Ring Forcing: Towards Precise Long-Term Memory for Autoregressive Video Diffusion

2026-08-27

AI Summary

本文针对视频生成模型缺乏长期记忆的问题，提出了Ring Forcing框架，通过环形训练策略和压缩-时间步组合策略增强模型的长期记忆能力和对象持久性。

Abstract

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.

Problem

• long-term memory
• object permanence
• memory capacity

Innovation

• Ring Forcing
• long-term memory
• autoregressive video diffusion
• sparse RoPE
• memory capacity