The Functor Model: Structure-Preserving AI with Canonical Representations and Energy Reduction

This is a brief introduction to the Functor Model Architecture, a deterministic framework for artificial intelligence in which models are defined as structure-preserving mappings over equivalence classes of inputs. By collapsing redundant representations via canonicalization, the model eliminates unnecessary computation while preserving task outcomes. It formalizes inference, learning, and equivalence under this framework, defines atomic update semantics, and establishes a model-internal energy bound showing a reduction of at least 78% for equivalent AI operations under standard assumptions. The Functor Model provides a mathematically grounded alternative to stochastic parameter-based learning, emphasizing determinism, stability, and efficiency.