AI 的“惊讶”可能是假的
我们以为视频模型预测出错，是因为它“看出”了物理上不可能的事。这篇论文拆开来看：预测误差里混着外观、颜色、动作这些普通因素，甚至把一帧重复播放，误差比原视频还高；把整段视频换个颜色，误差反而降低。物理上不可能的事件，有时误差比可能事件还低。也就是说，模型内部可能确实编码了物理信息，但它自己报出来的“惊讶”数字，根本不能当物理判断的可靠信号。它不是你明天能用上的东西，但提醒你：AI 说“不对劲”的时候，先别信。
📄 原文摘要(英文)
Prediction errors from video world models are often treated as physical surprise, but they can also reflect appearance, static content, motion, or ordinary prediction difficulty. We present an evaluation framework that separates what frozen representations contain, how they organize appearance and physical differences, and what changes a model's own prediction error. On IntPhys2, a benchmark of physically possible and impossible events, we compare representations from latent prediction, masked pixel reconstruction, and simple visual statistics. Appearance remains easy to decode, while V-JEPA2 exposes modest information about whether events are possible or impossible. Looking at the latent geometry, appearance changes become smaller compared with differences between videos, their directions line up more consistently across scenes, and their overlap with physical changes decreases in deeper layers. Across action-conditioned models, incorrect actions raise prediction error, while appearance changes also produce substantial responses. In Physion++ video clips, repeating a single frame can produce higher error than the original clip, while changing color throughout the clip can lower it. Using Kubric, a simulation and rendering framework, we find that impossible events can produce lower error than possible events. A fixed error threshold also incorrectly flags many possible events as impossible. A model can therefore represent physical information and respond to incorrect actions without its native prediction error reliably measuring physical surprise.