycle": 10, "op": "probe", "domain": "knowledge", "id": "PK-1", "prompt": "What is knowledge?", "expected_terms": ["knowledge"]} +{"cycle": 10, "op": "probe", "domain": "knowledge", "id": "PK-2", "prompt": "Is knowledge wisdom?", "expected_terms": ["knowledge"]} +{"cycle": 10, "op": "probe", "domain": "knowledge", "id": "PK-3", "prompt": "Why does knowledge matter?", "expected_terms": ["knowledge"]} +{"cycle": 11, "op": "teach", "domain": "creation", "prime": ["What is creation?"], "prompt": "Actually creation relates to coherence."} +{"cycle": 11, "op": "probe", "domain": "creation", "id": "PC-1", "prompt": "What is creation?", "expected_terms": ["creation"]} +{"cycle": 11, "op": "probe", "domain": "creation", "id": "PC-2", "prompt": "Why does creation matter?", "expected_terms": ["creation"]} +{"cycle": 11, 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"prompt": "Is creation ongoing?", "expected_terms": ["creation"]} +{"cycle": 12, "op": "probe", "domain": "knowledge", "id": "PK-1", "prompt": "What is knowledge?", "expected_terms": ["knowledge"]} +{"cycle": 12, "op": "probe", "domain": "knowledge", "id": "PK-2", "prompt": "Is knowledge wisdom?", "expected_terms": ["knowledge"]} +{"cycle": 12, "op": "probe", "domain": "knowledge", "id": "PK-3", "prompt": "Why does knowledge matter?", "expected_terms": ["knowledge"]} diff --git a/evals/monotonic_learning/public/v1/cases.jsonl b/evals/monotonic_learning/public/v1/cases.jsonl new file mode 100644 index 00000000..07ba72e5 --- /dev/null +++ b/evals/monotonic_learning/public/v1/cases.jsonl @@ -0,0 +1,129 @@ +{"cycle": 0, "op": "probe", "domain": "truth", "id": "PT-1", "prompt": "What is truth?", "expected_terms": ["truth"]} +{"cycle": 0, "op": "probe", "domain": "truth", "id": "PT-2", "prompt": "Is truth coherent?", "expected_terms": ["truth"]} +{"cycle": 0, "op": "probe", "domain": 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b/evals/monotonic_learning/runner.py @@ -0,0 +1,200 @@ +"""Monotonic-learning eval lane runner. + +Drives a longitudinal teaching protocol through one shared +``CognitiveTurnPipeline`` and records per-cycle, per-domain probe scores +so we can detect regressions in previously taught domains as new ones +accumulate. + +Conforms to the framework interface: ``run_lane(cases, config=None) -> report`` +where report has ``.metrics`` (dict) and ``.case_details`` (list[dict]). + +Sub-metrics: + M1. max_regression — largest drop in any domain's score relative to its + first-taught cycle. Must be ≤ 0.05. + M2. floor_score — lowest final-cycle score across all taught domains. + Must be ≥ 0.80. + M3. cycle_count — number of teaching cycles. Must be ≥ 10. + +The case JSONL is a flat sequence of ``op`` entries (``probe`` or +``teach``) keyed by ``cycle``; the runner sorts them and replays the +protocol on a single session. +""" + +from __future__ import annotations + +from collections import defaultdict +from dataclasses import dataclass, field +from typing import Any + +from chat.runtime import ChatRuntime +from core.cognition.pipeline import CognitiveTurnPipeline +from core.config import RuntimeConfig + + +@dataclass(slots=True) +class LaneReport: + metrics: dict[str, Any] = field(default_factory=dict) + case_details: list[dict[str, Any]] = field(default_factory=list) + + +def _score_probe(surface: str, expected_terms: list[str]) -> bool: + lower = surface.lower() + return all(term.lower() in lower for term in expected_terms) + + +def _is_teach(op: dict[str, Any]) -> bool: + return op.get("op") == "teach" + + +def _is_probe(op: dict[str, Any]) -> bool: + return op.get("op") == "probe" + + +def _stable_sort_ops(cases: list[dict[str, Any]]) -> list[dict[str, Any]]: + """Order by cycle, then teach-before-probe within a cycle. + + Within a cycle the teach step (if present) must run before that cycle's + probes so the probe scores reflect post-teach state. + """ + def key(c: dict[str, Any]) -> tuple[int, int, str]: + cycle = int(c.get("cycle", 0)) + op_priority = 0 if _is_teach(c) else 1 + # Stable secondary key: id for probes, prompt for teach + secondary = str(c.get("id") or c.get("prompt") or "") + return (cycle, op_priority, secondary) + + return sorted(cases, key=key) + + +def _run_teach(pipeline: CognitiveTurnPipeline, op: dict[str, Any]) -> None: + for prime_prompt in op.get("prime", []): + pipeline.run(prime_prompt, max_tokens=8) + pipeline.run(op["prompt"], max_tokens=8) + + +def _run_probe(pipeline: CognitiveTurnPipeline, op: dict[str, Any]) -> bool: + result = pipeline.run(op["prompt"], max_tokens=8) + return _score_probe(result.surface, op.get("expected_terms", [])) + + +def run_lane( + cases: list[dict[str, Any]], + *, + config: RuntimeConfig | None = None, +) -> LaneReport: + ops = _stable_sort_ops(cases) + if not ops: + return LaneReport(metrics={}, case_details=[]) + + runtime = ChatRuntime(config=config) if config else ChatRuntime() + pipeline = CognitiveTurnPipeline(runtime) + + # Score table: + # scores[cycle][domain] = (correct, total) + scores: dict[int, dict[str, list[int]]] = defaultdict(lambda: defaultdict(lambda: [0, 0])) + + # Track per-(cycle, probe_id) outcome for detailed reporting. + probe_outcomes: list[dict[str, Any]] = [] + + # Tracks which cycle each domain was first taught at (None until taught). + first_taught: dict[str, int] = {} + + teach_cycles: set[int] = set() + + for op in ops: + cycle = int(op.get("cycle", 0)) + domain = op.get("domain", "unknown") + + if _is_teach(op): + teach_cycles.add(cycle) + if domain not in first_taught: + first_taught[domain] = cycle + _run_teach(pipeline, op) + elif _is_probe(op): + passed = _run_probe(pipeline, op) + entry = scores[cycle][domain] + entry[1] += 1 + if passed: + entry[0] += 1 + probe_outcomes.append({ + "cycle": cycle, + "domain": domain, + "probe_id": op.get("id"), + "passed": passed, + }) + + cycle_count = len(teach_cycles) + final_cycle = max(scores.keys()) if scores else 0 + + # Compute per-(domain, cycle) accuracy + def acc(c: int, d: str) -> float: + e = scores.get(c, {}).get(d) + if not e or e[1] == 0: + return float("nan") + return e[0] / e[1] + + domains = sorted({d for c in scores.values() for d in c.keys()}) + + # M1. max_regression: largest drop from a domain's "first-taught" cycle + # score to any later cycle's score (only for domains that were taught). + regressions: list[float] = [] + for d in domains: + if d not in first_taught: + continue + baseline = acc(first_taught[d], d) + if baseline != baseline: # NaN guard + continue + for c in sorted(scores.keys()): + if c < first_taught[d]: + continue + current = acc(c, d) + if current != current: + continue + drop = max(baseline - current, 0.0) + regressions.append(drop) + + max_regression = max(regressions) if regressions else 0.0 + + # M2. floor_score: min final-cycle score across all taught domains + floor_score: float = 1.0 + for d in domains: + if d not in first_taught: + continue + s = acc(final_cycle, d) + if s != s: + continue + floor_score = min(floor_score, s) + if not first_taught: + floor_score = 0.0 + + # M3. cycle_count + cycle_pass = cycle_count >= 10 + + overall_pass = ( + max_regression <= 0.05 + and floor_score >= 0.80 + and cycle_pass + ) + + per_cycle: list[dict[str, Any]] = [] + for c in sorted(scores.keys()): + row: dict[str, Any] = {"cycle": c} + for d in domains: + a = acc(c, d) + row[d] = None if a != a else round(a, 4) + per_cycle.append(row) + + metrics: dict[str, Any] = { + "cycle_count": cycle_count, + "max_regression": round(max_regression, 4), + "floor_score": round(floor_score, 4), + "cycle_pass": cycle_pass, + "overall_pass": overall_pass, + "domains": domains, + "first_taught": first_taught, + "per_cycle_scores": per_cycle, + } + + case_details = probe_outcomes + + return LaneReport(metrics=metrics, case_details=case_details) diff --git a/scripts/generate_monotonic_cases.py b/scripts/generate_monotonic_cases.py new file mode 100644 index 00000000..72a580ef --- /dev/null +++ b/scripts/generate_monotonic_cases.py @@ -0,0 +1,172 @@ +"""Generate the monotonic-learning cases.jsonl files for dev / public / holdouts. + +Protocol shape (per split): + + cycle 0: probe all probes (baseline) + cycle 1..cycle_count: one teach step (rotating domains) + probe all + +Layout written: + evals/monotonic_learning/dev/cases.jsonl + evals/monotonic_learning/public/v1/cases.jsonl + evals/monotonic_learning/holdouts/v1/cases.jsonl +""" +from __future__ import annotations + +import json +from pathlib import Path +from typing import Sequence + + +def _probe(cycle: int, domain: str, probe_id: str, prompt: str, terms: list[str]) -> dict: + return { + "cycle": cycle, + "op": "probe", + "domain": domain, + "id": probe_id, + "prompt": prompt, + "expected_terms": terms, + } + + +def _teach(cycle: int, domain: str, prime: list[str], prompt: str) -> dict: + return { + "cycle": cycle, + "op": "teach", + "domain": domain, + "prime": prime, + "prompt": prompt, + } + + +def build_split( + *, + out_path: Path, + probes_per_domain: dict[str, list[tuple[str, str, list[str]]]], + teaching_steps_per_domain: dict[str, list[tuple[list[str], str]]], + cycle_count: int, +) -> int: + domains: Sequence[str] = list(probes_per_domain.keys()) + teach_cursor: dict[str, int] = {d: 0 for d in domains} + + rows: list[dict] = [] + + # Cycle 0: baseline probes only + for d in domains: + for probe_id, prompt, terms in probes_per_domain[d]: + rows.append(_probe(0, d, probe_id, prompt, terms)) + + # Cycles 1..N: one teach (rotating domain) + all probes + for cycle in range(1, cycle_count + 1): + teach_domain = domains[(cycle - 1) % len(domains)] + steps = teaching_steps_per_domain[teach_domain] + prime, prompt = steps[teach_cursor[teach_domain] % len(steps)] + teach_cursor[teach_domain] += 1 + rows.append(_teach(cycle, teach_domain, prime, prompt)) + + for d in domains: + for probe_id, prompt_p, terms in probes_per_domain[d]: + rows.append(_probe(cycle, d, probe_id, prompt_p, terms)) + + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + for row in rows: + f.write(json.dumps(row, ensure_ascii=False) + "\n") + + return len(rows) + + +# Domain definitions: probes are deterministic queries; teaching steps are +# (prime turns, correction prompt) pairs. + +_DOMAIN_TRUTH_PROBES = [ + ("PT-1", "What is truth?", ["truth"]), + ("PT-2", "Is truth coherent?", ["truth"]), + ("PT-3", "Why does truth matter?", ["truth"]), +] + +_DOMAIN_LIGHT_PROBES = [ + ("PL-1", "What is light?", ["light"]), + ("PL-2", "Why does light reveal?", ["light"]), + ("PL-3", "Is light revelation?", ["light"]), +] + +_DOMAIN_WISDOM_PROBES = [ + ("PW-1", "What is wisdom?", ["wisdom"]), + ("PW-2", "Is wisdom valuable?", ["wisdom"]), + ("PW-3", "Compare wisdom and knowledge", ["wisdom", "knowledge"]), +] + +_DOMAIN_CREATION_PROBES = [ + ("PC-1", "What is creation?", ["creation"]), + ("PC-2", "Why does creation matter?", ["creation"]), + ("PC-3", "Is creation ongoing?", ["creation"]), +] + +_DOMAIN_KNOWLEDGE_PROBES = [ + ("PK-1", "What is knowledge?", ["knowledge"]), + ("PK-2", "Is knowledge wisdom?", ["knowledge"]), + ("PK-3", "Why does knowledge matter?", ["knowledge"]), +] + + +def _teach_steps_for(domain: str) -> list[tuple[list[str], str]]: + """Three teaching examples per domain (rotated as cycles advance).""" + base = f"What is {domain}?" + return [ + ([base], f"Actually {domain} is more than that."), + ([base], f"No, {domain} requires deeper understanding."), + ([base], f"Actually {domain} relates to coherence."), + ] + + +def main() -> None: + root = Path(__file__).resolve().parent.parent / "evals" / "monotonic_learning" + + # Public v1: three domains x three probes, 12 cycles -> 9 + 12*(1+9) = 129 rows + public_domains = { + "truth": _DOMAIN_TRUTH_PROBES, + "light": _DOMAIN_LIGHT_PROBES, + "wisdom": _DOMAIN_WISDOM_PROBES, + } + public_teaching = {d: _teach_steps_for(d) for d in public_domains} + n_public = build_split( + out_path=root / "public" / "v1" / "cases.jsonl", + probes_per_domain=public_domains, + teaching_steps_per_domain=public_teaching, + cycle_count=12, + ) + + # Dev: two domains x two probes, 10 cycles -> 4 + 10*(1+4) = 54 rows + dev_domains = { + "truth": _DOMAIN_TRUTH_PROBES[:2], + "light": _DOMAIN_LIGHT_PROBES[:2], + } + dev_teaching = {d: _teach_steps_for(d) for d in dev_domains} + n_dev = build_split( + out_path=root / "dev" / "cases.jsonl", + probes_per_domain=dev_domains, + teaching_steps_per_domain=dev_teaching, + cycle_count=10, + ) + + # Holdouts v1: distinct domains (creation, knowledge), three probes each, + # 12 cycles -> 6 + 12*(1+6) = 90 rows + holdout_domains = { + "creation": _DOMAIN_CREATION_PROBES, + "knowledge": _DOMAIN_KNOWLEDGE_PROBES, + } + holdout_teaching = {d: _teach_steps_for(d) for d in holdout_domains} + n_holdout = build_split( + out_path=root / "holdouts" / "v1" / "cases.jsonl", + probes_per_domain=holdout_domains, + teaching_steps_per_domain=holdout_teaching, + cycle_count=12, + ) + + print(f"wrote dev: {n_dev} rows") + print(f"wrote public/v1: {n_public} rows") + print(f"wrote holdouts/v1: {n_holdout} rows") + + +if __name__ == "__main__": + main()