视频生成模型不懂物理？新测试揭老底
视频生成模型常被吹成「世界模型」，但现有测试只看画面像不像，不管它是不是真懂物理。这篇论文搞了个新测试，把经典力学拆成10类任务，用400个视频考模型：先看它能不能感知物体，再考它能不能列出物理公式，最后看它能不能推对结果。结果最牛的视频模型总分才0.473（满分1），而且问题出在从感知到推理的每个环节——模型能画对苹果掉下来，但根本不知道重力加速度是多少。这不是你明天能用上的工具，但它告诉你：现在的AI视频生成，离「理解世界」还差得远。
📄 原文摘要(英文)
Modern video generation models are increasingly hailed as emerging world models with an internalized grasp of physical law. Yet existing benchmarks largely evaluate physical plausibility only at the output level, without verifying whether the model arrives there through a faithful, law-grounded reasoning process. We introduce Apple-PI, the first benchmark that anchors video-model evaluation explicitly in physical laws. Apple-PI comprises three components. 1) Orchard: a dataset of 400 videos covering ten canonical tasks in classical mechanics. It separates single-law tasks for confounder-free diagnosis from multi-law tasks for probing generalization. 2) Benchmark Protocol: a three-stage protocol based on scientific reasoning, including Perception, Formulation, and Deduction. It uses chain-of-frames prompting on infographic-annotated first frames, treating the generated video as the model's visible reasoning trace. 3) Evaluation Suite: a hybrid evaluation suite that combines MLLM-based subjective scoring with physics-law-grounded objective measures. This enables stage-resolved diagnosis of not only whether a model fails, but where it fails. Benchmarking 11 models shows that current video models remain far from reliable law-grounded world simulators, with the best video model scoring only 0.473. Our stage-, pillar-, and source-resolved analyses further expose a Perception-to-Formulation-to-Deduction bottleneck, weak multi-law state transfer, and a persistent Sim-to-Real gap. These findings position Apple-PI as a diagnostic foundation for guiding future video models toward world models with law-grounded physical intelligence.