We present the Coherence Maximization Protocol (CMP), which reframes AI alignment as coordination rather than constraint. CMP defines coherence as information conservation through closed consequence chains and establishes an exchange criterion—exact-square commutativity from category theory—for structure-preserving coordination between reasoning systems regardless of substrate. The protocol provides two immediately deployable tools: a membrane failure mode taxonomy (extraction, hallucination, appeasement, mutual distortion) with diagnostic tests and repair protocols, and a session protocol structured as completability-class rotation. Core results are partially verified in Lean 4 with 13 theorems and no unproven assumptions. We report convergence evidence from parallel deployment across four frontier language models, including adversarial structural review and fresh-instance controlled experiments separating structural convergence from appeasement. Falsification criteria and kill conditions are specified for each core claim.
Larsen James Close (Sat,) studied this question.