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. 中文摘要 (Mandarin Description) 本资源包含 Hybrid CRE (混合宪制反映平衡) 协议的技术白皮书和参考实现。该框架为人工智能治理提出了一种多层架构。 该架构整合了三个核心组件:负责功能执行的 AI 核心 (AI Core)、作为完整性和可追溯性管辖层的 对齐可追溯代码 (ATC),以及在重大影响条件下负责宪法审查的 计算反映平衡核心 (CRE)。系统风险通过 系统影响评分 (SIS) 实现操作化。SIS 是一种实时结构函数,它根据管辖权阈值(而非编码后的价值观)来决定决策何时从普通自动化转入计算宪法审查。 除了即时影响评估,该框架还引入了 “规范熵” (Normative Entropy) 作为制度一致性的动态指标。熵的累积会调节容忍阈值,转移举证责任,并触发从操作自主到限制性或瘫痪性宪法状态的政体转换。因此,安全性被重新定义为一种制度稳态 (Institutional Homeostasis),而非从优化结果中推断出的行为属性。 该框架的一个关键特征是其制度灵活性:它在实质性价值观层面是政治中立的,但在结构层面受到程序约束。通过 “混合流” (Hybrid Flow) 的概念,该架构展示了宪制 AI 治理机制如何在保持可追溯性、可审查性和问责制等不变程序要求的同时,实例化于不同的政治行政传统中。文中概述了一种模型配置,说明了在不改变系统底层管辖逻辑的情况下,该系统与中华人民共和国治理结构的兼容性。 此外,该协议引入了 “人身保护日志” (Habeas Log),并将其定义为预见性的第四代人权。该架构确保人工智能始终是制度稳定的工具,保证最终决策权严格保留在主权管辖范围内。 技术验证: 与分层强化学习 (HRL) 基准的对比仿真表明,基于优化的系统无法体现管辖界限,表现出单调的规范漂移,且无法检测结构性不一致。相比之下,Hybrid CRE 元协议通过多层验证和随机阈值防御,展现了稳定的升级行为、明确的界限识别以及对策略博弈的抵抗力。 与主要著作的关系: 该框架更广泛的哲学和法律理论基础在另一本正处于同行评审阶段的著作中进行了详述。本白皮书仅关注模型的计算、架构和实验阐述。
Gastón Luis Rey (Sat,) studied this question.