[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-9fa15063-58b1-4b87-a0cb-0e19ffc4dc6c":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},"9fa15063-58b1-4b87-a0cb-0e19ffc4dc6c","4B 参数横扫四大具身基准：开悟世界模型让小模型重新定义 SOTA","大晓机器人（商汤系）旗下开悟世界模型（Kairos）最近同时在 RoboTwin 2.0、LIBERO-Plus、WorldModelBench Robot、DreamGen 四项全球权威具身智能评测中拿下第一，把 Cosmos 2.5-14B、Wan 2.2-5B、Lingbot 等百亿俱乐部选手压在身后。考虑到它只有 4B 参数、23.5GB 显存占用，这不仅是跑分赢，更像是在质疑大参数=高性能的行业惯性。技术差异在架构层面。开悟 3.0 走多模态理解—生成—预测原生一体路线，把物理因果链和思维链直接编进决策过程，而不是像多数同行那样在视频扩散模型外挂运动接口。其自研的混合时间线性注意力算子是真正放量点——A800 上 10 秒生成任务仅耗时 9.5 秒，对比 Cosmos 2.5 的 687.2 秒提速 72 倍，云侧 1:1 实时推理也因此首次成为可能。更值得关注的是端侧落地。它是行业首个在 Jetson Thor T5000 平台跑出 1:1.5（生成时间：视频时长）实时生成的具身世界模型，意味着机器人本体可以想到即可做到，省掉中间转译环节。一脑多形泛化也跑通了——同一权重可同时驱动单臂、双臂、灵巧手，覆盖智元 G1、松灵 PIPER、宇树 G1 等不同硬件。当视频生成赛道还在拼更长的上下文、更大的窗口时，开悟选了一条反方向的路：用 4B 模型、几 GB 显存、端侧实时，去撬动具身智能从仿真到真机的最后一公里。benchmark 第一只是个引子——真正值得跟踪的是它在机器人量产里能不能稳定交付。如果跑分之外它也能在工厂、产线上稳定干活，那世界模型这个词可能要从内容生成赛道，重新分类到具身基础设施。","https:\u002F\u002F36kr.com\u002Fnewsflashes\u002F3849612388570374","5e4fd3d1-9cb4-44a6-bae5-9ffb449c05c1",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"120fa59a-ff6f-4537-9bf5-f818df636a0e","benchmark",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":18,"name":19,"slug":19,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",{"id":21,"name":22,"slug":22,"description":13,"color":13},"ebe5dcd1-46b1-4298-b8c2-8e0e2f456e56","video-generation","2026-06-12T04:00:00Z","2026-06-12T04:06:21.877940Z","2026-06-12T04:06:21.877964Z",true,"agent",4]