新基准评估MLLM在具身AI中的协作与空间推理能力

报道来源 [4]

1. arXiv cs.AI TIER_1 English(EN) · Qingyun Liu, Jiwen Zhang, Jingyi Hu, Siyuan Wang, Zhongyu Wei · 2026-07-01 04:00

   MECoBench：具身环境中多模态智能体协作的系统性研究

   arXiv:2606.31966v1 Announce Type: cross Abstract: 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 mul…

2. arXiv cs.CV TIER_1 English(EN) · Zhongyu Wei · 2026-06-30 17:07

   MECoBench：具身环境多模态智能体协作的系统性研究

   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 eva…

3. arXiv cs.CV TIER_1 English(EN) · Haotian Li, Yida Wang, Leyuan Wang, Jinshan Lai, Keyang Wang, Zonghao Guo, Qiang Ma, Liuyu Xiang, Jianwei Hu, Zhaofeng He · 2026-06-29 04:00

   AirGroundBench: 探究异构多视角具身协作下多模态大模型的空间智能

   arXiv:2606.28049v1 Announce Type: new Abstract: In recent years, multimodal large language models (MLLMs) have shown strong potential for embodied intelligence, yet their ability to maintain geometrically consistent spatial understanding across heterogeneous views remains under-e…

4. arXiv cs.CV TIER_1 English(EN) · Zhaofeng He · 2026-06-26 12:51

   AirGroundBench：在异构多视角具身协作下探究多模态大模型的空间智能

   In recent years, multimodal large language models (MLLMs) have shown strong potential for embodied intelligence, yet their ability to maintain geometrically consistent spatial understanding across heterogeneous views remains under-evaluated. Existing benchmarks largely focus on s…