小心你信任的对象：LLM多智能体博弈中受污染通信下的协调动态
Be Careful Who You Trust: Coordination Dynamics under Corrupted Communication in LLM Multi-Agent Games
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中文总结 AI 辅助
本研究通过迭代猎鹿博弈实验，发现受污染的公共通信会严重降低LLM多智能体的公共合作成功率，但原始选择受影响较小，且需区分原始选择、公共动作与执行结果以评估稳健性。
中文摘要 AI 辅助
大型语言模型越来越多地被用作交互式智能体，但当公共通信不可靠时，其协调的稳健性仍不清楚。我们在同质LLM群体参与的迭代$N$人猎鹿游戏中研究该问题，并采用受控的程序化动作反转，该反转同时改变公共记录和用于执行的动作。在涵盖群体规模、协调阈值、污染水平和七种LLM的实验网格中，我们观察到三种主要模式。首先，诚实智能体在翻转前的Stag选择随污染增加而下降，但公共成功的急剧下降主要是机械性的。在焦点$N=5,M=3$设置中，翻转前成功率在80%污染下仍保持78%，而公共成功率降至12%。其次，诚实选择与决策时可用的公共历史相关，尤其是在高污染下。第三，三种阈值式公共报告基准与LLM智能体产生相似的动作匹配率，表明LLM决策与这些基准之间存在实质性的描述性一致性。总体而言，我们的结果表明，在评估多智能体稳健性时，必须区分原始选择、公共动作和执行结果，因为受污染的通信会严重且可预测地削弱互利合作。
英文摘要
Large language models are increasingly used as interacting agents, but it remains unclear how robust their coordination is when public communication is unreliable. We study this question in iterated $N$-player Stag Hunt games played by homogeneous LLM groups under controlled programmatic action inversion, which changes both the public transcript and the actions used for execution. Across an experimental grid spanning group sizes, coordination thresholds, corruption levels, and seven LLMs, we observe three main patterns. First, honest agents' pre-flip Stag choices decline as corruption increases, but the sharp fall in public success is primarily mechanical. In the focal $N=5,M=3$ setting, pre-flip success remains 78% at 80% corruption, while public success falls to 12%. Second, honest choices are associated with the public history available at decision time, particularly under high corruption. Third, three threshold-style public-report benchmarks yield similar action-match rates to the LLM agents, showing substantial descriptive agreement between LLM decisions and these benchmarks. Overall, our results show that original choices, public actions, and executed outcomes must be separated when evaluating multi-agent robustness, as corrupted communication can severely and predictably degrade mutually beneficial cooperation.
发表机构
- University College London（伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。
