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feat(evals): monotonic-learning lane v1 — no regression across cycles

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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.
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@ -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

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# 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/
```

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{"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"]}

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{"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, "op": "probe", "domain": "creation", "id": "PC-3", "prompt": "Is creation ongoing?", "expected_terms": ["creation"]}
{"cycle": 11, "op": "probe", "domain": "knowledge", "id": "PK-1", "prompt": "What is knowledge?", "expected_terms": ["knowledge"]}
{"cycle": 11, "op": "probe", "domain": "knowledge", "id": "PK-2", "prompt": "Is knowledge wisdom?", "expected_terms": ["knowledge"]}
{"cycle": 11, "op": "probe", "domain": "knowledge", "id": "PK-3", "prompt": "Why does knowledge matter?", "expected_terms": ["knowledge"]}
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"split": "holdouts",
"timestamp": "2026-05-16T18:53:52.305522+00:00",
"version": "v1"
}

807
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@ -0,0 +1,807 @@
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}

200
evals/monotonic_learning/runner.py Normal file
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"""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)

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scripts/generate_monotonic_cases.py Normal file
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"""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()