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.
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.
