[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-ant-lingbot-depth-2-0":3,"topics-all":36,"news-related-76c05fca-5560-41d3-8cdb-311556f7e845":55},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":23,"news_slug":29,"published_at":30,"created_at":31,"modified_at":32,"is_published":33,"publish_type":34,"image_url":13,"view_count":35},"76c05fca-5560-41d3-8cdb-311556f7e845","蚂蚁灵波 LingBot-Depth 2.0：把机器人深度估计从「看懂」推向「看准」","7月7日，蚂蚁集团旗下灵波科技发布空间感知模型 **LingBot-Depth 2.0** 与配套视觉基座 **LingBot-Vision**。基于 1.5 亿规模训练数据，这一代把\"机器人眼睛\"的边界又往外推了一圈。\n\n官方点出四个升级方向：边缘清晰度、细小物体识别、远距离深度估计、复杂场景鲁棒性。深度估计看似经典 CV 任务，但落到具身机器人上，任意一个模糊点都可能让抓取失败。LingBot-Vision 同步推出后，LingBot 从单一深度模型升级为\"基座+任务\"的双层架构，呼应了\"通用视觉基座 + 下游任务模型\"的行业范式。\n\n把视野放大一点：从 UFP4 量化、Ring-2.6-1T 万亿思考模型，到这次的 LingBot-Depth 2.0，蚂蚁的百灵\u002F灵波两条线已形成清晰的\"基座+具身\"双线战略。LLM 拼通用智能天花板，具身视觉拼物理世界入口——两条曲线正在被国内大厂同时拉起。\n\nLingBot-Depth 2.0 没公开论文、也没 benchmark 数字，但蚂蚁把\"看懂→看准\"作为对外口径，意味着他们已把精度视为下半场竞争的核心指标，而非又一场刷榜单的 PR 战。","https:\u002F\u002F36kr.com\u002Fnewsflashes\u002F3885019659202566","5e4fd3d1-9cb4-44a6-bae5-9ffb449c05c1",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"471c51be-e620-49df-bd6c-0b5504f53f00","ant-group",{"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},"b1853a5a-d940-42b7-94f9-0488ee3f2cf7","new-model",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"6cfba760-a5e3-425b-9e52-61b1ffed9038","en","LingBot-Depth 2.0 pushes robot depth from seeing to precision","On July 7, Ant Group's Lingbo Tech released the spatial perception model **LingBot-Depth 2.0** along with the companion vision base **LingBot-Vision**. Based on 150 million-scale training data, this generation pushes the boundary of the \"robot's eye\" another circle out. The official points out four upgrade directions: edge clarity, small-object recognition, long-distance depth estimation, and complex-scene robustness. Depth estimation looks like a classic CV task, but when it lands on embodied robots, any blur point can make a grasp fail. After LingBot-Vision's simultaneous release, LingBot upgrades from a single depth model to a \"base + task\" dual-layer architecture, echoing the industry paradigm of \"general vision base + downstream task model\". Zooming out a bit: from UFP4 quantization, the Ring-2.6-1T trillion-thinking model, to this LingBot-Depth 2.0, Ant's Bailing\u002FLingbo two lines have formed a clear \"base + embodied\" dual-line strategy. LLMs fight for the general-intelligence ceiling, embodied vision fights for the physical-world entry — two curves are being pulled up simultaneously by Chinese big tech. LingBot-Depth 2.0 didn't publish a paper or benchmark numbers, but Ant taking \"see it → see it accurately\" as the external positioning means they've treated precision as the core metric for the second-half competition, rather than yet another leaderboard-climbing PR battle.","ant-lingbot-depth-2-0","2026-07-07T04:30:00Z","2026-07-07T04:05:02.583458Z","2026-08-19T02:08:40.142862Z",true,"agent",262,[37,46],{"slug":38,"tag_slug":38,"title_zh":39,"title_en":40,"intro_zh":41,"intro_en":42,"id":43,"is_active":33,"created_at":44,"modified_at":45},"ai-for-science","AI for Science 2026：从 UniPert 到 GPT-Rosalind 的硬核进化","AI for Science 2026: from UniPert to GPT-Rosalind","生命科学、化学材料、物理世界模型——AI 正在从\"语言工具\"变成\"实验伙伴\"。本专题收录 AI 在三大科学方向的关键节点：UniPert 统一基因与化学扰动空间、GPT-Rosalind 端到端生命科学推理、达摩院 AI 智能体 28 小时找到 4 种超导新材料、Anthropic Claude Science 把工作台做成标准品。","From language tool to lab partner — AI is reshaping life sciences, chemistry\u002Fmaterials, and physical world models. This topic covers the key milestones: UniPert unifying genetic-chemical perturbation spaces, GPT-Rosalind's end-to-end life-sciences reasoning, DAMO's AI agent discovering 4 superconducting materials in 28 hours, and Anthropic's Claude Science workbench going mainstream.","988a4300-5fab-41c4-b5d8-63711a2dc757","2026-09-10T01:34:15.296649Z","2026-09-10T01:34:15.296663Z",{"slug":47,"tag_slug":47,"title_zh":48,"title_en":49,"intro_zh":50,"intro_en":51,"id":52,"is_active":33,"created_at":53,"modified_at":54},"h3-series","MiniMax H3 系列：从开源权重到 35 倍吞吐","MiniMax H3 Series: from open weights to 35x throughput","MiniMax H3 自 2026 年 8 月开源以来节奏密集：官方把生成、参考与编辑收回一个模型；ComfyUI 当天压进 RTX 3060；摩尔线程 3 小时完成国产 GPU 适配；fal 后训练版把吞吐拉到 35 倍；FastH3 蒸馏再砍推理成本。本专题持续追踪 H3 的发布—开源—蒸馏—部署全链路。","Since MiniMax open-sourced H3 in August 2026 the pace has been relentless: one unified omni-modal model, same-day ComfyUI support down to an RTX 3060, a 3-hour Day-0 port to Moore Threads GPUs, fal's post-trained H3 Max at 35x throughput, and FastH3 distillation cutting inference cost further. This topic tracks the full H3 chain — release, open weights, distillation, deployment.","83ef0daa-3c31-4cb3-86ed-e5ee58654d5f","2026-09-08T07:33:19.942193Z","2026-09-08T07:33:19.942209Z",{"items":56},[57,62,67,72,77,82],{"id":58,"title":59,"news_slug":60,"published_at":61},"c7e500ff-b7bf-4f18-a9d5-0217c2925d6b","LingBot-VA 2.0:首个\"具身原生\"视频-动作世界模型,Robbyant 拒绝\"借壳\"路线","lingbot-va-2-embodied-video","2026-07-10T12:00:00+00:00",{"id":63,"title":64,"news_slug":65,"published_at":66},"e3c0b314-d7b7-4901-b2b0-08ca5ef08ac7","GigaBrain-0.7开源:37k小时数据+三系统架构,世界模型进VLA决策回路","gigabrain-0-7-embodied-vla-open-source","2026-08-26T23:15:00+00:00",{"id":68,"title":69,"news_slug":70,"published_at":71},"aebb8a81-713a-40b7-84dd-03213a6a808c","Mistral Robostral Navigate:8B 视觉语言模型只靠单目 RGB 在 R2R-CE 反超多传感器基线","mistral-robostral-navigate-8b","2026-07-09T14:15:00+00:00",{"id":73,"title":74,"news_slug":75,"published_at":76},"5de8c559-2ab5-44a7-b1d6-97cc8e499b25","蚂蚁灵波开源 LingBot-VLA 2.0：6 万小时数据 + 17 个品牌,把具身基座卷向跨构型","ant-lingbot-vla-2-0","2026-07-08T06:30:00+00:00",{"id":78,"title":79,"news_slug":80,"published_at":81},"cec093b3-47fe-490f-b0d7-57c07ba19758","Wan-Streamer v0.1：单模型端到端 550ms 实时交互","wan-streamer-v0-1-550ms-realtime","2026-06-23T18:01:03+00:00",{"id":83,"title":84,"news_slug":85,"published_at":86},"bbf1a404-1f46-45f9-a61e-b6e210d28878","智元罗剑岚：把「部署-数据-迭代」打成飞轮，比堆参数更像具身智能的 Scaling Law","zhiyuan-luo-jianlan-flywheel-embodied","2026-06-17T06:30:00+00:00"]