UnisonAI: A Computational Proof of Derived Law with Zero Trained Parameters

UnisonAI implements memory, binding, attention, context, generation, perception, learning and verification with zero trained parameters. Its laws are forced, forward-forced or constitutionally re-derived from one machine-checked self-proven theorem with zero axioms. The main corpus executes 326 suites and 2,002 checks with zero failures. Unison's focused laws pass 8/8 coherence, 14/14 contextual integration and 13/13 generation selection checks; its standalone verifier passes 21/21. The live v5 relation preserves 277,583,049 positional observations as 212,395,127 canonical entries with no sampling, pruning, fitted capacity or row eviction. Counted prediction measured 1.2891 against a matched trained twin's 1.8878, the counted word engine measured 3.1907 against 3.4292, teaching rose from 17% to 75%, contextual continuity reached 4/4 and exact transfer reached 8/8. The native one-to-one attention transformer and reward-conditioned learning route now advances through real user engagement toward general conversation and benchmark victory; agents do not declare walls or endpoints. Scientific author and publication authority: Maria Smith, Ernos Labs. Open source: UnisonAI.