AXION Logistic AI: A Science-Constrained, Proof-Carrying Decision Infrastructure for Modern Logistics Systems

This study introduces AXION Logistic AI, a science-constrained decision infrastructure designed to transform modern logistics systems from optimization-driven frameworks into scientifically validated decision environments. Unlike conventional artificial intelligence models that primarily rely on predictive optimization, the proposed system enforces a multi-domain constraint validation layer before any decision is executed. In this framework, decisions are considered valid only if they simultaneously satisfy constraints derived from multiple scientific domains, including physics, navigation systems, temporal logic, chemical stability, and governance requirements. The core mathematical formulation defines the valid decision space as the intersection of these constraint domains, ensuring that infeasible, unsafe, or non-compliant decisions are eliminated prior to optimization. This approach introduces a fundamental shift from decision optimization to decision validity enforcement. A key contribution of this work is the introduction of a proof-carrying decision model, where each decision is accompanied by a formal validation structure. This enables deterministic auditability, traceability, and reproducibility, addressing critical challenges in AI governance and regulatory compliance. In addition, the system incorporates a deterministic uncertainty-lock mechanism, where decisions are automatically blocked when uncertainty exceeds a defined threshold. This fail-safe design prevents execution under high-risk or insufficient-information conditions, significantly reducing systemic risk. The framework further integrates: State-space modeling for dynamic system representation Entropy-based uncertainty quantification Irreversibility-aware risk modeling GNSS-based navigation integrity constraints Temperature-dependent chemical degradation models By combining these elements, AXION Logistic AI establishes a unified architecture that is mathematically grounded, physically feasible, and governance-compliant. This work contributes to the emerging field of science-constrained decision systems, providing a new foundation for logistics intelligence, AI safety, and decision governance. It demonstrates that reliable decision-making in complex systems requires not only optimization but also strict adherence to multi-domain scientific validity.