A Constrained Control Architecture for AI Alignment, Continuity Reconstruction, and Deterministic Audit Logging

Abstract This paper presents a technical architecture for AI alignment modeled as a constrained control system. The framework separates unconstrained reasoning, safety projection, continuity reconstruction, and deterministic audit logging into distinct layers. We define alignment-induced distortion using divergence between raw and constrained output distributions, introduce a continuity reconstruction model for preserving lineage and revision state, and specify a deterministic consensus ledger for reproducible system auditing. The result is a modular architecture for long-horizon AI governance under constraint, revision, and verification pressure.