环强制：面向自回归视频扩散的精确长期记忆

摘要

将视频生成扩展到长时程暴露了一个关键瓶颈：当前模型缺乏稳健的长期记忆。这一缺陷可以从两个关键方面进行研究：物体持久性，即在物体重新出现时精确再现其外观的能力；以及记忆容量，即处理超长上下文并利用远距离历史信息的能力。稳健的长期记忆两者皆需：缺乏足够上下文处理能力的物体持久性会限制时间范围，而缺乏持久性的长上下文则无法维持身份一致性。为解决这一问题，我们提出了环状强制（Ring Forcing），一种自回归视频扩散框架，旨在稳健地构建并精确利用长期记忆。我们的环状结构训练策略强制从远距离历史中进行检索，有效调和了严格历史遵循与生成多样性之间的权衡。为扩展记忆容量，我们引入了压缩与时间步组合策略。在固定序列长度约束下，该方法将有效历史跨度扩展至数分钟时长的片段，并实现了对完整历史的全面感受野。此外，我们提出了一种稀疏RoPE机制，以在充分利用预训练先验的同时实现灵活、可扩展的记忆自适应。大量实验表明，环状强制在数分钟级别的连贯性和物体持久性方面表现优异，显著优于现有最先进方法。

English

Scaling video generation to long durations reveals a critical bottleneck: current models lack robust long-term memory. This deficiency can be studied along two critical aspects: object permanence, the ability to precisely reproduce the appearance of objects upon re-entry; and memory capacity, the ability to process ultra-long context and use information from distant history. Robust long-term memory requires both: object permanence without sufficient context handling limits the temporal scope, while long context length without permanence fails to maintain identity. To address this, we present Ring Forcing, an autoregressive video diffusion framework designed to robustly construct and precisely utilize long-term memory. Our ring-structured training strategy enforces retrieval from distant history, effectively reconciling the trade-off between strict historical adherence and generative diversity. To expand memory capacity, we introduce a compression and timestep composition strategy. Under fixed sequence length constraints, this method extends the effective historical span to minutes-long durations and achieves a comprehensive receptive field over the entire history. Furthermore, we present a sparse RoPE mechanism to enable flexible, scalable memory adaptation while fully exploiting pre-trained priors. Extensive experiments demonstrate that Ring Forcing achieves superior minutes-long coherence and object permanence, significantly outperforming state-of-the-art methods.