English(EN) Plan-Bench: Can Existing VLMs Go Well Beyond Perception to Reason and Plan in Embodied Worlds?

新的Plan-Bench基准揭示了VLM在推理和规划方面的局限性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员推出了Plan-Bench，一个旨在评估具身环境中视觉语言模型（VLMs）推理和规划能力的新基准。该基准包含16个子类别的699个样本，旨在将这些高级规划技能与动作执行区分开来。对28个VLMs的初步评估显示，即使是表现最好的模型，准确率也仅达到75.0%，表明在改进方面仍有很大空间。研究还发现，模型越大通常表现越好，并且作为具身AI任务的高级规划者的潜力也在不断增长。 AI

影响突出了VLM规划能力的当前局限性，指导未来研究朝着更强大的具身AI代理发展。

排序理由该集群包含一篇介绍AI模型评估基准的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Juntong Feng, Qingan Liu, Jingru Dong, Yue Zhu, Weiming Zheng, Yujie Cheng, Xinzhe Han, Shuai Hao, Yunzhi Zhuge, Haiwen Diao, Huchuan Lu · 2026-10-15 04:00

Plan-Bench：现有的VLMs能否超越感知，在具身世界中进行推理和规划？

arXiv:2610.18836v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have achieved strong performance in visual perception and spatial reasoning, leading to growing interest in using them as high-level planners for embodied AI. However, physical tasks require models …

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报道来源 [1]

Plan-Bench：现有的VLMs能否超越感知，在具身世界中进行推理和规划？

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