Wave M takes the workbench to mastery and closes its biggest design gap:
it surfaces the teaching/ratification loop but is blind to the
calibrated-learning / serving-discipline loop (gold-tether arena, reliability
gate, Wilson floor vs θ ceiling, 'the engine earns the right to guess') and
to cognition itself (pipeline stages, field substrate, identity continuity).
Lens: Anthropic + xAI as target users who'd WANT to use it.
- wave-M-worthiness.md: full plan, Phases A–E, the missing-surfaces table,
the backend-reader-first / never-re-implement-engine-math disciplines,
execution order (B→C→D, A parallel).
- wave-M-phaseB-calibration-briefs: executable Phase B pack grounded in the
real core/reliability_gate shapes (ClassTally / conservative_floor /
license_for / Action θ) and the committed report.json evidence — B1
readers (GATING, Python), B2 Calibration route, B3 wrong=0 global frame,
B4 leeway wiring. Dependency DAG + STOP gates + no-theater rules.
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