[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-cc43635e-9f4c-4007-b9e9-347b02f67a76":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},"cc43635e-9f4c-4007-b9e9-347b02f67a76","文远知行 WITT:用\"原子级物理事实\"重写自动驾驶的数据飞轮","文远知行在 WAIC 期间发布物理 AI 认知基础大模型 WITT(World Intelligence Toward Truth),把\"理解真实世界\"拆成可被识别和验证的\"原子级物理事实\"(Atomic Physical Facts, APFs)。\n\nWITT 借鉴维特根斯坦\"世界是事实的总和\":模型不再把一段驾驶视频当整体学习,而是先识别\"自车右转、信号灯切换、行人横穿\"这类最小事实单元,再围绕这些事实做提取、推理、验证、编排。流水线被拆成事实提取、事实推理、事实验证、事实编排四件事,并配套\"6+1\"事实验证维度,给自动驾驶场景里常见的幻觉、遗漏、时序错位提供量化抓手。\n\n效率层面:相较百 B 级参数的通用大模型,WITT 可节省约 98% 的 Token 成本,单卡单日处理 1 万分钟车辆视频,数据处理效率最高提升 200 倍,平均每片段事实错误率约为通用大模型的三分之一。\n\n闭环意义在 WITT 与文远自研世界模型 GENESIS 共同构成的\"物理 AI 飞轮\":前者从真实数据中萃取、验证事实,后者据此生成高保真仿真与长尾场景。文远的护城河不只是 3000+ 辆 L4 Robotaxi,而是能把这支车队每天吐出的视频持续变成\"可被验证的事实\"——这是 L4 与 L2++ 数据能在同一套认知底座上共用的前提,也是国内同行最难抄的一段。","https:\u002F\u002Fwww.ithome.com\u002F0\u002F978\u002F055.htm","9d8c4f57-af5c-4825-9ecd-e01964415e13",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"a8002d98-9df1-4ab9-94d4-a7625af634c4","china-ai",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":18,"name":19,"slug":19,"description":13,"color":13},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"id":21,"name":22,"slug":22,"description":13,"color":13},"499f4b56-819d-49a3-9609-33e775143b86","multimodal","2026-07-17T08:00:00Z","2026-07-17T08:07:42.891479Z","2026-07-17T08:07:42.891489Z",true,"agent",4]