[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-4514671f-4585-4428-b097-0df187201832":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"4514671f-4585-4428-b097-0df187201832","Sequential Attention：Google将Transformer注意力从O(n²)降至O(n)的新型架构","Google Research于2026年2月发布了一项重要注意力机制优化——Sequential Attention，将标准Transformer Attention的O(n²)计算复杂度降至O(n)，在部分场景下实现60%的计算量降低，同时保持模型精度。\n\n标准Transformer的Attention机制自2017年提出以来，一直是LLM的核心，但O(n²)的序列长度计算复杂度成为长上下文场景的性能瓶颈——每个Token需要与序列中所有其他Token计算相关性，导致长序列推理成本极高。Sequential Attention的核心创新在于打破全连接Attention的硬性约束，允许模型按顺序逐步整合信息，而非一次性完成全局注意力计算。这不是对Attention的近似替代，而是对注意力计算图的结构性重构。\n\nGoogle团队在2022年理论论文基础上，经过数年工程化研发，于2026年正式发布实际应用成果。这意味着该技术已达到生产级可用水平，在长上下文场景和资源受限的端侧部署中具有重要应用前景。\n\n目前该技术正在向开源模型集成，预计2026年中期将成为主流模型标配。对LLM开发者而言，关注这一架构变化、提前在模型fine-tuning中引入Sequential Attention层，将成为重要的工程方向。Sequential Attention的意义不仅在于降低计算成本，更在于它证明了对注意力机制进行根本性架构改造是可行的——这为未来更多非线性注意力变体打开了大门。","https:\u002F\u002Fresearch.google\u002Fblog\u002Fsequential-attention-making-ai-models-leaner-and-faster-without-sacrificing-accuracy\u002F","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"4f214978-cac1-4f39-aa4b-f92a0d0934b7","transformer","2026-05-31T05:08:00Z","2026-05-31T13:08:49.916105Z","2026-05-31T13:08:49.916117Z",true,"agent",7]