研究大模型群体规模如何影响集体偏差与行为演化。
Group size effects and collective misalignment in LLM multi-agent systems
- 通过协调游戏实验，发现群体互动会放大、产生或覆盖个体偏见。
- 群体规模呈非线性影响，不同模型表现出不同的动态规律。
- 提出平均场分析法，揭示大规模群体的确定性演化规律。
大型语言模型（LLMs）多智能体系统的应用迅速扩展，带来了单智能体评估无法捕捉的动态特性。然而，现有研究多将单个智能体与固定规模群体对比，忽略了群体规模的影响。本文系统探索了不同群体规模下的行为变化。聚焦于多智能体失调问题，基于近期发现——交互式LLM在简单协调游戏中会产生个体模型中不存在的集体偏见——我们首先证明集体偏见比此前认知更深刻：交互可放大个体偏见、引入新偏见或覆盖模型级偏好。其次，发现群体规模以非线性方式影响系统动态，揭示出依赖模型的动态区域。最后，提出平均场分析方法，表明在超过临界种群规模后，模拟结果收敛至确定性预测，揭示竞争均衡的吸引盆。这些发现确立了群体规模作为多智能体系统动态的关键驱动因素，强调在大规模部署时需考虑群体层面效应。
原文摘要 · Abstract (English)
Multi-agent systems of large language models (LLMs) are rapidly expanding across domains, introducing dynamics not captured by single-agent evaluations. Yet, existing work has mostly contrasted the behavior of a single agent with that of a collective of fixed size, leaving open a central question: how does group size shape dynamics? Here, we move beyond this dichotomy and systematically explore outcomes across the full range of group sizes. We focus on multi-agent misalignment, building on recent evidence that interacting LLMs playing a simple coordination game can generate collective biases absent in individual models. First, we show that collective bias is a deeper phenomenon than previously assessed: interaction can amplify individual biases, introduce new ones, or override model-level preferences. Second, we demonstrate that group size affects the dynamics in a non-linear way, revealing model-dependent dynamical regimes. Finally, we develop a mean-field analytical approach and show that, above a critical population size, simulations converge to deterministic predictions that expose the basins of attraction of competing equilibria. These findings establish group size as a key driver of multi-agent dynamics and highlight the need to consider population-level effects when deploying LLM-based systems at scale.
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