Completes the Wave M B3.5 consolidation slice (b–e), built on #728.
B3.5-b — calibration as a first-class evidence subject (`calibration_class`,
address `calibration:<class_name>`): RightInspector projection + Evidence
Chain Rail semantics (serving-discipline evidence, not runtime truth).
B3.5-c / B4a — nullable `LeewayEvidence` read model threaded through turn,
replay, cognition-proposal, and math-proposal surfaces, with a shared
absence-honest card. B4 is gated correctly: the tuple exists in typed data but
no producer populates it, so the card renders absence (verified: no non-null
producer in workbench/core/chat).
B3.5-d/e — UI-UX-GUIDE.md, b4-leeway-feasibility-gate.md, phase-a-residue-ledger.md.
Practice artifact — earn-it-for-real (runner-reproducible). The committed
`report.json` (additive earns PROPOSE @0.861, 95/5/50) is now emitted by a
deterministic runner rather than copied from the queue. `propose_runner`
gains `regenerate_practice_artifacts()`, which runs ONE sealed `resolve_pooled`
practice pass and writes BOTH report.json (the per-class ledger the calibration
reader consumes) and ratification_queue.json — two projections of one ledger,
coherent by construction and byte-reproducible. `runner.main()` delegates to
it (lazy import, no cycle), so both entry points produce the identical pair.
This closes the gap where a hand-copied report.json agreed with the queue but
no runner produced it. `resolve_pooled` is the aggressive sealed PROPOSE-regime
scorer (proposal-only/HITL, unsafe for serving, legitimate for
attempt-and-eliminate); wrong=5 is the sealed-practice learning signal, NOT the
serving wrong=0. No serving/derivation/reliability_gate source touched; the
practice lane is not in the serving-frozen SHA gate.
Validated:
- python -m pytest tests/test_workbench_{calibration,journal,replay,schemas}.py -> 31 passed
- python -m pytest tests/ -k "workbench or propose or learning_arena or practice"
-> 190 passed (3 failing tests in test_adr_0175_phase2_practice_lane.py are
PRE-EXISTING reds on clean origin/main: stale 4/0/46 assertions on build_report,
which this change does not touch)
- report.json + ratification_queue.json: deterministic (run1==run2) and
reproduced byte-identically by both `python -m ...runner` and `...propose_runner`
- pnpm build green; 144 UI tests across calibration/leeway/evidence/replay/
doctrine-gates/routes-docs-drift all pass
First Wave M / Phase B piece (GATING): read-only backend that makes the
calibrated-learning / serving-discipline loop inspectable — 'the engine
earns the right to guess', ADR-0175.
The workbench computes NONE of these numbers:
- GET /calibration/classes — per-class gold-tether view from the persisted
practice arena ledger (evals/gsm8k_math/practice/v1/report.json per_class).
Each class's reliability_floor is the engine's own one-sided Wilson
conservative_floor (via ClassTally.reliability); PROPOSE (θ=0.85) / SERVE
(θ=0.99) license verdicts come from core.reliability_gate.license_for.
Failures-first ordering. A test proves the reader's floor equals a direct
conservative_floor() call — no re-implementation.
- GET /serving/metrics — the live correct/refused/wrong counts read unchanged
from the committed train_sample + holdout_dev report.json (currently
4/46/0 and 5/495/0 — wrong=0). Never re-runs a lane.
Honest current state: the committed practice ledger's three classes
(additive/divisive/multiplicative) are all below N_MIN=10, so none has
earned a license yet — the reader shows exactly that, no fake green light.
- workbench/calibration.py: pure readers; imports core.reliability_gate;
EvidenceUnavailableError -> 501 (fail-closed) when the artifact is absent.
- schemas + TS mirrors (CalibrationClass, ServingMetrics); both snapshots
regenerated (deterministic); both drift gates pass.
- trust boundary: read-only over committed artifacts + engine-owned
derivation; no execution, no mutation, no license ever changed.
Verified: 30 Python tests (incl. the no-reimplementation proof + fail-closed),
390 vitest, both schema drift gates, snapshots deterministic.
