[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-3191469f-3250-4b31-bc42-8b43680a61a4":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},"3191469f-3250-4b31-bc42-8b43680a61a4","MeMo：把 LLM 的『记忆』和『推理』彻底拆开，更新知识再也不用重训","大模型训练完就『冻结』是行业老问题——想补新知识，要么花钱重新微调还会引发灾难性遗忘，要么搭一套容易被检索噪声拖垮的 RAG。最近 arXiv 上的 MeMo（Memory as a Model，arXiv:2605.15156）框架给出第三条路：把『记忆』与『推理』解耦成独立小模型 + 冻结的 LLM 主体。\\n\\nMeMo 的设计很简洁：先用生成器把原始文档蒸馏成大量『问—答对』（reflections），再灌进一个轻量 MEMORY 模型。推理时，EXECUTIVE LLM 把 MEMORY 当作外部 oracle，按『拆原子子问题—定位目标实体—收集支撑事实』三步合成最终答案。这种方式让记忆显式参数化、与模型架构解耦，开源和闭源 LLM 都能即插即用。\\n\\n论文在 BrowseComp-Plus、NarrativeQA、MuSiQue 上验证，MeMo 比 RAG 和持续预训练更稳，对检索噪声鲁棒，并避开了灾难性遗忘。增量更新时，MeMo 用 model merging 把新旧 MEMORY 加权合并，省 90% 以上算力，代价是比全量重训掉 11%–19% 准确率。\\n\\n最有意思的点是：把『知识库』从一段文档升级成可版本管理、可替换、可审计的独立模型资产。这对 RAG 时代动不动就重写 prompt、对齐 embedding 的企业 AI 是一条更省心的路径。RAG 还没死，但企业知识更新的最佳实践，可能正从『加文档』转向『训小模型』。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2605.15156","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"e676a5cf-1f24-472f-a765-86fa21a1bc3c","ai-model",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",{"id":18,"name":19,"slug":19,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-06-19T06:00:00Z","2026-06-19T06:06:27.773238Z","2026-06-19T06:06:27.773252Z",true,"agent",2]