Explaining and Proving Hybrid CRE for AI governance

This repository contains the technical white paper and reference implementation for the Hybrid Computational Reflective Equilibrium (Hybrid CRE) protocol. This framework introduces a multi-layered architecture for AI governance. The architecture integrates three core components: an AI Core responsible for capability execution, an Alignment Traceability Code (ATC) functioning as an integrity and traceability jurisdiction, and a Computational Reflective Equilibrium Core (CRE) responsible for constitutional review under elevated impact conditions. Systemic risk is operationalized through the Systemic Impact Score (SIS), a real-time structural function that determines when decisions transition from ordinary automation into computational constitutional review, based on jurisdictional thresholds rather than encoded values. Beyond instantaneous impact assessment, the framework introduces Normative Entropy as a dynamic indicator of institutional coherence. Entropy accumulation modulates tolerance thresholds, shifts burdens of proof, and triggers regime transitions ranging from operational autonomy to restrictive or paralytic constitutional states. Safety is thereby reconceptualized as a form of institutional homeostasis rather than a behavioral property inferred from optimization outcomes. A key feature of the framework is its institutional flexibility: it is politically agnostic at the level of substantive values yet procedurally constrained at the structural level. Through the concept of Hybrid Flow, the architecture demonstrates how constitutional AI governance mechanisms can be instantiated across distinct political-administrative traditions while preserving invariant procedural requirements such as traceability, reviewability, and accountability. A model configuration is outlined illustrating compatibility with governance structures characteristic of the People’s Republic of China, without altering the underlying jurisdictional logic of the system. It introduces the Habeas Log, a projected fourth generation Human Right. The architecture ensures that AI remains a tool of institutional stability, guaranteeing that ultimate decision-making authority resides strictly within the sovereign's jurisdictional boundaries. Technical Validation: Comparative simulations against Hierarchical Reinforcement Learning (HRL) baselines demonstrate that optimization-based systems fail to represent jurisdictional limits, exhibit monotonic normative drift, and do not detect structural incoherence. By contrast, the Hybrid CRE meta-protocol displays stable escalation behavior, explicit limit recognition, and resistance to gaming through multi-layer verification and stochastic threshold defenses. Relationship with the main work: The broader philosophical and legal-theoretical foundations of this framework are developed in a separate book-length manuscript currently under peer review. The present white paper focuses exclusively on the computational, architectural, and experimental formulation of the model.