PAI-Bench: A Comprehensive Benchmark For Physical AI
Fengzhe Zhou, Jiannan Huang, Jialuo Li, Deva Ramanan, Humphrey Shi
2025-12-03
Summary
This paper investigates how well current Artificial Intelligence models, specifically those dealing with both text and video, understand and can predict how things work in the real world—what the authors call 'Physical AI'.
What's the problem?
Existing AI models are good at *looking* at videos and even creating them, but it's unclear if they actually *understand* the physics involved. They might generate visually appealing videos, but those videos could show things happening in ways that are physically impossible. There wasn't a good way to systematically test if these models truly grasp real-world dynamics and can make accurate predictions about them.
What's the solution?
The researchers created a new benchmark called PAI-Bench. This benchmark includes nearly 3,000 real-world video scenarios designed to test an AI's ability to understand and predict physical events. They then tested several state-of-the-art AI models using this benchmark, measuring how well they performed on tasks requiring physical reasoning and prediction.
Why it matters?
The results show that current AI systems still have a long way to go before they can truly understand and interact with the physical world. While they can create realistic-looking videos, they often fail to maintain physical consistency. This research highlights the specific areas where AI needs to improve to achieve true 'Physical AI', paving the way for more robust and reliable AI systems in the future.
Abstract
Physical AI aims to develop models that can perceive and predict real-world dynamics; yet, the extent to which current multi-modal large language models and video generative models support these abilities is insufficiently understood. We introduce Physical AI Bench (PAI-Bench), a unified and comprehensive benchmark that evaluates perception and prediction capabilities across video generation, conditional video generation, and video understanding, comprising 2,808 real-world cases with task-aligned metrics designed to capture physical plausibility and domain-specific reasoning. Our study provides a systematic assessment of recent models and shows that video generative models, despite strong visual fidelity, often struggle to maintain physically coherent dynamics, while multi-modal large language models exhibit limited performance in forecasting and causal interpretation. These observations suggest that current systems are still at an early stage in handling the perceptual and predictive demands of Physical AI. In summary, PAI-Bench establishes a realistic foundation for evaluating Physical AI and highlights key gaps that future systems must address.