Paton-Native AI Architectures: Admissibility-Driven Learning Systems within the Paton Framework

This paper presents a forward-looking structural architecture for artificial intelligence systems built from the Paton System framework. While existing AI systems demonstrate strong capabilities in optimisation, pattern recognition, and scalable learning, they lack a pre-theoretical admissibility layer governing which states are permitted prior to learning. The paper introduces Paton-native AI architectures, in which admissibility is enforced at the architectural level rather than applied post hoc. Learning is constrained to admissible regions, updates are restricted by constraint compatibility, and system stability is preserved through lowest admissible configuration behaviour under strain. Constraint-typed flow and admissible trajectories ensure that learning proceeds only within structurally valid regions. This framework does not introduce new computational primitives and does not replace existing machine learning methods. It provides a pre-theoretical structural lens in which admissibility precedes optimisation, extending prior work on admissibility-based training systems into full architectural design.