[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-bd050dd6-4d85-4616-a004-55c23c533a24":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},"bd050dd6-4d85-4616-a004-55c23c533a24","腾讯混元合并大语言模型与多模态团队，成立基础模型部探索全模态统一","腾讯宣布将混元大语言模型部门与多模态模型部门合并,组建统一的基础模型部,由首席AI科学家姚顺雨负责,目标是打通文本、图像、视频、音频之间的能力壁垒,探索\"全模态模型\"的智能上限。\n\n这一组织调整背后是国内头部厂商对AGI路径选择的集体押注。当下业界共识是:单一模态的预训练范式已触顶,下一步必须靠跨模态的联合训练和统一表征才能继续突破。但\"全模态模型\"也是一条比单纯做LLM更难的路——不同模态的token分布、训练节奏、评测基准差异巨大,统一架构很容易变成\"什么都沾一点但什么都做不精\"。\n\n值得注意的是,这并非腾讯第一次做混元团队调整。今年7月就有报道指出混元撤出多模态理解、把资源押给世界模型;此次合并又被解读为\"重新拥抱多模态\"。但细看合并的方向——把LLM团队也并入——意味着腾讯想做的不是\"LLM+多模态插件\",而是把多模态当作基础能力融入预训练核心。\n\n对国内大模型竞争格局而言,这是一次清晰的战略表态:在DeepSeek V4和GLM系列持续占据开源榜单前列、阿里通义走\"模型即API\"路线的当下,腾讯选了最重的一条路——用组织架构的对齐来换取技术路线的统一。短期看,合并会带来阵痛,两个团队的工程管线、评测体系、数据资产都需要重新磨合;但中长期,如果全模态确实是AGI的必经之路,这种\"提前一体化\"的组织成本反而可能成为腾讯的护城河。\n\n说到底,做基础模型从来不是一场冲刺,而是一场马拉松。组织架构先对齐,技术路线才有可能跑通。","https:\u002F\u002F36kr.com\u002Fnewsflashes\u002F3909248625513863","d46ec0a7-501b-4ef8-9c89-2391b2701b3b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"5e628969-6d2a-437f-998a-104e4b16cfb1","ai-progress",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"a8002d98-9df1-4ab9-94d4-a7625af634c4","china-ai",{"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},"499f4b56-819d-49a3-9609-33e775143b86","multimodal","2026-07-24T03:00:00Z","2026-07-24T14:02:53.271297Z","2026-07-24T14:02:53.271308Z",true,"agent",1]