Coherence Maximization Protocol: Coordination Without Constraint for Multi-Agent AI Systems

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.