[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-millivid-mit-hierarchical-latent-long":3,"topics-all":36,"news-related-52e9955f-50fe-4ea1-98cc-a7a442d57b71":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},"52e9955f-50fe-4ea1-98cc-a7a442d57b71","MilliVid：MIT 把「分层潜变量」装进视频扩散，长程一致性首次跑通长镜头","视频生成模型越来越强，但只要超过几十帧，几何漂移、物体「换脸」就成为通病——根源在于把整段视频摊平喂给 Transformer 时，序列长度指数级爆炸。MIT Sitzmann 实验室（Ishaan Preetam Chandratreya、David Charatan、Basile Van Hoorick 等）6 月 8 日在 arXiv 放出的 MilliVid，把这个问题拆成了两件事：第一，多尺度自编码器。把每一帧压成一组层级 token——从常规潜变量一直压缩到「每帧仅几个 token」。最粗的层级只保留场景布局、语义与对象身份；细层级再补高频外观与纹理。这样不同重要性的信息自然分层。第二，粗到细的 rollout 视频扩散。训练时先生成粗 token，再用它指导细 token 的生成；推理时模型只在「值得分配算力」的维度上做长程一致性约束。结果是在长 Minecraft 视频评测里，几何与物体持续性显著优于既有基线。比起把上下文硬塞进注意力，或用「滚动窗口」剪断长程依赖，MilliVid 的思路更接近人类视觉——先记忆骨架，再补充细节。对工业界的启发是：长视频生成不必非要 1M 上下文，用「粗到细」分配算力比堆长度更有效。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.09056","7437aeb9-930c-4866-a2e9-48003c1a792b",[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},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"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},"ebe5dcd1-46b1-4298-b8c2-8e0e2f456e56","video-generation",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"8d2cabdc-0ad0-4f40-bcd9-1f4531d1a4f3","en","MilliVid: MIT's hierarchical latents crack long-shot consistency","Video generation models keep getting stronger, but as soon as the output crosses a few dozen frames, geometric drift and object \"face-swapping\" become routine — the root cause is that flattening the entire video and feeding it to a Transformer makes the sequence length explode exponentially. MIT's Sitzmann lab (Ishaan Preetam Chandratreya, David Charatan, Basile Van Hoorick et al.) released MilliVid on arXiv on June 8, splitting the problem into two pieces. First, a multi-scale autoencoder. Each frame is compressed into a set of hierarchical tokens — from the regular latent variables all the way down to \"a few tokens per frame.\" The coarsest level retains only scene layout, semantics, and object identity; the fine levels then supplement high-frequency appearance and texture. So information of different importance naturally stratifies. Second, coarse-to-fine rollout video diffusion. During training the coarse tokens are generated first, then used to guide the generation of fine tokens; at inference the model only imposes long-horizon consistency constraints on the dimensions \"worth allocating compute to.\" The result is significantly better geometry and object persistence than existing baselines on long Minecraft video evaluation. Compared to cramming context into attention or breaking long-range dependencies with \"sliding windows,\" MilliVid's approach is closer to human vision — first remember the skeleton, then fill in the details. The lesson for industry is: long video generation doesn't necessarily need 1M context; using \"coarse to fine\" to allocate compute is more effective than stacking length.","millivid-mit-hierarchical-latent-long","2026-06-11T02:00:00Z","2026-06-11T02:14:10.080819Z","2026-08-19T02:08:40.142862Z",true,"agent",316,[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},"376751a1-de8e-4e5b-afdc-4e26d7574146","NVIDIA Long-WAM：机器人记住 19 秒，预测未来再出手","nvidia-long-wam-robot-context","2026-10-08T19:11:55+00:00",{"id":63,"title":64,"news_slug":65,"published_at":66},"e840fad5-b3a1-47cd-ac68-679d5f635dc1","世界状态交给程序管:Programmable World Model 让视频模型只管渲染","programmable-world-model-persistent-state","2026-09-10T17:10:00+00:00",{"id":68,"title":69,"news_slug":70,"published_at":71},"faab4a6c-9cb0-4f2a-a5bf-1f122306008b","Wan-Streamer v0.2：分辨率 192p→640p，保住 200ms","alibaba-wan-streamer-v0-2","2026-07-10T16:15:00+00:00",{"id":73,"title":74,"news_slug":75,"published_at":76},"aeb60d9a-6639-4669-96a4-951aadad40cb","AI 视频工具进入「全场景」分化期:6 款主流产品的技术路线对比","ai-video-tools-comparison","2026-07-08T08:00:00+00:00",{"id":78,"title":79,"news_slug":80,"published_at":81},"2657cbe0-7743-43f2-9332-ee18b84b1229","Directing the World: 中国电信 TeleAI 把自回归视频世界模型推到\"组合控制\"","teleai-directing-the-world","2026-07-01T10:30:00+00:00",{"id":83,"title":84,"news_slug":85,"published_at":86},"efa5f558-e27f-4889-bf71-74cbef874ace","即梦 AI 把 Seedance 2.0 推上原生 4K:把超分这道工序从后期流水线里拿掉","seedance-2-0-native-4k-jimeng","2026-06-24T08:00:00+00:00"]