当潜在预测误差支持物理意外主张时——视频世界模型研究
When Does Latent Prediction Error Support Physical Surprise Claims in Video World Models?
研究表明视频世界模型的潜在预测误差反映外观和运动而非可靠测量物理意外。在IntPhys2基准上，潜在预测、掩码像素重建和简单视觉统计的比较显示，外观易于解码，V-JEPA2仅略微揭示事件是否可能。
Published on Nov 1
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Abstract
The study evaluates frozen video representations to show that prediction errors reflect appearance and motion rather than reliably measuring physical surprise.
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.
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来源: Hugging Face Daily Papers · huggingface.co