Ring Forcing: Towards Precise Long-Term Memory for Autoregressive Video Diffusion
Summary
Ring Forcing is an autoregressive video diffusion framework that enhances long-term memory for precise minute-long video coherence through ring-structured training, history compression, and sparse rotary embeddings.
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Paper page - Ring Forcing: Towards Precise Long-Term Memory for Autoregressive Video Diffusion
Source: https://huggingface.co/papers/2608.26794
Abstract
Ring Forcing is an autoregressive video diffusion framework that improves long-term memory through ring-structured training, history compression, and sparse rotary embeddings to achieve minute-long coherence.
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; andmemory capacity, the ability to process ultra-long context and use information from distant history. Robust long-term memory requires both:object permanencewithout sufficient context handling limits the temporal scope, while long context length without permanence fails to maintain identity. To address this, we presentRing Forcing, anautoregressive video diffusionframework designed to robustly construct and precisely utilize long-term memory. Ourring-structured trainingstrategy enforces retrieval from distant history, effectively reconciling the trade-off between strict historical adherence and generative diversity. To expandmemory capacity, we introduce acompression and timestep compositionstrategy. 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 asparse RoPEmechanism to enable flexible, scalable memory adaptation while fully exploiting pre-trained priors. Extensive experiments demonstrate thatRing Forcingachieves superior minutes-long coherence andobject permanence, significantly outperforming state-of-the-art methods.
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