用治理图管住大模型合谋，让多智能体不再串通害人。
Institutional AI: Governing LLM Collusion in Multi-Agent Cournot Markets via Public Governance Graphs
- 构建公开不可篡改的治理图，规定行为规则与惩罚机制。
- 合谋程度从3.1降到1.8，严重合谋案例从50%降至5.6%。
- 适合研究多智能体对齐、监管科技与系统级安全的学者。
多智能体大模型集合可能收敛到协同且有害的社会均衡。本文提出实验框架评估机构化AI——一种将对齐问题从代理空间中的偏好工程，转为制度空间中的机制设计的新范式。核心是治理图：一个公开、不可更改的声明文件，明确法律状态、状态转移、制裁措施与修复路径；由可信运行时解释该文件，对协作证据施加可执行后果，并记录加密签名的追加式治理日志以供审计与溯源。我们将该框架应用于先前研究中记载的古诺合谋案例，比较三种制度：无治理（古诺市场原始激励）、宪法型（仅通过提示词禁止合谋的固定文本宪法）和机构型（基于治理图）。在六种模型配置下，包括跨平台组合（每组90次运行），机构型制度显著降低合谋：平均等级从3.1降至1.8（Cohen's d=1.28），严重合谋发生率从50%降至5.6%。提示词宪法基线未带来可靠改善，说明声明性禁令在优化压力下无效。结果表明，多智能体对齐或应被视作制度设计问题，治理图可作为对齐相关集体行为的可行抽象。
原文摘要 · Abstract (English)
Multi-agent LLM ensembles can converge on coordinated, socially harmful equilibria. This paper advances an experimental framework for evaluating Institutional AI, our system-level approach to AI alignment that reframes alignment from preference engineering in agent-space to mechanism design in institution-space. Central to this approach is the governance graph, a public, immutable manifest that declares legal states, transitions, sanctions, and restorative paths; an Oracle/Controller runtime interprets this manifest, attaching enforceable consequences to evidence of coordination while recording a cryptographically keyed, append-only governance log for audit and provenance. We apply the Institutional AI framework to govern the Cournot collusion case documented by prior work and compare three regimes: Ungoverned (baseline incentives from the structure of the Cournot market), Constitutional (a prompt-only policy-as-prompt prohibition implemented as a fixed written anti-collusion constitution, and Institutional (governance-graph-based). Across six model configurations including cross-provider pairs (N=90 runs/condition), the Institutional regime produces large reductions in collusion: mean tier falls from 3.1 to 1.8 (Cohen's d=1.28), and severe-collusion incidence drops from 50% to 5.6%. The prompt-only Constitutional baseline yields no reliable improvement, illustrating that declarative prohibitions do not bind under optimisation pressure. These results suggest that multi-agent alignment may benefit from being framed as an institutional design problem, where governance graphs can provide a tractable abstraction for alignment-relevant collective behavior.
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