视频生成想快，反而该把窗口调大
交互式视频生成一直靠小窗口来压低延迟，但这篇发现：窗口越大，画质越好、GPU 利用率越高，而延迟只多一点点。他们提出一种「投机采样」:把大窗口的预测结果缓存下来，条件没变就复用，先交已确定的部分、未来还能改。这打破了「小窗口才能快」的硬约束，让窗口大小从延迟瓶颈变成画质和速度的调节旋钮。它不是你明天能用上的东西，但指向一个反直觉的方向：在交互式视频里，快不一定来自小。
📄 原文摘要(英文)
Interactive video generation commonly relies on small bidirectional chunks to reduce response latency. Yet our preliminary experiments show that larger chunks improve video quality and better utilize GPU parallelism, allowing more latents to be sampled with only a modest increase in sampling time. Motivated by these observations, we propose Speculative Sampling, a strategy that combines the modeling power of large bidirectional windows with the low-latency interaction of small ones. It expands the modeling window by appending noise latents as padding and caches the resulting predictions for reuse when the condition remains unchanged. Predictions are delivered incrementally, while the uncommitted future remains replaceable. Our latency analysis shows that this removes the waiting-time lower bound imposed by chunk boundaries, shifting the optimization focus from reducing chunk size to accelerating per-chunk sampling. Bidirectional window size thus becomes a decision variable for jointly optimizing visual quality and sampling time, rather than a necessary constraint on low-latency interaction. This opens a path to serving large bidirectional models in interactive streaming systems without complex streaming post-training.