Hybrid Functor Model: A Controlled Integration of Deterministic and Probabilistic Inference

Modern AI systems increasingly require both deterministic and probabilistic inference. Deterministic models provide reproducibility, auditability, and reliability, while probabilistic models enable uncertainty handling and reasoning under incomplete information. We introduce the Hybrid Functor Model, a formal extension of the Functor Model Architecture, in which a routing morphism selects between deterministic and probabilistic inference branches as a function of input, context, and governed weighting. Unlike traditional hybrid systems, which intermix deterministic and stochastic behaviors implicitly, the Hybrid Functor Model enforces explicit, auditable selection of inference regimes. This yields improved semantic clarity, governance, and operational reliability in mixed inference environments.