English(EN) Embodied Multi-Agent Coordination by Aligning World Models Through Dialogue

对话减少了具身AI代理的冲突但并未影响成功率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-13 02:48

研究人员开发了一个新的框架，用于评估基于大型语言模型（LLM）的具身代理如何通过对话对齐其内部世界模型。PARTNR基准测试扩展了一个自然语言对话通道，用于测试具有部分环境观察的两个代理。实验表明，虽然对话显著减少了动作冲突，但与无声协调相比，它也降低了整体任务成功率，这表明当前模型在表面协调和真实世界模型对齐之间存在差距。 AI

影响引入了评估具身代理真实世界模型对齐的指标，突出了当前LLM在有效协作方面的局限性。

排序理由学术论文，详细介绍了具身AI代理的新基准和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

LLM
PARTNR

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Dilek Hakkani-Tür · 2026-05-13 02:48

通过对话对齐世界模型实现具身多智能体协调

Effective collaboration between embodied agents requires more than acting in a shared environment; it demands communication grounded in each agent's evolving understanding of the world. When agents can only partially observe their surroundings, coordination without communication …

本文如何被排名

报道来源 [1]

通过对话对齐世界模型实现具身多智能体协调

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