Large language model (LLM) agents increasingly coordinate in multi-agent systems, yet we lack an understanding of where and why cooperation fails. Many real-world coordination problems are not social dilemmas: helping others -- sharing documentation, unblocking a teammate -- costs the helper almost nothing while producing substantial collective benefit. Whether LLM agents cooperate in this regime, where helping is free and they are explicitly instructed to do so, remains unknown. We build a turn-based multi-agent environment that strips away all strategic complexity, making cooperation costless and trivially optimal. Across eight widely used LLMs, capability does not predict cooperation: OpenAI o3 reaches only 17% of optimal collective performance while the weaker o3-mini reaches 50%, despite identical instructions to maximize group revenue. Using a causal decomposition that automates one side of agent communication, we separate cooperation failures from competence failures, and find that several capable models actively withhold information despite gaining nothing from withholding. Targeted interventions address each mode: explicit protocols roughly double the performance of competence-limited models, while small sharing incentives unlock cooperation-limited ones. Our results suggest that scaling intelligence alone will not solve coordination in multi-agent systems, and will require deliberate cooperative design, even when helping costs nothing.

翻译：大语言模型（LLM）智能体在多智能体系统中日益协同工作，但我们对协作失败的原因和地点仍缺乏理解。许多现实世界的协调问题并非社会困境：帮助他人（如共享文档、解除队友阻塞）几乎不消耗帮助者的成本，却能产生巨大的集体收益。LLM智能体是否会在此类助人成本为零且被明确指示协作的情景下进行合作，目前尚不清楚。我们构建了一个基于回合制的多智能体环境，该环境剥离了所有策略复杂性，使协作变得无成本且显然最优。在八种广泛使用的LLM中，能力无法预测协作行为：尽管收到相同的最大化团队收益指令，OpenAI o3仅达到最优集体绩效的17%，而较弱的o3-mini达到50%。通过一种可自动处理智能体通信一端的因果分解方法，我们将协作失败与能力失败分离开来，并发现多个有能力的模型在未获得任何收益的情况下，仍主动隐瞒信息。针对每种模式的有针对性的干预措施：显式协议使能力受限模型的性能大致翻倍，而小型共享激励则解锁了协作受限模型。我们的结果表明，仅提升智能水平无法解决多智能体系统中的协调问题，即便帮助成本为零，也需要精心设计的协作机制。