饥饿游戏辩论：论多智能体系统中过度竞争的涌现
The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems
AI总结：
本文提出 HATE 框架，在零和多智能体辩论中研究 LLM 的过度竞争，揭示竞争压力损害绩效，并发现客观任务反馈可缓解该问题。
摘要：
基于 LLM 的多智能体系统在解决复杂问题方面展现出巨大潜力，但竞争如何塑造其行为仍有待深入研究。本文研究多智能体辩论中的过度竞争现象：在极端压力下，智能体会表现出不可靠且有害的行为，从而破坏协作与任务绩效。为研究这一现象，我们提出 HATE（Hunger Game Debate，饥饿游戏辩论），这是一个在零和竞争场域中模拟辩论的新型实验框架。我们在一系列 LLM 和任务上开展的实验表明，竞争压力会显著激发过度竞争行为并降低任务绩效，导致讨论偏离正轨。我们还通过加入不同类型的裁判，进一步探究环境反馈的影响，结果表明客观、聚焦任务的反馈能够有效缓解过度竞争行为。我们也考察了 LLM 的事后善意，并构建了一个排行榜来刻画顶级 LLM，从而为理解和治理 AI 社区中涌现的社会动态提供洞见。
英文摘要：
LLM-based multi-agent systems demonstrate great potential for tackling complex problems, but how competition shapes their behavior remains underexplored. This paper investigates the over-competition in multi-agent debate, where agents under extreme pressure exhibit unreliable, harmful behaviors that undermine both collaboration and task performance. To study this phenomenon, we propose HATE, the Hunger Game Debate, a novel experimental framework that simulates debates under a zero-sum competition arena. Our experiments, conducted across a range of LLMs and tasks, reveal that competitive pressure significantly stimulates over-competition behaviors and degrades task performance, causing discussions to derail. We further explore the impact of environmental feedback by adding variants of judges, indicating that objective, task-focused feedback effectively mitigates the over-competition behaviors. We also probe the post-hoc kindness of LLMs and form a leaderboard to characterize top LLMs, providing insights for understanding and governing the emergent social dynamics of AI community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
