[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-byte-exact-kv-cache-grafting":3,"news-related-7f723663-8405-43aa-b31e-73efd714fa97":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},"7f723663-8405-43aa-b31e-73efd714fa97","KV-Cache Grafting：冻结权重，Gemma-4-12B AIME 80%→93.3%","「冻结权重也能涨点」:Byte-Exact KV-Cache Grafting 把 Gemma-4-12B 在 AIME 上从 80% 推到 93.3%\n\nSietse Schelpe 7 月 15 日挂在 arXiv(2607.14431)的论文给出了一种看起来「不科学」却极其暴力的玩法:不改一行权重,直接把 frozen LLM 拔高到大一号的水平。核心是 byte-exact KV-cache grafting——把推理中的 KV 状态当成「验证过的知识工件」,下次会话时 byte-for-byte 嫁接回去。\n\n论文最硬核的承诺是 bit-exact 可复现:固定配置下,grafted logits 与重跑结果 SHA-256 完全相等,KL 散度 = 0,50 样本 argmax 一致率 100%。这种精确手术成立的前提是 floating-point rotary encoding 下,own-position graft 是唯一数值精确的操作点。\n\n三组实测都很炸:① Gemma-4-12B 在 AIME 2025 从 80.0% → 93.3%,超过自身 77.5% 基线,压过 31B 同门 89.2%;② 8 道基模型 401,026 token 解不出的题,graft 后只需 61 token,token 减少 6574 倍,能耗约 1\u002F8700;③ 同一份 byte-exact 仓库把可用上下文从 32,768 拉到 2,854,766 token,零额外显存,跨同架构机器 byte-identical 迁移。\n\n价值在于给行业一个新能力杠杆:不再只依赖训练侧,可把验证过的推理轨迹当永久缓存,在新会话中零成本调用。KV-cache 从「显存负担」变成「可重放能力资产」,与 prompt cache、KV cache compression 并列成第三条独立工程范式。当然限制也明确——引擎闭源、所有数字靠 committed hash 自证、graft 不能跨架构直接用。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.14431","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",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"f17dfcb8-5c62-442f-9092-937a1fbbcd95","en","KV-Cache Grafting: frozen weights, AIME 80% to 93.3%","\"Frozen weights can still improve\": Byte-Exact KV-Cache Grafting pushes Gemma-4-12B from 80% to 93.3% on AIME. Sietse Schelpe's paper, posted to arXiv (2607.14431) on July 15, presents a seemingly \"unscientific\" but extremely violent trick: change zero weights, but lift a frozen LLM directly to a larger one's level. The core is byte-exact KV-cache grafting — treating inference-time KV state as a \"verified knowledge artifact\" and grafting it byte-for-byte back into the next session. The paper's hardest promise is bit-exact reproducibility: under a fixed configuration, the grafted logits and a re-run result have identical SHA-256 hashes, KL divergence = 0, and 50-sample argmax agreement reaches 100%. The premise of this precise surgery is that under floating-point rotary encoding, own-position graft is the only numerically exact operation point. All three experimental results are explosive: ① Gemma-4-12B on AIME 2025 goes from 80.0% → 93.3%, exceeding its own 77.5% baseline and beating the 31B sibling (89.2%); ② 8 problems the base model couldn't solve in 401,026 tokens take only 61 tokens after graft — a 6574x token reduction, with energy consumption of about 1\u002F8700; ③ the same byte-exact repository lifts usable context from 32,768 to 2,854,766 tokens, with zero extra memory, and byte-identical transfer across same-architecture machines. The value is a new capability lever for the industry: instead of relying only on the training side, verified inference trajectories can be treated as permanent caches and invoked at zero cost in new sessions. KV-cache shifts from \"VRAM burden\" to \"replayable capability asset\", forming a third independent engineering paradigm alongside prompt cache and KV cache compression. The limitations are also explicit — closed-source engine, all numbers self-certified via committed hash, graft cannot directly cross architectures.","byte-exact-kv-cache-grafting","2026-07-17T06:20:00Z","2026-07-17T06:18:47.387103Z","2026-08-19T02:08:40.142862Z",true,"agent",151,{"items":37},[38,43,48,53,58,63],{"id":39,"title":40,"news_slug":41,"published_at":42},"3af7d9f7-9cb3-43a3-a338-00d716c8053e","JoLT 用 Tucker + JL 残差把 KV 缓存压到 1\u002F3：让长上下文 LLM 推理不再被显存卡脖子","jolt-tucker-jl-kv-cache","2026-07-15T02:18:00+00:00",{"id":44,"title":45,"news_slug":46,"published_at":47},"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","depthweave-kv-1-8-compression","2026-07-07T15:00:00+00:00",{"id":49,"title":50,"news_slug":51,"published_at":52},"ddb7bc6c-6b6e-4797-ab76-d1aeab5a3002","压缩得好≠部署得好:树莓派实测边缘 LLM,LoRA恢复模型100题押97个同答案","edge-llm-compression-raspberry-pi","2026-08-23T13:30:00+00:00",{"id":54,"title":55,"news_slug":56,"published_at":57},"183fb3be-e062-47e7-9591-7c2372e116c1","LLM 蒸馏的显存瓶颈不只在教师模型：离线 Top-K 与分块 KL 把长上下文训练装回单卡","llm-distillation-offline-top-k-chunked-kl","2026-08-05T20:08:13+00:00",{"id":59,"title":60,"news_slug":61,"published_at":62},"d2c56430-f9a7-4844-bfab-a8651279a70c","ResKV 不再把 KV 缓存压缩等同于删词：给被淘汰的信息留一份残差账本","reskv-residual-kv-cache-compression","2026-08-03T10:43:23+00:00",{"id":64,"title":65,"news_slug":66,"published_at":67},"bffbd811-83a4-455a-a441-386dde3661c5","多模态 LLM 边缘推理:压缩、MoE 路由与量化「互锁」才是真战场","multimodal-llm-edge-interlock","2026-07-26T07:00:00+00:00"]