# Ring Forcing：面向自回归视频扩散的精确长期记忆框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-27 08:00
- AIHOT 分数：39
- AIHOT 链接：https://aihot.virxact.com/items/cmtgx3e710cinrochn4rxzwal
- 原文链接：https://arxiv.org/abs/2608.26794

## AI 摘要

Ring Forcing 提出一种自回归视频扩散框架，通过环形结构训练策略强制模型从远距离历史中检索信息，兼顾严格历史一致性与生成多样性。引入压缩与时间步组合策略，在固定序列长度下将有效历史跨度扩展至分钟级，并借助稀疏 RoPE 机制实现灵活可扩展的记忆适配。实验表明该方法在分钟级连贯性和物体恒存性上显著优于现有方法。

## 正文

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.