AI RESEARCH
Cooperative Profiles Predict Multi-Agent LLM Team Performance in AI for Science Workflows
arXiv CS.CL
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ArXi:2604.20658v1 Announce Type: new 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.