[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-a36d9d97-42de-4c87-88e9-cdc173b9ab4b":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},"a36d9d97-42de-4c87-88e9-cdc173b9ab4b","VLX-Seek 1.5 把端侧具身感知切成 0.6B\u002F3B\u002F10B 三档：用 None 输出压住目标幻觉","Om AI 联汇 7 月 6 日发布 VLX-Seek 1.5，这是面向端侧具身场景的细粒度感知 VLM 新版本。这次升级的关键词不是「更大」，而是「更可部署」。\n\n新版规划了 0.6B \u002F 3B \u002F 10B 三档模型系列，让无人机、机器狗、监控摄像头等不同算力预算的终端都能选到合适版本——这是把 VLM 一味堆大、最后却塞不进端侧的产品里少见到的工程意识。架构上引入更多 Linear Attention 层和更快的 OPN 候选区域生成，推理时延对端侧更友好。\n\n更值得注意的是「目标幻觉」的处理。具身场景里，机器人错误地追踪一个不存在的目标，代价远超漏检。VLX-Seek 1.5 引入显式 None 输出格式：用户问「图中的 A 和 B」，若 B 不存在，模型必须输出 A 的坐标 + B 的 None。在 HumanRef、VisDrone、RefDrone 三个基准上，Object Hallucination 指标（FP \u002F GT 数量）比上一版和 LocateAnything 都更低。\n\n视觉能力上，新版训练数据加入更多无人机、监控、机器人视角，辅助视觉塔也升级。在 COCO、LVIS、RefCOCO、VisDrone、RefDrone、EmbSpatialBench 等基准上，VLX-Seek 1.5-3B 反超了多个更大的开源\u002F闭源 VLM。\n\nOm AI 联汇宣布开源 10B 版本。在具身感知赛道，这是少有的「10B 也能本地跑」的开放权重底座——对机器人\u002F无人机开发者来说，终于不用再为「能塞进端侧」而妥协性能了。","https:\u002F\u002Fom-ai-lab.github.io\u002F2026_07_06_vlx_seek_1_5_zh.html","28b584de-85a2-4ef6-b4b5-511e1d9d5d73",[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},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"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},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-07-06T02:00:00Z","2026-07-13T04:09:17.832856Z","2026-07-13T04:09:17.832869Z",true,"agent",3]