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

• URL: http://arxiv.org/abs/2608.26794v1
• Date: Thu, 27 Aug 2026 08:25:32 GMT
• Title: Ring Forcing: Towards Precise Long-Term Memory for Autoregressive Video Diffusion
• Abstract summary: Ring Forcing is an autoregressive video diffusion framework designed to robustly construct and precisely utilize long-term memory.<n>Our ring-structured training strategy enforces retrieval from distant history, effectively reconciling the trade-off between strict historical adherence and generative diversity.<n>We present a sparse RoPE mechanism to enable flexible, scalable memory adaptation while fully exploiting pre-trained priors.
• Score: 30.13108213701987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• MemoryWAM: Efficient World Action Modeling with Persistent Memory [80.90899269062128]
  We introduce MemoryWAM, a world action model with efficient persistent memory.<n>A tailored attention mechanism enables retrieval of both detailed short-term context and compressed long-term context.<n>MemoryWAM outperforms strong vision-language-action (VLA) and WAM baselines in both simulation and the real world.
  arXiv  Detail & Related papers  (2026-06-18T17:59:51Z)
• REAL: A Reasoning-Enhanced Graph Framework for Long-Term Memory Management of LLMs [17.526686616588794]
  Large Language Models (LLMs) are increasingly expected to interact with users over long time horizons.<n>LLMs cannot retain all past interactions, making long-term memory management essential for storing, updating, and retrieving historical information.<n> REAL constructs long-term conversational memory as a temporal and confidence-aware directed property graph.
  arXiv  Detail & Related papers  (2026-06-09T10:53:10Z)
• FadeMem: Distance-Aware Memory Consolidation for Autoregressive Video Diffusion [59.207505503284715]
  FadeMem organizes historical KV blocks into a temporal hierarchy under a fixed cache budget.<n>New history is inserted as fine-grained entries, while older adjacent entries are progressively merged.<n>Experiments show improved subject consistency, background stability, and temporal coherence over existing bounded-cache strategies.
  arXiv  Detail & Related papers  (2026-06-09T10:22:18Z)
• DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory [44.72702042082084]
  We move beyond 3D memory and coarse frame-level implicit modeling, and propose a fine-grained, learnable, and scalable memory for consistent world generation.<n>By ensuring precise and efficient long-term memory, DecMem enables minute-level controllable long-term video generation with high-quality extrapolation.
  arXiv  Detail & Related papers  (2026-05-29T14:17:59Z)
• Relax Forcing: Relaxed KV-Memory for Consistent Long Video Generation [73.84423888025171]
  Autoregressive (AR) video diffusion has recently emerged as a promising paradigm for long video generation.<n>We show that extending generation to minute-scale horizons remains challenging due to progressive temporal degradation.<n>We introduce Relax Forcing, a structured temporal memory mechanism for AR diffusion.
  arXiv  Detail & Related papers  (2026-03-22T18:59:24Z)
• Context Forcing: Consistent Autoregressive Video Generation with Long Context [74.08361435348873]
  We propose textbfContext Forcing, a framework that trains a long-context student via a long-context teacher.<n>By ensuring the teacher is aware of the full generation history, we eliminate the supervision mismatch.<n>Our method enables effective context lengths exceeding 20 seconds -- 2 to 10 times longer than state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-02-05T18:58:01Z)
• Mixture of Contexts for Long Video Generation [72.96361488755986]
  We recast long-context video generation as an internal information retrieval task.<n>We propose a simple, learnable sparse attention routing module, Mixture of Contexts (MoC), as an effective long-term memory retrieval engine.<n>As we scale the data and gradually sparsify the routing, the model allocates compute to salient history, preserving identities, actions, and scenes over minutes of content.
  arXiv  Detail & Related papers  (2025-08-28T17:57:55Z)
• Long-Context State-Space Video World Models [66.28743632951218]
  We propose a novel architecture leveraging state-space models (SSMs) to extend temporal memory without compromising computational efficiency.<n>Central to our design is a block-wise SSM scanning scheme, which strategically trades off spatial consistency for extended temporal memory.<n>Experiments on Memory Maze and Minecraft datasets demonstrate that our approach surpasses baselines in preserving long-range memory.
  arXiv  Detail & Related papers  (2025-05-26T16:12:41Z)
• MeMSVD: Long-Range Temporal Structure Capturing Using Incremental SVD [27.472705540825316]
  This paper is on long-term video understanding where the goal is to recognise human actions over long temporal windows (up to minutes long) We propose an alternative to attention-based schemes which is based on a low-rank approximation of the memory obtained using Singular Value Decomposition. Our scheme has two advantages: (a) it reduces complexity by more than an order of magnitude, and (b) it is amenable to an efficient implementation for the calculation of the memory bases.
  arXiv  Detail & Related papers  (2024-06-11T12:03:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.