Surprise Forcing: What to Remember, When to Skip in Long Video Generation
Streaming autoregressive diffusion makes minute-scale video synthesis practical, but its bounded context and fixed denoising schedule allocate resources uniformly across a highly non-stationary sequence. A rolling key-value cache forgets distant visual evidence even when that evidence remains important, while every generated chunk receives the same number of denoising passes irrespective of its actual difficulty. We introduce Surprise Forcing, a training-free framework that treats both limitations as online resource-allocation problems. A Surprise-Gated Memory Bank summarizes evicted frames with value-token descriptors, evaluates them using complementary global-deviation and nearest-neigh...
| Authors | Shuwei Shi, Zhen Li, Muyao Niu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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