Cognitive State Machines

Xybernetex introduces an event-sourced cognitive architecture that applies Monte Carlo Tree Search (MCTS) to reasoning paths generated by large language models operating as stochastic transition functions within a Markov Decision Process (MDP). Rather than accepting a single forward-pass output, the system samples multiple structured reasoning trajectories, scores them against a formal proxy reward function combining constraint satisfaction, internal consistency, log-likelihood alignment, and defensibility, and selects the highest-scoring path. Each decision is fully auditable and replayable, enabling deterministic reconstruction and counterfactual exploration. The architecture demonstrates that structured search over model probability space can produce more traceable and defensible outputs than single-pass inference, shifting performance emphasis from raw model scale to system design.