What question did this study set out to answer?

The aim is to present a modular architecture for AI alignment that enhances governance through constraints and verifiable auditing.

May 12, 2026Open Access

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

Key Points

The aim is to present a modular architecture for AI alignment that enhances governance through constraints and verifiable auditing.
Developed a constrained control system framework for AI alignment.
Separated safety, reconstruction, and auditing processes into distinct layers.
Defined metrics for evaluating output distributions and lineage preservation.
Achieved effective alignment by quantifying distortion in output distributions.
Introduced a continuity reconstruction model that enhances lineage tracking.
Established a reliable deterministic ledger for reproducible auditing of AI systems.

Abstract

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.

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