想研究多模态智能体怎么合作干活？MECoBench 用一堆模型做了系统实验，结论直接告诉你协作和通信到底有没有用。
MECoBench是一个多模态具身协作基准，覆盖多种真实任务、两种协作结构和三种协作模式。基于GPT-4V、Gemini等MLLM的实验表明，协作整体提升任务完成度，但收益受协作增益与协调复杂度平衡影响。通信是协作增益的关键，最佳协作模式随团队规模和模型能力变化。协作在噪声先验和探索条件下能增强鲁棒性。基准代码和数据集已开源。
MECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied Environments
Recent multimodal large language models (MLLMs) have strong potential as embodied agents, but their ability to collaborate in visually grounded environments remains underexplored. To address this gap, we introduce MECoBench, a multimodal embodied cooperation benchmark with an evaluation platform spanning diverse real-world tasks, two cooperation structures, and three collaboration modes. Through extensive experiments across various MLLMs, we summarize three key findings: (i) Collaboration generally improves embodied task completion, but its benefits depend on balancing collaborative gains against coordination complexity. (ii) Communication is essential to collaboration gains, while the best collaboration mode depends on team size and model capability. (iii) Moreover, collaboration improves robustness under noisy priors and exploration conditions. Generally, MECoBench provides a systematic testbed for understanding the mechanisms and limits of multimodal embodied collaboration. Code and dataset are available at https://github.com/q-i-n-g/MECoBench.