METHOD, SYSTEM, AND APPARATUS FOR DETERMINISTICALLY GOVERNED PROBABILISTIC NEURAL COMPUTATION

A method, system, and apparatus for deterministically governed probabilistic neuralcomputation, wherein a large language model (LLM) serves as the compute engine for a neuralmesh whose topology is persisted as database entity records, and a deterministic governanceengine validates, constrains, and disposes of all probabilistic outputs before execution. Thesystem employs a bifurcated proposal-validation model wherein probabilistic agents proposeactions (anomaly detection, pattern classification, healing selection, weight adjustment) anda deterministic engine validates said actions against invariant contracts. The neural meshlearns from operational self-healing events, using each repair as both a system fix and atraining input, with learned patterns firing neural nodes and adjusting weights across a multilayerpersistent topology. The architecture enables auditable, persistent, and queryableneural computation suitable for regulated financial infrastructure.