DeepPHY: Benchmarking Agentic VLMs on Physical Reasoning
This addresses the need for cost-effective evaluation of VLMs in complex physical tasks, though it is incremental as it focuses on benchmarking rather than solving the underlying reasoning issues.
The paper tackles the problem of evaluating Vision Language Models' (VLMs) physical reasoning abilities by introducing DeepPHY, a benchmark framework with simulated environments, finding that state-of-the-art VLMs perform poorly in translating physical knowledge into predictive control.
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.