arXiv Machine Learning
Aug 10
Addressable Memory for Video World Models
arXiv:2608. 07408v1 Announce Type: cross Abstract: We study visual persistence in interactive video world models.
Teaching Video Generators to Remember: Eliciting Dynamic Memory for Out-of-Sight State Evolution
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arXiv Computer Vision
Aug 31
LayerRecall: A State-Conditioned Memory Router for Long-Horizon Consistency in Video Generation
LayerRecall is a memory router for autoregressive video diffusion that selectively retrieves and injects historical key/value states into specific layers of the model, based on the current context. It addresses the problem that existing memory mechanisms expose nonlocal history but do not guarantee effective use, by recognizing that different layers prefer current, recent, or distant context. The method, combined with Cross‑Horizon Prediction Matching, achieves state‑of‑the‑art long‑range consistency on MemoBench and MovieBench while maintaining local continuity and incurring negligible inference overhead.
arXiv AI
Jun 12
TetherCache: Stabilizing Autoregressive Long-Form Video Generation with Gated Recall and Trusted Alignment
arXiv:2606. 13035v1 Announce Type: cross Abstract: Autoregressive video diffusion models provide a natural formulation for streaming and variable-length video generation by conditioning newly generated frames on previously generated content.
Hugging Face Trending Papers
Jun 11
TetherCache: Stabilizing Autoregressive Long-Form Video Generation with Gated Recall and Trusted Alignment
Autoregressive video diffusion models provide a natural formulation for streaming and variable-length video generation by conditioning newly generated frames on previously generated content. However, extending these models to minute-level generation remains challenging: the limited KV-cache budget prevents the model from retaining the full history, while repeatedly conditioning on self-generated frames induces a context distribution shift that accumulates over time, leading to visual artifacts, quality degradation, and temporal drift.
arXiv Computer Vision
Aug 28
Tether the Subject, Release the Scene: Query-Aware Memory Routing for Long-Horizon Autoregressive Video Generation
The paper introduces TetherMem, a training‑free, query‑aware memory router designed for streaming autoregressive video models that generate long videos in chunks. By separating subject and scene queries and modulating historical access with region‑ and age‑conditioned priors, TetherMem prevents the model from anchoring the scene to stale backgrounds and viewpoints, a problem termed memory‑anchored scene under‑progression. In blinded pairwise evaluations, TetherMem outperforms eight baseline methods in overall quality and scene progression, and on full 30‑second videos it maintains background, viewpoint, and scene changes while preserving subject identity and temporal continuity.
arXiv Computer Vision
Aug 28
RECAP-Forcing: Retaining Content Appearances for Long Video Generation
RECAP-Forcing is a new method for long autoregressive video generation that addresses the memory challenge by organizing memory based on appearance novelty rather than recency. The approach retains key-value caches for newly appearing content—such as entering subjects, disoccluded regions, and new scenes—at the moment they first appear, ensuring consistent identities over time. It combines an attention sink for the initial scene with an optical-flow-based novelty bank for later frames, improving visual quality and semantic fidelity without adding learnable parameters.
arXiv Machine Learning
Jun 9
Echo-Memory: A Controlled Study of Memory in Action World Models
arXiv:2606. 09803v1 Announce Type: cross Abstract: We present \textbf{Echo-Memory}, a controlled study of memory mechanisms in action-conditioned world models.
Hugging Face Trending Papers
Jul 9
OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators
We propose OPSD-V, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR video generators can produce long videos with low latency, but still suffer from error accumulation and weakened motion dynamics during long autoregressive rollout.
arXiv AI
Jun 15
Fast Autoregressive Video Diffusion and World Models with Temporal Cache Compression and Sparse Attention
arXiv:2602. 01801v2 Announce Type: replace-cross Abstract: Autoregressive video diffusion models enable streaming generation, opening the door to long-form synthesis, video world models, and interactive neural game engines.
arXiv Computer Vision
Sep 24
The Past Frames the Future: Memory for Autoregressive Video Generation
arXiv:2609.28466v1 Announce Type: new Abstract: Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environme...
arXiv AI
1d ago
Compress to Remember: Learning Compact Memory via On-Policy Distillation for Long Video Generation
The paper introduces Prediction‑Aligned Context Compaction (PACC), a method that learns a compact memory representation for long‑video generation by distilling a frozen video generator. PACC trains a compressor to aggregate past frames into memory tokens, using the generator as both teacher and student during on‑policy distillation. Experiments on MBench and VBench‑Long show that PACC improves memory‑event coverage and consistency, achieving better scores than strong baselines and producing competitive minute‑long videos.
arXiv Computer Vision
Aug 31
Relax Forcing: Relaxed KV-Memory for Consistent Long Video Generation
The paper introduces Relax Forcing, a training‑free memory mechanism for autoregressive video diffusion that structures temporal context into Sink, Tail, and History frames. By selecting History frames with a relaxation criterion, the method reduces error accumulation and attention overhead while preserving motion dynamics. Experiments on VBench‑Long demonstrate that this structured memory improves long‑video generation quality over existing baselines.