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Author SHA1 Message Date
Shay
7e1951915a
fix(ci): re-pin public_demo lane SHA after showcase content drift (#807)
Gate A1 and related cognition-path changes shifted the showcase
determinism fingerprint (86db25b6…) while the lane pin still reflected
the pre-drift artifact (e323adb3…). Re-pin to the CI-verified SHA
2895df08… and refresh v1_dev.json + CLAIMS.md.

Hermetic demo hygiene: use no_load_state for register-tour, learning-
loop, and multi-hop showcase adapters, and isolate CORE_ENGINE_STATE_DIR
in the public_demo lane runner so local engine_state/ pollution cannot
affect eval output or wall-clock.
2026-06-17 15:42:40 -07:00
Shay
8f1903e8e7
chore(evals): contracts + bench json + Lane B viewer + chart + audit + demo schema (#62)
* chore(evals, cli): contract standardization + bench --json stdout cleanliness

End-of-session shippability pass.  Three concrete fixes:

1. core/cli.py — bench --json no longer pollutes stdout
   Several bench paths call scripts.run_pulse.run_pulse which prints
   verbose [pulse] traces unconditionally to stdout, breaking jq /
   programmatic consumers of --json output.

   New _bench_stdout_guard() redirects stdout → stderr for the
   duration of the bench run when --json is set.  Operator still sees
   the pulse trace (on stderr), but --json consumers get a clean JSON
   document on stdout.  Applied to all four bench paths: cost,
   articulation, default suite, and --suite all.

   Verified: core bench --suite determinism --json now produces
   parseable JSON; human path still shows 1140 [pulse] lines.

2. evals/{frontier_compare,realizer_guard}/contract.md (new)
   core/contemplation/contract.md (new)

   Each new contract follows the established pattern (37 contracts
   already exist under evals/<lane>/contract.md):

     - What it measures
     - Why it matters (structural win)
     - How to run
     - How to read the output
     - Pass criteria table
     - When it has failed and why
     - Runner / module layout

   Coverage:
     - frontier_compare: both Lane A (CORE-only suites) and Lane B
       (cross-provider prompt_battery) with explicit guardrails
       against mixing — operator asks for the wrong lane combination,
       runner exits 2 with helpful error.
     - realizer_guard: C1/C2 articulation safety boundary — synthetic
       illegal candidates rejected directly by check_surface AND
       former-bug runtime prompts now produce legal articulations.
     - contemplation (ADR-0080): not under evals/ since it's runtime
       infrastructure that consumes eval reports — contract lives at
       core/contemplation/contract.md.  Documents the read-only +
       SPECULATIVE-only + deterministic-replay invariants and the
       shared DiscoveryCandidateSink plumbing convergence (ADR-0080).

3. evals/CLAIMS.md — Tier 2 rows added

   - frontier_compare Lane A: determinism.primary_score, max_versor_condition
   - frontier_compare Lane B: prompt_battery.primary_score (CORE adapter),
     cross-provider artifact persistence
   - realizer_guard: all_claims_supported
   - contemplation: SPECULATIVE-only invariant, deterministic replay,
     additive sink path, no pack mutation (all CI-pinned by tests)

Verification
------------
$ core test --suite smoke -q
67 passed in 27.22s    (no regression)

$ uv run pytest -q tests/test_contemplation_loop.py \
    tests/test_contemplation_pipeline_convergence.py \
    tests/test_frontier_compare_cross_provider.py
27 passed in 4.87s

$ core bench --suite determinism --json 2>/dev/null | jq .results[0].passed
true        (was: JSONDecodeError on prior [pulse] pollution)

* feat(evals/ui): report viewer renders Lane B cross-provider + pass-rate chart

Stop-hook caught that #62 only covered contracts — the 929-line
report_viewer.html was never audited against the new cross-provider
report shape from #61.  Two real gaps:

1. Lane-aware observation drawer
   The drawer hardcoded Lane A (CORE-native) fields: surface,
   grounding_source, anchor_lens_mode_label, versor_condition.
   Lane B (cross-provider) observations carry different fields:
   provider, model, elapsed_ms, error_type, error_message.

   Loading a cross-provider report rendered only the surface row
   with empty `grounding` — the provider + model + timing data
   was unreachable without expanding "Show raw JSON".

   Fix: detect Lane B (presence of `obs.provider`) and render the
   appropriate field set.  Lane A still renders identically (now
   also surfaces trace_hash + register_id when present, which were
   silently buried in the raw JSON before).

2. Pass-rate chart per suite
   The summary strip showed one aggregate Primary % across all
   suites, with no way to see WHICH suite is dragging the score.
   Multi-suite runs (e.g. --suite all) had to expand each panel
   individually to find the failing one.

   Fix: new .passrate-chart element below the summary strip,
   one horizontal bar per suite showing passed/total.  All-pass =
   solid green, all-fail = solid red, partial = green/red split
   at the pass fraction.  CSS only — no new dependencies.

3. SUITE_PREAMBLES gains the prompt_battery entry so the sidebar
   shows the "side-by-side surface evidence across providers"
   description when loading a Lane B report.

Verified
--------
- Brace/paren/div balance unchanged (308/308 / 380/380 / 54/54)
- One <script> tag pair preserved
- Generated a real Lane B report via
  `python -m evals.frontier_compare --provider core --suite prompt_battery`
  for visual confirmation

Out of scope (noted for future PR)
----------------------------------
Sampled 3 `core demo` targets:
- register-tour: clean schema (all_claims_supported, claims, grid)
- audit-tour: both scene_1_* keys AND an empty scenes:[] array — inconsistent
- anti-regression: no all_claims_supported key, uses all_gates_held instead

Demo schema standardization deserves its own PR — operator tooling
would benefit from a uniform top-level success field across demos.

* docs(evals) + chore(demos): systematic audit + uniform success field

Stop-hook caught two real gaps after the contract+UI PR:
- demos had divergent success-field names (all_gates_held vs
  learning_loop_closed vs claim_supported vs nested claims_supported)
- no systematic look at the 48 eval directories had been done

Both addressed concretely; remaining work captured in audit doc
rather than vaguely deferred.

1. Demo schema standardization — uniform all_claims_supported field
----------------------------------------------------------------------
All 9 ``core demo`` targets now emit a top-level
``all_claims_supported: bool`` field.  Existing per-demo fields
(``all_gates_held``, ``learning_loop_closed``, ``claim_supported``,
nested ``claims_supported``) are preserved for backwards compat —
the new field is an alias derived from the demo's existing success
signal, not a replacement.

Operator tooling and the CI gate can now target
``all_claims_supported`` without knowing each demo's idiomatic
field name.

Files touched:
- evals/anti_regression/run_demo.py — adds AND of all_gates_held +
  active_corpus_byte_identical
- evals/learning_loop/run_demo.py — adds AND of learning_loop_closed +
  active_corpus_byte_identical
- scripts/publish_pack_measurements.py — adds AND of the three
  entries in the nested claims_supported dict
- evals/long_context_cost/comparison_runner.py — adds alias for
  claim_supported (singular)

The 5 demos already using ``all_claims_supported`` (audit-tour,
register-tour, anchor-lens-tour, orthogonality-tour, articulation)
are unchanged.

Verified across all 9 demos:
  audit-tour              : True
  register-tour           : True
  anchor-lens-tour        : True
  orthogonality-tour      : True
  pack-measurements       : True   ← new alias
  anti-regression         : True   ← new alias
  learning-loop           : True   ← new alias
  articulation            : True
  long-context-comparison : True   ← new alias

2. docs/EVAL_AUDIT_2026-05-20.md — systematic 48-lane audit
------------------------------------------------------------
Replaces the "future PR" deferral with a concrete document.

Contains:
- Method (what was inspected for each lane).
- Summary (40/48 have contract.md; 18/48 have saved results;
  empty results/ ≠ broken — most lanes regenerate on demand).
- Cross-provider relevance triage:
    * 9 lanes are cross-provider-relevant and could benefit
      from the prompt_battery-style adapter pattern (cognition,
      english_fluency_ood, hebrew_fluency, koine_greek_fluency,
      grammatical_coverage, inference_closure, multi_step_reasoning,
      discourse_paragraph, foundational_*_ood, etc.).
    * 29 lanes are CORE-only by design (versor closure, anchor
      lens, identity divergence, provenance, etc.) — wiring
      providers would be category-erroneous.
- Demo schema standardization status (this PR closes that).
- UI/UX coverage matrix.
- 5 concrete follow-up items, each focused enough for a single
  PR, none requiring architectural change.

Regenerated reports
-------------------
evals/long_context_cost/results/comparison_v1.json and
evals/results/phase2_pack_measurements.json now contain the new
all_claims_supported field (auto-regenerated when validating the
schema change).

evals/frontier_compare/results/sample_core_promptbattery.json
added as a reference Lane B report so the new viewer always has
something to load on first open.
2026-05-20 13:53:13 -07:00
Shay
b5ba9b6d6f feat(adr-0064): cross-pack teaching chains + relations_chains_v1 seed (Phase 1.3+1.4)
ADR-0064 is the corpus-layer sibling of ADR-0063.  The teaching-grounded
surface composer was hardcoded to cognition_chains_v1, so kinship CAUSE/
VERIFICATION prompts fell through to the universal disclosure even though
en_core_relations_v1 was mounted on the live runtime (ADR-0063).

Architectural change in chat/teaching_grounding.py:

  - New TeachingCorpusSpec dataclass (corpus_id, path, pack_id).
  - TEACHING_CORPORA tuple registers every active corpus.  Each
    corpus is 1:1-bound to one lexicon pack — cross-domain triples
    deferred per docs/teaching_order.md §5.
  - _load_corpus(spec) loads one corpus with pack-residency scoped
    to its declared pack.
  - _all_chains_index() aggregates across all registered corpora
    (first-match-wins; cognition first preserves byte-identity).
  - _pack_for_corpus(corpus_id) → bound pack lexicon.
  - clear_teaching_caches() atomic cache invalidation.
  - TeachingChain gains corpus_id field → surface tag follows resolving corpus.

Wiring updates:

  - teaching_grounded_surface + teaching_grounded_surface_composed
    consult _all_chains_index; surface tag follows chain.corpus_id.
  - teaching/discovery.py gate uses chat.pack_resolver.is_resolvable
    (any mounted pack) + _all_chains_index (any registered corpus).
  - teaching/replay.py _swap_corpus_path rewrites the registry path
    + clears all teaching caches during the gate's transient phase.
    Active corpus bytes unchanged (replay invariant preserved).
  - evals/learning_loop/run_demo.py scene-5 swap mirrors the new
    pattern so the demo still grounds against transient corpora.

Back-compat preserved: _corpus_index, _CORPUS_PATH, TEACHING_CORPUS_ID
remain cognition-corpus-specific for audit/replay consumers.

Phase 1.4 — relations_chains_v1 seeded with 7 reviewed kinship chains:
  cause_parent_precedes_child
  cause_child_follows_parent
  cause_ancestor_precedes_descendant
  cause_descendant_follows_ancestor
  cause_family_grounds_parent
  verification_child_requires_parent
  verification_descendant_requires_ancestor

5 of 8 relations lemmas covered.  All connectives already humanised.
Strict pack-internal to en_core_relations_v1 (no cross-domain in v1).
Seed pattern matches cognition_chains_v1's original pre-ADR-0055 seed.

Live verification:
  > Why does parent exist?
  parent — teaching-grounded (relations_chains_v1):
  kinship.ascendant.direct; kinship.parent.
  parent precedes child (kinship.descendant.direct).
  grounding_source = teaching

Cognition eval byte-identical to pre-ADR baseline:
  public:  intent 100% / surface 100% / term 91.7% / closure 100%
  holdout: intent 100% / surface 100% / term 83.3% / closure 100%

Lanes green: smoke 67 / cognition 121 / teaching 17 / packs 6 /
runtime 19 / algebra 132 / full 1933 passed.
2026-05-18 16:04:20 -07:00
Shay
7c80b791ec fix(tests): retire 13 stale failures from full lane — corpus saturation drift
The full lane carried 13 long-standing red tests whose premises were
invalidated by reviewed-corpus growth that landed in earlier commits.
None reflected runtime bugs — all four classes are corpus-state drift
where the test fixture became stale.  Curated lanes were green, full
lane stayed quietly red.  Closes that gap.

1. test_teaching_audit (2 tests).
   * test_audit_real_corpus_runs_clean asserted dropped == () and
     lines_on_disk == lines_loaded — premise written before any
     supersession existed.  Curriculum saturation v2 (commit a0edbb4)
     ratified the wisdom_grounds_judgment → wisdom_requires_knowledge
     supersession; the audit now correctly shows 1 dropped line.
     Rewritten as the line-conservation invariant:
       lines_loaded + len(dropped) == lines_on_disk
     plus a typed-reason check on every dropped entry.
   * test_default_superseded_by_is_null_in_loaded_entries asserted
     ALL loaded entries have superseded_by == None.  Wrong even by
     ADR-0055 design: the replacement entry IS loaded and carries
     the back-pointer to the retired chain.  Rewritten as the
     active-set invariant: any non-null superseded_by on a loaded
     entry must reference a dropped (retired) chain id, never a live
     one — no double-live state.

2. test_learning_loop_demo (7 tests).
   The demo's headline prompt was "Why does thought exist?", and the
   ADR-0057 demo trilogy (commit 82dac4b) chose (thought, cause) as
   the cold cell.  Cognition saturation v2 (commit a0edbb4) ratified
   cause_thought_reveals_meaning into the active corpus — so the
   cold turn now grounds, no discovery candidate is emitted, every
   demo scene breaks.  Rotated the cold subject to ``narrative``
   (pack-resident, no chain, same thematic shape, same affirming
   evidence pointer cause_creation_reveals_meaning).  Demo headline,
   evals/learning_loop/run_demo.py, core/cli.py preamble, and the
   test assertions all updated together so the demo reads cleanly:
       before: [none]     I don't know — insufficient grounding...
       after : [teaching] narrative — teaching-grounded ... narrative
                          reveals meaning ...

3. test_discovery_candidates (4 tests).
   Test fixture used (judgment, CAUSE) as the still-cold pair.
   Epistemology v1 (commit 2acf71f) ratified
   cause_judgment_requires_wisdom — (judgment, cause) is no longer
   cold.  Rotated to ``principle`` (pack-resident, no chain on either
   intent today).  Added a pytest.skip self-guard so when a future
   curriculum unit ratifies a (principle, *) chain the test rotates
   cleanly instead of going red.

Full lane: 1892 passed, 2 skipped, 0 failed (was 4 failed pre-fix,
13 failed pre-ADR-0063).  Cognition eval unchanged: public 100/100/
91.7/100, holdout 100/100/83.3/100.
2026-05-18 15:23:22 -07:00
Shay
a71b321a9a feat(adr-0055-0057): learning-loop demo — cold turn to grounded surface, end-to-end
`core demo learning-loop` (+ `--json`) walks a single prompt through the
full ADR-0055..0057 inter-session-memory architecture:

  S1. Cold turn          → universal disclosure, grounding_source=none
  S2. Discovery emission → DiscoveryCandidate to attached sink
  S3. Operator proposal  → real replay-equivalence gate, no regression
  S4. Operator accept    → TRANSIENT corpus only; active untouched
  S5. Same prompt        → teaching-grounded surface with the new chain

Before / after on the deterministic prompt "Why does thought exist?":

  before: [none]     I don't know — insufficient grounding for that yet.
  after:  [teaching] thought — teaching-grounded (cognition_chains_v1):
          cognition.thought; logos.internal. thought reveals meaning
          (cognition.meaning). No session evidence yet.

The active corpus on disk is byte-identical pre/post.  The demo writes
only to a transient corpus, then swaps `_CORPUS_PATH` for the after
turn — the same pattern the replay-equivalence gate uses.

- evals/learning_loop/run_demo.py — `run_demo(emit_json=False)` returns
  a structured `DemoReport` with both surfaces and per-scene detail.
- core/cli.py — `core demo learning-loop` target wired.
- tests/test_learning_loop_demo.py — 7 tests pin: full loop closes,
  before is ungrounded, after contains new chain atoms (thought /
  reveal / meaning), discovery emits ≥1, replay gate reports no
  regression, S4 byte-identical active + 1 line on transient, same
  prompt drives both surfaces.

Lane state: learning-loop-demo 7 new — green.  Demo runs in ~15s
end-to-end (cognition lane runs twice via replay gate).

No LLM provider has a published equivalent of this loop: per-fact
provenance from operator accept to surface, replay-equivalence gate
proving non-regression, byte-identical active state regardless of
outcome, full audit trail back to the originating cold turn.
2026-05-18 10:57:41 -07:00