放松强制：用于一致长视频生成的放松KV内存
Relax Forcing: Relaxed KV-Memory for Consistent Long Video Generation
AI总结：
本文提出Relax Forcing机制，通过结构化时间内存提升长视频生成的运动动态和时间一致性，减少注意力开销。
摘要：
自回归（AR）视频扩散最近 emerged 作为长视频生成的有希望的范式，能够超越双向模型的限制进行因果合成。为了解决训练-推理不匹配问题，一系列自我强制策略被提出，通过在训练期间将模型条件于其自身预测来提高rollout稳定性。尽管这些方法显著缓解了暴露偏差，但将生成扩展到分钟级时间范围仍具有挑战性，因为存在渐进的时间退化。在本文中，我们表明，这一限制并非主要由内存不足引起，而是由推理过程中如何利用时间内存所导致。通过实证分析，我们发现增加内存并不总是能提高长时间范围生成，而且历史上下文的时间放置显著影响运动动态，而视觉质量基本不变。这些发现表明时间内存不应被视为同质缓冲区。受此启发，我们引入了Relax Forcing，一种结构化的时间内存机制用于AR扩散。而不是关注密集生成的历史，Relax Forcing将时间上下文分解为三种功能角色：Sink用于全局稳定性，Tail用于短期连续性，以及动态选择的History用于结构运动指导，并只选择最相关的过去信息。这种设计在扩展期间减轻了误差积累，同时保持了运动演变。在VBench-Long上的实验表明，Relax Forcing提高了运动动态和整体时间一致性，同时减少了注意力开销。我们的结果表明，结构化时间内存对于可扩展的长视频生成是必要的，补充了现有的基于强制的训练策略。
英文摘要：
Autoregressive video diffusion has recently emerged as a promising paradigm for long-video generation, enabling causal synthesis beyond the temporal limits of bidirectional models. Existing forcing-based training strategies reduce exposure bias by conditioning models on their own predictions during rollout, yet minute-scale generation remains challenging due to progressive temporal degradation and constrained motion evolution. In this work, we study the role of temporal KV memory during long-horizon autoregressive inference. Our analysis shows that simply retaining more historical frames does not consistently improve generation quality; instead, both the quantity and temporal placement of memory strongly affect motion dynamics. These findings suggest that temporal memory should be treated as structured context rather than a homogeneous chronological buffer. Motivated by this observation, we introduce Relax Forcing, a training-free memory mechanism for autoregressive video diffusion. Relax Forcing decomposes temporal context into three functional components: Sink frames that provide global stability, Tail frames that preserve short-term continuity, and dynamically selected History frames that supply mid-range motion structure. History frames are selected using a relaxation-based criterion that encourages alignment with global anchors while suppressing redundancy with recent context. This role-aware sparse memory design mitigates error accumulation during long-horizon rollout while preserving motion evolution and reducing attention overhead. Experiments on VBench-Long show that Relax Forcing improves long-video generation, achieving stronger motion dynamics and higher overall scores than existing autoregressive baselines. These results indicate that structured temporal memory is an effective and complementary direction for scalable long-video generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
