AXION AI: A Deterministic Regulatory Validation Protocol with Authority-Constrained Non-Relaxation Under Systemic Stress

AXION AI is a deterministic regulatory validation protocol designed for high-impact financial decision environments. This paper introduces a hybrid discrete–continuous dynamical framework in which systemic stress evolves under input-to-state stable (ISS) conditions, while decision authorization is enforced through a strictly monotone authority threshold. The core contribution is the formalization of an Authority Contraction Law and a No Authority Relaxation Theorem (NART), proving that required authorization strictly increases under rising systemic stress. The protocol guarantees deterministic replay through canonical JSON serialization (RFC 8785 JCS), stable decimal arithmetic, ordered validation logic, and cryptographic lineage anchoring using FIPS-approved primitives. Unlike predictive AI systems, AXION AI operates as decision-authority infrastructure, producing governance-level outputs: VALID, ESCALATE, or INVALID. The framework aligns with global regulatory and risk management standards including NIST AI RMF, ISO/IEC 42001, FATF, and Basel operational resilience principles. This work positions deterministic authority validation as a structural mechanism for systemic risk containment in regulated financial systems.