Multi-agent systems built from teams of large language models (LLMs) are increasingly deployed for collaborative scientific reasoning and problem-solving. These systems require agents to coordinate under shared constraints, such as GPUs or credit balances, where cooperative behavior matters. Behavioral economics provides a rich toolkit of games that isolate distinct cooperation mechanisms, yet it remains unknown whether a model's behavior in these stylized settings predicts its performance in realistic collaborative tasks. Here, we benchmark 35 open-weight LLMs across six behavioral economics games and show that game-derived cooperative profiles robustly predict downstream performance in AI-for-Science tasks, where teams of LLM agents collaboratively analyze data, build models, and produce scientific reports under shared budget constraints. Models that effectively coordinate games and invest in multiplicative team production (rather than greedy strategies) produce better scientific reports across three outcomes, accuracy, quality, and completion. These associations hold after controlling for multiple factors, indicating that cooperative disposition is a distinct, measurable property of LLMs not reducible to general ability. Our behavioral games framework thus offers a fast and inexpensive diagnostic for screening cooperative fitness before costly multi-agent deployment.

翻译：由大型语言模型组成的多智能体系统正越来越多地部署于协作性科学推理与问题解决任务中。这些系统要求智能体在共享约束条件下（如图形处理器或信用额度）进行协调，其中合作行为至关重要。行为经济学提供了丰富的博弈工具集，可分离不同合作机制，但模型在这些典型场景中的行为能否预测其在真实协作任务中的表现仍属未知。本研究对35个开放权重的大型语言模型在六项行为经济学博弈中的表现进行了基准测试，并证明博弈衍生出的合作档案能够稳健预测其在AI驱动科学任务中的下游性能——在该任务中，由LLM智能体组成的团队在共享预算约束下协作分析数据、构建模型并生成科学报告。能够有效协调博弈并投资于乘数型团队生产（而非贪婪策略）的模型，在准确性、质量和完成度三项指标上生成了更优的科学报告。上述关联在控制多种因素后依然成立，表明合作倾向是LLM一种独立可测的属性，不可简化为通用能力。因此，我们的行为博弈框架为在代价高昂的多智能体部署前筛选合作适配性，提供了一种快速廉价的诊断工具。