Recursive Admissibility Architecture: A Structural Bridge from Internal System Dynamics to Domain Behaviour

Many complex systems generate candidate states, evaluate those states against constraints, and continue operation only while those constraints remain satisfied. This paper formalises this pattern as a recursive admissibility architecture within the Paton System. The framework demonstrates how internal structural relations (Tier-2), admissibility constraints (Tier-3), observable outputs (Tier-4), and recursive continuation (Tier-5) together produce the operational behaviour observed in real-world systems. Neural networks and other learning systems provide a clear example of this architecture in practice. The result establishes a structural bridge between the internal architecture of systems and their observable behaviour across domains.