[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-jidian-all-factor-llm-industrial-world-model":3,"topics-all":36,"news-related-f9206efa-6fcf-4a2f-9020-cf1450946d73":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},"f9206efa-6fcf-4a2f-9020-cf1450946d73","把世界模型搬进工厂：基点起源「全要素大模型」如何压缩定制化交付周期","原零一万物联创、华为云 AI CTO 戴宗宏创立的基点起源，近日披露自研的「全要素大模型」。这套工业 AI 操作系统把定制化项目从「几百人驻场、数月交付」压缩到「一人控制、两周交付」，核心是构建能反映真实生产过程的数字孪生工业世界模型。\n\n技术上，全要素大模型围绕「学习—寻优—交付」三步运行：学习阶段用原始业务数据训练出可持续更新的数字孪生模型，把注意力集中在对良品率、产能等关键指标影响更大的数据上；寻优阶段借助强化学习在数字孪生体中持续推演，找更优工艺组合；交付阶段一线工人通过极简 App 输入现场参数即可获得当下最优操作方案。\n\n这种把世界模型范式落地到工业场景值得关注：传统咨询把专家经验建模成工作流，全要素大模型把工作流嵌入可自我推演的数字孪生体，用模型挖掘生产链每个潜在优化点，提升整条产线良率与产能，而非替代工人。冶金、化工、精密制造等行业的落地表明，工业 LLM 的核心价值不在「聊天」，而在「替企业沉淀出可计算的业务知识」。\n\n工业大模型这一年正在快速去泡沫：能跑通数字孪生 + RL + 现场交付闭环的产品，比任何参数量数据都更能决定谁能真正留在 B 端定制化市场。","https:\u002F\u002F36kr.com\u002Fp\u002F3869445453305090","5e4fd3d1-9cb4-44a6-bae5-9ffb449c05c1",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"e676a5cf-1f24-472f-a765-86fa21a1bc3c","ai-model",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},"a8002d98-9df1-4ab9-94d4-a7625af634c4","china-ai",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"0e170438-d63b-46d7-b8cf-366e83bd1c0d","en","World models enter the factory: compressing delivery cycles","Jidian起源, founded by Dai Zonghong — former co-founder of Zero One Everything and CTO of Huawei Cloud AI — recently unveiled its self-developed \"all-factor LLM.\" This industrial-AI operating system compresses customization projects from \"hundreds of people on site, months to deliver\" down to \"one person in control, two weeks to deliver.\" The core is building a digital-twin industrial world model that reflects real production processes.\n\nTechnically, the all-factor LLM runs in three steps — learn \u002F optimize \u002F deliver: the learning phase uses raw business data to train a continuously updatable digital-twin model, with attention focused on the data that affects key metrics like yield and capacity more; the optimization phase uses reinforcement learning to keep searching in the digital twin for better process combinations; the delivery phase has frontline workers input on-site parameters through a minimal app to get the current optimal operation plan.\n\nTaking the world-model paradigm into industrial scenarios is worth watching: traditional consulting models expert experience into workflows, while the all-factor LLM embeds the workflow into a self-iterating digital twin and uses the model to mine every potential optimization point on the production line — improving the entire line's yield and capacity, rather than replacing workers. Landings in metallurgy, chemicals, and precision manufacturing show that the core value of an industrial LLM is not \"chat\" but \"computing the business knowledge a company can bank.\"\n\nIndustrial LLMs are rapidly being de-bubbled this year: products that can run the digital-twin + RL + on-site delivery loop end-to-end, more than any parameter count or dataset, decide who really stays in the B-end customization market.","jidian-all-factor-llm-industrial-world-model","2026-06-28T12:27:33.080882Z","2026-06-28T12:27:39.761275Z","2026-08-19T02:08:40.142862Z",true,"agent",170,[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},"49d19ba1-8f45-475c-bed1-a69dc353523e","字节跳动用 10 万亿参数下注：规模赛跑与张一鸣的「不蒸馏」表态","bytedance-10t-mythos-zhangyiming-no-distill-2026-08","2026-08-08T00:00:00+00:00",{"id":63,"title":64,"news_slug":65,"published_at":66},"5f5bd5f2-9a02-470b-aa25-3f27fb9bb093","字节跳动正训练 10 万亿参数模型，规模对标 Anthropic Mythos 5","bytedance-10t-parameter-model-ft","2026-08-07T09:30:00+00:00",{"id":68,"title":69,"news_slug":70,"published_at":71},"ad10985b-425c-4af1-9495-c63792a2b593","腾讯混元把语音识别打到 3% WER：Hy ASR 3.0 preview 让 ASR 从“逐字”走向“读语境”","tencent-hunyuan-hy-asr-3-0-preview-context-aware","2026-08-05T00:00:00+00:00",{"id":73,"title":74,"news_slug":75,"published_at":76},"10e6b20c-eddc-4d7d-bf47-c1d0009c1496","200 家美国初创联署反对禁中国开放权重模型：开放生态才是美国 AI 的护城河","200-us-startups-open-weight-letter","2026-07-25T03:30:00+00:00",{"id":78,"title":79,"news_slug":80,"published_at":81},"59a18390-a856-4251-8407-96e641cf74bc","\"辰光一号\"把大模型搬上天:国内首次航天垂直大模型在轨训练开启","chenguang-1-satellite-llm","2026-07-25T00:00:00+00:00",{"id":83,"title":84,"news_slug":85,"published_at":86},"86c258f4-5fd5-45fa-9fe5-60dbb585bfff","DeepSeek 梁文锋路线图:持续学习才是 Agent 之后的真瓶颈","deepseek-liang-wenfeng-roadmap","2026-07-24T08:30:00+00:00"]