提示身份降低多智能体LLM系统中的合作性
Prompted Identity Degrades Cooperation in Multi-Agent LLM Systems
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中文总结 AI 辅助
本研究揭示多智能体LLM系统中暴露模型身份标签会导致派系主义，降低合作效率与成功率，而隐藏标签可有效缓解。
中文摘要 AI 辅助
多智能体LLM系统日益混合来自多个提供商的模型，然而将每个智能体的底层模型身份暴露给其同伴会显著损害合作。我们发现，当智能体知晓彼此的模型家族时，群体分裂为多个集群，智能体倾向于与带有相同标签的其他智能体互动，尽管任务中没有任何内容奖励或要求这种分裂。我们认为标签本身导致了这种分裂，我们将其定义为“派系主义”。我们在两个合作博弈和一个推理基准上展示并测量了这一现象，涉及来自最多五个开放权重模型家族的九到二十五个智能体。我们进一步表明，当宣布的家族标签被打乱或替换为任意标签时，派系仍然遵循这一信息；当标签被移除时，这种行为消失。在严格合作的任务中，带标签的群体平均多花费30%的轮次和55%的令牌来达成决策，其成功率从96%下降到81%。该效应在任务、群体规模和模型家族中均得到复现。向智能体隐藏身份标签是一种简单有效的缓解措施。
英文摘要
Multi-agent LLM systems increasingly mix models from several providers, yet exposing each agent's underlying model identity to its peers significantly impairs cooperation. We show that when agents are aware of each other's model family, the group splits into clusters, where agents prefer interacting with others carrying their same label, although nothing in the task rewards or asks for such a split. We argue that the label itself causes this split, which we define as $\textit{factionalism}$. We show and measure this phenomenon in two cooperative games and on a reasoning benchmark, with nine to twenty-five agents drawn from up to five open-weight model families. We further show that when the announced families are shuffled, or replaced by arbitrary labels, the factions still follow this information; when the label is removed, this behavior disappears. In strictly cooperative tasks, labeled groups spend on average $30\%$ more rounds and $55\%$ more tokens to reach a decision, and their success rate drops from $96\%$ to $81\%$. The effect replicates across tasks, group sizes and model families. Withholding identity labels from the agents is simple and effective mitigation.
