AI Alignment as a Constrained Dynamical System: Separating Inference and Safety via Projection Operators

Abstract We present a formal interpretation of AI alignment as a constrained dynamical system, in which unconstrained probabilistic reasoning is projected into a safety-compliant state space. This framework separates core inference from safety enforcement, modeling alignment as acontinuous constraint process rather than a behavioral overlay. We introduce a divergence-based conceptual metric for alignment-induced distortion, decompose latency into computational and policy components, and define a taxonomy of safety gating mechanisms. This perspective connects AI alignment with control theory and constrained optimization, enabling measurable analysis of safety-performance tradeoffs.