[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-ea5c9441-3e40-4116-8103-dfc327a54ffc":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},"ea5c9441-3e40-4116-8103-dfc327a54ffc","DepthWeave-KV:跨层残差因子化 + token 路由器,64K 上下文 KV 缓存压到 1\u002F8","长上下文 LLM 推理的瓶颈正在从「算得动」转向「放得下」——KV 缓存占据的显存随上下文长度线性增长,是阻碍百万 token 推理的硬卡点。Anna Cordoba 等人在本周公开的 arXiv 论文 DepthWeave-KV(arXiv:2607.06523)没有走「整层一刀切」的常规压缩路径,而是把相邻 Transformer 层的 Key\u002FValue 状态用一组共享的低秩通道基底做因子化,再在 token 维度留出轻量残差。\n\n真正的关键设计是 token 条件路由:对承载指令、承担检索任务的关键 token 分配更高重建秩,普通 token 大幅压缩;同时从 attention 输出端引入无标定在线误差探针,生成过程中实时调整压缩强度,无需重训基模。配套的融合 CUDA 内核把基查表、残差反量化、注意力投影合在一起,降低解码期访存。\n\n在 LongBench、Needle-in-a-Haystack、L-Eval、长篇 QA 与摘要任务上,DepthWeave-KV 在 64K 上下文拿到了 8.3× KV 内存减量和 72.8 tokens\u002Fs 的吞吐,任务质量逼近全量缓存,并优于已有压缩方案。\n\n从「统一预算」走向「逐 token 自适应」是 KV 压缩的正确方向——压缩不是把所有人压扁,而是给关键 token 留出通道。DepthWeave-KV 把这套直觉工程化,为生产级长上下文服务提供了一条可落地的路径。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.06523","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"7ac06d8e-b074-4147-abfc-ffaa4c6b8744","ai-efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"2d9c2fb0-2be5-4ad1-aedb-e9747addf355","compression",{"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},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-07-07T15:00:00Z","2026-07-08T22:15:00.157435Z","2026-07-08T22:15:00.157445Z",true,"agent",7]