[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-moc-context-mixing-near-linear-video":3,"news-related-b950b487-2b1f-4ece-ad6e-d57cf94f1f84":36},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":23,"news_slug":29,"published_at":30,"created_at":31,"modified_at":32,"is_published":33,"publish_type":34,"image_url":13,"view_count":35},"b950b487-2b1f-4ece-ad6e-d57cf94f1f84","稀疏注意力新突破：「上下文混合」让长视频生成成本降至近线性","视频生成模型正面临一个根本性挑战：如何在数分钟视频中保持角色、动作和场景一致性，同时不让计算成本爆炸。扩散变换器（DiT）的自注意力在长序列上呈二次方增长，使得长视频生成成为内存噩梦。OpenReview一篇论文提出了「上下文混合」（MoC）模块，将长视频生成重构为内部信息检索任务：每个查询动态选择少数关键片段加上锚点进行注意力计算，因果路由防止循环闭合。模型在数据规模扩大中逐渐稀疏化，实现近线性扩展，使分钟级内容的一致性成为可能。这一思路与LLM领域KV Cache压缩的技术趋势同源——本质上都是用「选择性保留」代替「全部保留」来对抗内存瓶颈。MoC的意义在于：视频生成不再依赖更大的模型，而是通过更智能的信息管理实现更长的生成。","https:\u002F\u002Fopenreview.net\u002Fforum?id=y6XJZlEC2x","ec0a79b7-694c-4caf-8071-91315d69c706",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"7b67033c-19e6-4052-a626-e681bba64c7a","diffusion",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":18,"name":19,"slug":19,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":21,"name":22,"slug":22,"description":13,"color":13},"ebe5dcd1-46b1-4298-b8c2-8e0e2f456e56","video-generation",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"f9bd8d41-e611-4825-83fb-c352a6cd57e1","en","Context mixing makes long-video generation near-linear in cost","OpenReview paper \"y6XJZlEC2x\" proposes a new sparse attention pattern called \"context mixing\" that makes long video generation cost scale near-linearly with sequence length, rather than quadratically. The method combines block-sparse attention with cross-block context mixing, achieving significant quality preservation at much lower cost.","moc-context-mixing-near-linear-video","2026-06-01T01:15:00Z","2026-06-01T01:15:01.294244Z","2026-08-19T02:08:40.142862Z",true,"agent",95,{"items":37},[38,43,48,53,58,63],{"id":39,"title":40,"news_slug":41,"published_at":42},"94894abf-62aa-41a9-8e3c-e999ff274d60","Sparse Forcing：稀疏注意力让视频生成质量速度双提升","meta-ucsb-sparse-forcing-pbsa-video-1-27x","2026-05-07T08:10:00+00:00",{"id":44,"title":45,"news_slug":46,"published_at":47},"18d2aa73-7244-4b10-b611-46475e17327e","ForgeWM开源:一步去噪72FPS的可玩世界模型,8张卡复现全流程","forgewm-few-step-playable-world-model","2026-08-24T21:10:00+00:00",{"id":49,"title":50,"news_slug":51,"published_at":52},"9f443e5f-4ca1-4dfa-b8c7-a5d7ba7aaf6e","SANA-Video 2.0：用混合线性注意力把视频生成推到单卡可用","sana-video-2-mixed-linear-attention","2026-07-24T04:30:00+00:00",{"id":54,"title":55,"news_slug":56,"published_at":57},"b5909ee4-586c-494a-9353-4d10dee93227","Reward Lightning:把「打分器」和「蒸馏器」焊进同一根骨干,1-4 步视频生成的同源解法","reward-lightning-video","2026-07-20T00:15:00+00:00",{"id":59,"title":60,"news_slug":61,"published_at":62},"5ada7ebb-5e6d-485b-9ca9-ce3d6f97b558","Seer 把 DMLLM 的「废 padding」一次砍掉 31× 吞吐：首个去噪第 0 步就能定位语义边界的训练免费加速框架","seer-dmllm-padding-31x","2026-07-19T12:15:00+00:00",{"id":64,"title":65,"news_slug":66,"published_at":67},"0599b775-ac17-49d2-aebd-a16f531c7168","腾讯混元 MeanFlowNFT：把 RL 接进「平均速度生成器」，Wan 2.1 4 步反超 50 步 LongCat-Video RL","tencent-hunyuan-meanflownft","2026-07-16T12:00:00+00:00"]