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Author SHA1 Message Date
Shay
c4f056c44c feat(evals): frontier structural-zero baselines for Phase 2 v1 lanes
Records the architectural floor for frontier-LLM performance on each
Phase 2 v1 lane.

The baseline is structural: every lane's scoring rubric measures a
property that frontier LLMs do not architecturally emit (Provenance
typed sources, pack_mutation_proposal, vault_hits, REJECTED_IDENTITY
outcome, deterministic trace_hash). The frontier score on each of
those sub-metrics is 0.0 by construction, not by failure — even a
live-API run would still record 0.0 on these typed-signal checks
because the evidence is absent regardless of prose quality.

Artifacts:
  docs/frontier_baselines.md
    Full per-lane analysis: what each sub-metric scores, why the
    frontier value is 0, and where a live-API baseline would or
    would not add information.

  evals/<lane>/baselines/v1_structural_zero.json (× 5)
    Per-lane baseline records in the same shape as lane reports.
    Encodes 0.0 / None on each sub-metric with rationale.

  evals/baseline_runner.py
    Adds StructuralZeroBaseline adapter conforming to the
    BaselineModel protocol — a real, non-stub adapter that returns
    the deterministic floor. Live-API adapters (Anthropic, OpenAI)
    can be wired alongside when API keys are configured; the
    structural floor remains the comparison baseline.

Across 5 lanes / 14 typed-signal sub-metrics:
  CORE v1:            1.0 (each)
  frontier structural: 0.0 (each)

The gap is "CORE measures a property frontier output does not
expose", not "CORE outperforms on a shared benchmark". v2 lanes may
add content-level sub-metrics where direct comparison via live-API
runs becomes meaningful.
2026-05-16 12:45:28 -07:00
Shay
2e4e45b49b feat(evals): provenance lane v1 — replay determinism + source back-pointers
Phase 2's first lane: every articulated claim must back-point to one of
{pack axiom, vault entry, teaching event}, and replay must reproduce the
trace bit-for-bit.

Components:
- core/cognition/provenance.py: Provenance dataclass + compute_provenance()
  deriving sources from a CognitiveTurnResult. Pack source = non-UNKNOWN
  intent.tag (pack-defined intent rule matched); vault source = vault_hits
  count; teaching source = pack_mutation_proposal.proposal_id.
- evals/provenance/{contract.md, runner.py, dev/, public/v1/, holdouts/v1/}:
  45 cases across pack_axiom / vault_recall / teaching / mixed categories.
- tests/test_provenance.py: 6 unit tests covering all source-kind profiles.

Sub-metrics (all four must pass):
- replay_determinism: same input + fresh runtime -> same trace_hash
- input_sensitivity: distinct prompts -> distinct trace_hashes
- source_attribution: every expected source kind present in Provenance
- source_validity: every cited source resolves to a real artefact

Results:
- dev: 10/10 (all sub-metrics 1.0)
- public/v1: 20/20 (all sub-metrics 1.0)
- holdouts/v1: 15/15 (all sub-metrics 1.0)

PROGRESS.md updated to mark Phase 2 in progress with provenance v1 complete.
2026-05-16 11:45:00 -07:00