Parity/coherence failure protocol

The model runs without errors but output is wrong. Scalar F.print taps hide directional bugs, and each new tap costs a full recompile. Build a per-layer tensor-dump comparator first. Every later check becomes a numpy read from disk.

Use this skill when MAX output disagrees with a PyTorch reference you can run and hook. The primary case is a custom-architecture port that serves but fails parity or coherence checks. The same protocol covers a quantized variant of a working port, a multi-GPU conversion of a working single-GPU port, and a regression after a MAX upgrade. It applies anywhere a trusted reference exists.

Don't use this skill when:

• The server crashes on load → fix config, weights, module (import-model)
• You haven't finished implementing the module → import-model Phase 2
• An already-verified model needs logit-comparison tolerances tuned → that's threshold calibration, which this skill doesn't cover