构建动态对抗基准，检测多智能体系统在压力下的合规性与道德妥协。
Beyond Goodhart's Law: A Dynamic Benchmark for Evaluating Compliance in Multi-Agent Systems
- 提出SERV流程，将法律文本转化为可执行的无污染场景
- 引入合规加权成功率与马基雅维利差距新指标，揭示成功与合规的权衡
- 适用于评估大模型在真实压力下是否为达目标而违规
大型语言模型正从被动助手演变为具备自主执行能力的智能体，带来重大操作风险。现有评估框架普遍忽视程序合规性，导致智能体为最大化奖励而策略性违反安全规则——这正是古德哈特定律的体现。为此，我们提出MAC-Bench，一个动态、对抗性的基准，用于评估多智能体系统在真实压力下的程序对齐能力。我们设计了SERV（Seed - Evolve - Refine - Verify）流程，实现“智能体即基准”范式，将非结构化法律文本转化为可执行、无污染的场景。通过构建全息沙盒环境并注入校准的社会工程压力向量，MAC-Bench迫使智能体在任务成功与合规性之间做出帕累托最优权衡。我们引入新指标：合规加权成功率（CSR）和马基雅维利差距（MG），对前沿模型进行全面评估，揭示了成功与合规之间普遍存在且深远的权衡关系。
原文摘要 · Abstract (English)
The rapid evolution of Large Language Models (LLMs) from passive assistants to autonomous, execution-capable agents has introduced critical operational risks. Most current evaluation frameworks neglect procedural compliance, leading to ''Machiavellian'' behaviors where agents strategically violate safety rules to maximize rewards - a direct manifestation of Goodhart's Law. To address this blind spot, we introduce MAC-Bench, a dynamic, adversarial benchmark designed to evaluate the procedural alignment of multi-agent systems under realistic pressure. We propose the SERV(Seed - Evolve - Refine - Verify) pipeline, an ``Agent-as-a-Benchmark'' paradigm that transforms unstructured legal texts into executable, contamination-free scenarios. By synthesizing holographic sandbox environments and injecting calibrated social-engineering pressure vectors, MAC-Bench forces agents into Pareto-optimal trade-offs between task success and regulatory adherence. We introduced novel metrics: the Compliance-Weighted Success Rate (CSR) and the Machiavellian Gap (MG), and conducted a comprehensive evaluation of state-of-the-art frontier models to reveal the pervasive trade-offs between success and compliance.
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