Surprise Forcing: What to Remember, When to Skip in Long Video Generation
Surprise Forcing is a training-free framework that improves long video generation by addressing two limitations of streaming autoregressive diffusion: bounded context and fixed denoising schedule. It uses a Surprise-Gated Memory Bank to selectively retain important visual evidence and Surprise-Aware Denoising to skip denoising steps for easy chunks. Experiments show improved consistency and quality while maintaining real-time throughput.
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[Submitted on 20 Jul 2026]
Title:Surprise Forcing: What to Remember, When to Skip in Long Video Generation
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Abstract: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-neighbor novelty signals, and regulates admission through a feedback-controlled budget in normalized score space. Priority-based replacement and relevance-aware routing then keep the external memory compact and useful. In parallel, Surprise-Aware Denoising estimates chunk difficulty from the maximum adjacent-frame cosine distance after the first denoising pass and uses a local percentile scheduler to skip intermediate steps for comparatively easy chunks. Experiments on VBench, VBench-Long, and VBench-2.0 show that the proposed allocation strategy improves long-horizon consistency and visual quality while retaining real-time streaming throughput.
Comments: Technical report
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.18436 [cs.CV]
(or arXiv:2607.18436v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.18436
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shuwei Shi [view email] [v1] Mon, 20 Jul 2026 18:37:58 UTC (30,756 KB)
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