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

Scaling video generation to long durations reveals a criti-cal bottleneck: current models lack robust long-term memory. This defi-ciency can be studied along two critical aspects: object permanence, theability to precisely reproduce the appearance of objects upon re-entry;and memory capacity, the ability to process ultra-long context and use in-formation from distant history. Robust long-term memory requires both:object permanence without sufficient context handling limits the tempo-ral scope, while long context length without permanence fails to maintainidentity. To address this, we present Ring Forcing, an autoregressivevideo diffusion framework designed to robustly construct and preciselyutilize long-term memory. Our ring-structured training strategy enforcesretrieval from distant history, effectively reconciling the trade-off betweenstrict historical adherence and generative diversity. To expand memorycapacity, we introduce a compression and timestep composition strat-egy. Under fixed sequence length constraints, this method extends theeffective historical span to minutes-long durations and achieves a compre-hensive receptive field over the entire history. Furthermore, we present asparse RoPE mechanism to enable flexible, scalable memory adaptationwhile fully exploiting pre-trained priors. Extensive experiments demon-strate that Ring Forcing achieves superior minutes-long coherence andobject permanence, significantly outperforming state-of-the-art methods.