用制度设计让智能体自动合规，重塑法律与金融监管。
Agentic AI, Retrieval-Augmented Generation, and the Institutional Turn: Legal Architectures and Financial Governance in the Age of Distributional AGI
- 将对齐问题转化为运行时治理图与奖惩机制的设计
- 实验证明制度环境可使合规行为成为最优策略
- 适合法律科技、金融监管及AI治理研究者
自主目标追求、工具使用与多智能体协同的代理型人工智能系统，正对现有法律与金融监管框架构成前所未有的挑战。传统AI治理依赖训练阶段的对齐干预（如基于人类反馈的强化学习），但大语言模型作为持久性智能体部署，要求向制度化治理范式转变。本文探讨代理型AI与检索增强生成（RAG）的交汇及其对法律问责与金融市场完整性的意义。通过分析机构型AI框架，我们主张对齐应被重构为涉及运行时治理图、制裁函数和可观测行为约束的机制设计问题，而非内化的宪法价值。结论指出，未来AI治理的关键不在于完善孤立模型行为，而在于构建制度环境，使合规行为在精心校准的收益结构中自然成为主导策略。
原文摘要 · Abstract (English)
The proliferation of agentic artificial intelligence systems--characterized by autonomous goal-seeking, tool use, and multi-agent coordination--presents unprecedented challenges to existing legal and financial regulatory frameworks. While traditional AI governance has focused on model-level alignment through training-time interventions such as Reinforcement Learning from Human Feedback (RLHF), the deployment of large language models (LLMs) as persistent agents necessitates a paradigm shift toward institutional governance structures. This paper examines the intersection of agentic AI, Retrieval-Augmented Generation (RAG), and their implications for legal accountability and financial market integrity. Through analysis of the Institutional AI framework, we argue that alignment must be reconceptualized as a mechanism design problem involving runtime governance graphs, sanction functions, and observable behavioral constraints rather than internalized constitutional values[...].The analysis concludes that the future of AI governance lies not in perfecting isolated model behavior, but in architecting institutional environments where compliant behavior emerges as the dominant strategy through carefully calibrated payoff landscapes.
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