视频模型懂物理吗？新基准揭穿“世界模型”神话
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，不管它是不是真按物理规律推理。研究者搞了个新基准Apple-PI，用400段经典力学视频（比如小球碰撞、自由落体）来考模型：先看它能不能感知场景，再判断它是否用物理定律推理，最后看推导结果。结果最牛的视频模型也只得了0.473分（满分1），而且问题出在从感知到推理的链条上——模型能“看”到球，但不会用牛顿定律去算它怎么动。这不是你明天能用上的工具，但它戳破了一个流行叙事：画面逼真不等于懂物理。
📄 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.