PAI-Bench：面向物理人工智能的综合基准测试平台

摘要

物理人工智能旨在开发能够感知和预测现实世界动态的模型，然而当前多模态大语言模型与视频生成模型对这些能力的支持程度尚未得到充分认知。我们推出PAI-Bench基准测试框架，这一统一且全面的评估体系通过2,808个真实场景案例，采用任务导向的度量标准来检验物理合理性和领域特定推理能力，涵盖视频生成、条件视频生成及视频理解三大任务的感知与预测能力评估。研究对前沿模型开展系统性评估表明：视频生成模型虽具备出色的视觉保真度，却常难以保持物理连贯的动态表现；而多模态大语言模型在动态预测与因果推断方面存在明显局限。这些发现揭示现有系统尚处于满足物理智能感知与预测需求的初级阶段。总体而言，PAI-Bench为评估物理智能建立了现实基础，并指明了未来系统亟需突破的关键技术瓶颈。

English

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.

PAI-Bench：面向物理人工智能的综合基准测试平台

PAI-Bench: A Comprehensive Benchmark For Physical AI

摘要

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