DeepPHY: Benchmarking Agentic VLMs on Physical Reasoning
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918117247025152 |
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| author | Xu, Xinrun Bu, Pi Wang, Ye Karlsson, Börje F. Wang, Ziming Song, Tengtao Zhu, Qi Song, Jun Ding, Zhiming Zheng, Bo |
| author_facet | Xu, Xinrun Bu, Pi Wang, Ye Karlsson, Börje F. Wang, Ziming Song, Tengtao Zhu, Qi Song, Jun Ding, Zhiming Zheng, Bo |
| contents | Although Vision Language Models (VLMs) exhibit strong perceptual abilities and impressive visual reasoning, they struggle with attention to detail and precise action planning in complex, dynamic environments, leading to subpar performance. Real-world tasks typically require complex interactions, advanced spatial reasoning, long-term planning, and continuous strategy refinement, usually necessitating understanding the physics rules of the target scenario. However, evaluating these capabilities in real-world scenarios is often prohibitively expensive. To bridge this gap, we introduce DeepPHY, a novel benchmark framework designed to systematically evaluate VLMs' understanding and reasoning about fundamental physical principles through a series of challenging simulated environments. DeepPHY integrates multiple physical reasoning environments of varying difficulty levels and incorporates fine-grained evaluation metrics. Our evaluation finds that even state-of-the-art VLMs struggle to translate descriptive physical knowledge into precise, predictive control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05405 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeepPHY: Benchmarking Agentic VLMs on Physical Reasoning Xu, Xinrun Bu, Pi Wang, Ye Karlsson, Börje F. Wang, Ziming Song, Tengtao Zhu, Qi Song, Jun Ding, Zhiming Zheng, Bo Artificial Intelligence Although Vision Language Models (VLMs) exhibit strong perceptual abilities and impressive visual reasoning, they struggle with attention to detail and precise action planning in complex, dynamic environments, leading to subpar performance. Real-world tasks typically require complex interactions, advanced spatial reasoning, long-term planning, and continuous strategy refinement, usually necessitating understanding the physics rules of the target scenario. However, evaluating these capabilities in real-world scenarios is often prohibitively expensive. To bridge this gap, we introduce DeepPHY, a novel benchmark framework designed to systematically evaluate VLMs' understanding and reasoning about fundamental physical principles through a series of challenging simulated environments. DeepPHY integrates multiple physical reasoning environments of varying difficulty levels and incorporates fine-grained evaluation metrics. Our evaluation finds that even state-of-the-art VLMs struggle to translate descriptive physical knowledge into precise, predictive control. |
| title | DeepPHY: Benchmarking Agentic VLMs on Physical Reasoning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.05405 |