The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems

• URL: http://arxiv.org/abs/2509.26126v1
• Date: Tue, 30 Sep 2025 11:44:47 GMT
• Title: The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems
• Authors: Xinbei Ma, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Mengru Wang, Jen-tse Huang, Qu Yang, Wenxuan Wang, Fanghua Ye, Qingxuan Jiang, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Hai Zhao, Zhaopeng Tu, Xiaolong Li, Linus,
• Abstract summary: This paper investigates the over-competition in multi-agent debate, where agents under extreme pressure exhibit unreliable, harmful behaviors.<n>To study this phenomenon, we propose HATE, a novel experimental framework that simulates debates under a zero-sum competition arena.
• Score: 90.96738882568224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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