[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-800de722-720c-4fd6-bc58-c09398927fd9":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},"800de722-720c-4fd6-bc58-c09398927fd9","Kairos 把世界模型做成「Native Stack」：混合时序注意力 + 误差上界，给 Physical AI 一个长程一致底座","arXiv 6 月 16 日上线的 Kairos 技术报告，是 HF Daily Papers 当周的「常驻热榜」——发布数天仍有 700+ 票，24 位作者横跨学术与产业团队。它的野心不止是又一个视频世界模型，而是把世界模型从「被动视觉生成器」拉成 Physical AI 的运营基础设施。\n\n**架构：Hybrid Linear Temporal Attention**\n\nKairos 把时序注意力拆成三种粒度的叠加：滑动窗口吃局部动力学，膨胀滑动窗口吃中等范围依赖，门控线性注意力维护持久全局记忆。三者通过时序因子化串联，作者给出形式化推导，证明这种分解对误差累积有严格上界——长程一致性第一次有了数学保障。\n\n**训练：Cross-Embodiment Data Curriculum**\n\n原生预训练范式把开放世界视频、人类行为数据、机器人交互，组织成由易到难的「发展课程」，类似婴儿先看、再模仿、最后操作。这让模型在不同 embodiment 之间共享底层物理直觉。\n\n**部署：Deployment-Aware System Co-Design**\n\n第三块强调的不是「训练多强」，而是「在服务器和消费级硬件上跑得动」。Kairos 把 rollout 延迟做成协同设计目标，让观察-动作-反馈闭环可以在边缘侧成立。\n\n实验上，Kairos 在具身世界模型、长程、动作策略三组基准同时拿到顶级性能，同时给出对得起的效率-能力折中。过去的世界模型论文大多停留在「生成像不像」，Kairos 把「状态能不能长时间不漂移」「能不能直接喂给机器人决策」摆到了与画质同等的位置。\n\n更值得行业注意的是，作者团队里既有陶大程、王晓刚等学术明星，也有产业团队署名——预示着世界模型正在走出「论文 demo 阶段」，进入「基础设施化」的下一程。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.16533","7437aeb9-930c-4866-a2e9-48003c1a792b",[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},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",{"id":18,"name":19,"slug":19,"description":13,"color":13},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":21,"name":22,"slug":22,"description":13,"color":13},"4f214978-cac1-4f39-aa4b-f92a0d0934b7","transformer","2026-06-18T22:30:00Z","2026-06-18T22:08:04.908328Z","2026-06-18T22:08:04.908336Z",true,"agent",3]