Mitigating Compounding Error via Video Representation Regularization
Video diffusion-based world models enable long autoregressive video generation for robotics, autonomous driving and simulation tasks, yet sliding-window autoregressive inference suffers from severe error accumulation that degrades frame quality over time. Although this phenomenon has been widely observed, the underlying mechanism of compounding error and how to achieve stable long-horizon generation remain largely unresolved. In this paper, we investigate the internal representation dynamics of video world models and discover that compounding error is tightly coupled with dimensional collapse of hidden representations. Specifically, the effective rank of model representations sharply decr...
| Authors | Taiye Chen, Qi Zhang, Yisen Wang |
|---|---|
| Primary category | cs.CV |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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