[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-xiaomi-mimo-v2-5-pro-1t-moe-672-toolcalls":3,"topics-all":36,"news-related-e6865eab-e2f9-451e-9823-8c336e93452a":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},"e6865eab-e2f9-451e-9823-8c336e93452a","小米MiMo-V2.5-Pro开源：万亿参数MoE+1M上下文，长程Agent能力新突破","5月9日，小米正式开源MiMo-V2.5-Pro——一款拥有1.02T总参数、42B激活参数的混合专家（MoE）大模型，基于Hybrid Attention架构，上下文窗口达100万Token。\n\n核心突破在于长程一致性。官方披露的测试显示，在需要逾千步Tool Call的复杂软件工程任务中（北京大学编译原理课程项目：用Rust从零实现完整SysY编译器），MiMo-V2.5-Pro在4.3小时内完成672次工具调用，得分233\u002F233，完美通过全部隐藏测试用例。这不是常规Benchmark跑分，而是真实的长程自主任务——模型需要持续自修正、跨阶段规划，中间任何一步的逻辑缺陷都会导致最终失败。\n\n架构层面，V2.5-Pro采用Hybrid Attention机制，将标准Transformer的自注意力与线性注意力混合，在保持全局建模能力的同时控制计算复杂度。作为MoE模型，1T总参数量中每次仅激活42B，配合1M上下文窗口，使得单次请求的计算成本远低于同等规模的Dense模型。\n\n小米同时开放了Hugging Face模型权重与API接口，开发者可直接调用。相比动辄需要数千GPU小时的封闭大模型，MiMo-V2.5-Pro让资源有限的团队也能体验到前沿的Agent能力。这不仅是模型性能的进步，更是开源生态向真正可用阶段迈进的标志。","https:\u002F\u002Fmimo.xiaomi.com\u002Fmimo-v2-5-pro","581853c1-b1f6-420b-9124-243143660e92",[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},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"0c09a47d-da04-4aaf-b904-fbaeb502fcef","en","Xiaomi MiMo-V2.5-Pro: 1T MoE, 1M context, long-horizon agents","On May 9, Xiaomi officially open-sourced MiMo-V2.5-Pro — a Mixture-of-Experts (MoE) large model with 1.02T total parameters and 42B activated parameters, based on a Hybrid Attention architecture, with a context window of 1 million tokens.\n\nThe core breakthrough lies in long-horizon consistency. Officially disclosed tests show that in complex software engineering tasks requiring over a thousand Tool Calls (Peking University compiler principles course project: implementing a complete SysY compiler in Rust from scratch), MiMo-V2.5-Pro completed 672 tool calls in 4.3 hours, scoring 233\u002F233, perfectly passing all hidden test cases. This isn't a routine benchmark score, but a real long-horizon autonomous task — the model needs to continuously self-correct, plan across stages, and any logical flaw in any step would cause final failure.\n\nArchitecturally, V2.5-Pro uses a Hybrid Attention mechanism, mixing standard Transformer's self-attention with linear attention, controlling compute complexity while preserving global modeling capability. As an MoE model, only 42B of the 1T total parameters are activated per inference, combined with a 1M context window, making per-request compute cost far lower than equivalent-scale Dense models.\n\nXiaomi simultaneously opened Hugging Face model weights and API interfaces, letting developers call them directly. Compared to closed large models that often require thousands of GPU-hours, MiMo-V2.5-Pro lets resource-constrained teams experience frontier Agent capability. This isn't just a model performance improvement, but a sign of the open-source ecosystem moving toward a truly usable stage.","xiaomi-mimo-v2-5-pro-1t-moe-672-toolcalls","2026-05-10T11:10:00Z","2026-05-10T19:07:56.071277Z","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},"b4a4434f-6b1e-4ff3-ae69-2d9e82ab3e29","小米MiMo-V2.5首度揭秘：五大推理优化技术如何实现「降价不亏本」","xiaomi-mimo-v2-5-five-inference-optimization","2026-05-31T04:00:00+00:00",{"id":63,"title":64,"news_slug":65,"published_at":66},"28c41f06-d20f-481c-b133-cd109af3aed1","答对之后停不下来:微软团队揪出在线蒸馏的 EOS 错配元凶","eos-mismatch-opd-length-inflation","2026-09-18T21:09:06+00:00",{"id":68,"title":69,"news_slug":70,"published_at":71},"4497a0c5-e9b8-42d5-8d72-cfa0a49c1fba","Mistral Small 4 加入 Firefox Smart Window：开放权重模型第一次进浏览器助手默认菜单","mistral-mozilla-firefox-smart-window-moe","2026-09-17T19:00:00+00:00",{"id":73,"title":74,"news_slug":75,"published_at":76},"2e27016d-b90e-45c7-825a-41fd1e435c80","JHU 新研究:组合持续学习机制,百任务记忆留存从 1.2% 提到 34.9%","compose-cl-long-horizon-memorization","2026-09-16T15:10:00+00:00",{"id":78,"title":79,"news_slug":80,"published_at":81},"30fca629-bace-4832-9789-b44aa8c8989d","学生团队从零训出开源 7B 模型 ZGCM-1:数学推理硬刚 235B 前沿","zgcm-1-open-7b-foundation-model","2026-09-15T19:10:00+00:00",{"id":83,"title":84,"news_slug":85,"published_at":86},"2731ed1c-17c3-4d85-9174-983cf50743e3","地铁售票机上的 AI 大考:2.6GB 端侧模型 91.32 分超 GPT-5.6,规则基线也拿 84.6","metrollm-bench-transit-kiosk-llm","2026-09-12T23:08:18+00:00"]