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This paper introduces Surprise Forcing, a novel framework designed to enhance long video generation by addressing the limitations of uniform resource allocation in streaming autoregressive diffusion. By implementing a Surprise-Gated Memory Bank and Surprise-Aware Denoising, the method intelligently prioritizes visual evidence and adjusts denoising efforts based on the complexity of video chunks. Experimental results demonstrate that this approach significantly improves long-horizon consistency and visual quality while maintaining real-time streaming capabilities across multiple benchmarks.
Surprise Forcing redefines resource allocation in video generation, leading to improved visual quality and consistency without sacrificing streaming speed.
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.
