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.
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.
By Yixuan Ding, Jiahao Kong, Wei Huang, Ruijie Quan, Yi Yang
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.
By Dvir Samuel, Issar Tzachor, Matan Levy, Michael Green, Gal Chechik, Rami Ben-Ari
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.
By Chen Li, Peng Zhang, Hanyu Zhou, Jialong Zuo, Fei Wang, Daiguo Zhou, Nong Sang, Changxin Gao
arXiv:2605.25333v3 Announce Type: replace
Abstract: Video world models should maintain evolving states when evidence is unobserved, yet current generators often freeze hidden states upon interruption...
By Tianshuo Xu, Yichen Xie, Depu Meng, Chensheng Peng, Quentin Herau, Bo Jiang, Yihan Hu, Wei Zhan
arXiv:2609.39096v1 Announce Type: new
Abstract: Autoregressive video diffusion supports streaming generation and interactive control, but its KV cache grows with the generated history. Existing compr...
By Zeqi Xiao, Qingle Liu, Kaiwen Zhang, Yifan Zhou, Zihan Ding, Xingang Pan