[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-tencent-editable-visual-design-agent":3,"topics-all":38,"news-related-a7095e70-a6c5-46b6-a616-446825bce5cf":57},{"id":4,"title":5,"summary":6,"content":7,"original_url":8,"source_id":9,"tags":10,"translations":24,"news_slug":31,"published_at":32,"created_at":33,"modified_at":34,"is_published":35,"publish_type":36,"image_url":14,"view_count":37},"a7095e70-a6c5-46b6-a616-446825bce5cf","Editable Visual Design：腾讯混元论文让 AI 设计稿带真实图层，直接拖拽改版","arXiv 2609.04034 提出用编码智能体替代扩散模型直出设计图：VLM 负责规划与审美判断，图像模型只按需生成独立素材，交付带真实文字、语义图层的 HTML，可拖拽编辑、可导出 PPT，Apache 2.0 开源仓库已近 500 星。","说一句\"给我生成一张新品海报\"，扩散模型几秒就能给出漂亮的图——但它给的是一张压平的位图：文字常常出错，想挪一个 logo、改一行价格，只能整图重画。arXiv 9 月 3 日公布的新论文《Editable Visual Design》直接把这个痛点当成靶子：论文页面挂在腾讯混元（Tencent Hunyuan）机构名下，叶俊彦（Junyan Ye）等 12 位作者署名，Hugging Face 论文页上线两天拿到 42 个赞，GitHub 仓库已冲到近 500 星。\n\n## 两难：扩散模型好看但不可改，纯代码可控但缺审美\n\n论文把现有两条路线的短板摆得很直。以 GPT-Image-2、Nano-Banana 为代表的扩散基座视觉表现力强，但端到端生成天然输出扁平位图、文字易错，没法做图层级的后期编辑；反过来，让编码智能体直接写代码画图，布局精确、图层解耦，却缺全局审美直觉，复杂视觉素材写不出来。漂亮和可编辑，一直是二选一。\n\n## 解法：VLM 当\"创意大脑\"，图像模型降级为\"素材车间\"\n\n这篇论文的分工设计很有意思：VLM 负责\"想\"——理解需求、拆任务、做审美判断；图像生成模型只在需要时出场，按需合成独立的视觉素材。整个流程走\"先想象、后行动\"的闭环：智能体先产出独立素材，再写原生 HTML\u002FCSS，对着渲染反馈反复修。关键约束是\"视觉先验、不搬像素\"——图像模型提供构图、配色、层级的艺术方向，但它的参考像素永远不进入最终交付物，排版和字体由智能体在 HTML 里重建。最终产物带真实文字和语义图层，用户在图形界面上鼠标拖拽就能改布局。\n\n## 不止能生成，还能\"回放\"\n\n另一个值得注意的细节是 Agent Design Replay：系统把智能体的创作与推理轨迹忠实记录下来，可以像职业设计师的工作过程一样重放。仓库对比表里专门列了这一项——图像生成没有创作路径可言，直接写代码只有部分可回放，而这套方案把\"设计是怎么一步步做出来的\"变成工件的一部分。仓库还内置确定性检查，覆盖画布、字体、图层契约、渲染和编辑器往返。\n\n## 落地：两个 Codex 技能，还能导出可编辑 PPT\n\n工程侧的交付是两个独立 Codex 技能：editable-design 负责从需求生成设计，产出 HTML、PNG、鼠标编辑器、图层分解动画和创作回放；html-to-pptx 则把干净的设计转成可编辑的 PowerPoint，元素可独立选中。README 推荐搭配 GPT-5.6、high 及以上推理档位使用。Gallery 给了 14 组提示词、覆盖 6 大类场景——从山柚观音茶饮海报、护肤电商主图，到\"Attention Is All You Need\"学术论文海报都在列。仓库以 Apache 2.0 开源，作者注明这是社区项目、并非官方 OpenAI 项目。\n\n## 所以呢\n\n这篇论文真正值得记的点，不是\"把图生成得更好看了\"，而是把生成模型在视觉管线里的位置降了一级：它不再负责交付，只负责供给素材和艺术方向；交付物变成了带图层契约的软件工件——可版本化、可组合、可回放。当\"设计稿\"从位图变成代码工件，修改、复用、协作才谈得上工程化。对做设计工具和 Agent 产品的团队，这个分工范式比任何单点跑分都更有参考价值。\n\n参考：arxiv.org\u002Fabs\u002F2609.04034；github.com\u002Fyejy53\u002FEditable-Design","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.04034","7437aeb9-930c-4866-a2e9-48003c1a792b",[11,15,18,21],{"id":12,"name":13,"slug":13,"description":14,"color":14},"6ad31a14-c0da-42df-81fd-564281f768db","agentic-ai",null,{"id":16,"name":17,"slug":17,"description":14,"color":14},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":19,"name":20,"slug":20,"description":14,"color":14},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",{"id":22,"name":23,"slug":23,"description":14,"color":14},"c883fd20-1d66-4fb7-9fc7-320fa7f87023","text-to-image",[25],{"id":26,"lang":27,"title":28,"summary":29,"content":30},"8951802d-96be-4c50-b698-cf2505671a5e","en","Editable Visual Design: layered HTML posters from a coding agent","Tencent Hunyuan paper turns prompts into layered, mouse-editable HTML designs via a coding agent. Open source, Apache 2.0, near 500 GitHub stars.","Generating a poster with a diffusion model takes seconds—but what you get is a flattened bitmap. Text is error-prone, and moving a logo or changing a price line means regenerating the whole image. A paper published on arXiv on Sep 3, \"Editable Visual Design\" (arXiv:2609.04034), takes this pain point head-on. The paper page is listed under the Tencent Hunyuan organization, with 12 authors led by Junyan Ye; it picked up 42 upvotes on the Hugging Face papers page within two days, and the GitHub repository has climbed to nearly 500 stars.\n\n## The dilemma: diffusion looks good but cannot be edited; pure code is controllable but lacks taste\n\nThe paper lays out the shortcomings of both existing routes. Diffusion base models such as GPT-Image-2 and Nano-Banana are visually expressive, but end-to-end generation inherently yields flattened bitmaps with error-prone text, precluding layer-wise post-editing. Conversely, having a coding agent write code to draw gives precise layout control and decoupled layers, yet suffers from a lack of global aesthetic intuition and the difficulty of coding complex visual assets. Beautiful and editable have always been an either-or choice.\n\n## The fix: the VLM as the creative brain, the image model demoted to an asset shop\n\nThe division of labor is the interesting part: the VLM does the thinking—requirement comprehension, task planning, and aesthetic judgment—while the image generation model only shows up on demand to synthesize standalone visual assets. The whole pipeline runs an \"imagine first, then act\" closed loop: the agent generates isolated assets, writes native HTML\u002FCSS, and iteratively refines the design against visual rendering feedback. A key constraint is \"visual prior, without pixel shipping\"—the image model provides art direction such as composition, hierarchy, color, and spatial relationships, but its reference pixels never enter the delivered design; typography and layout are rebuilt in HTML by the agent. The final artifact carries real text and semantic layers, and users can adjust layouts by simply dragging in a graphical interface.\n\n## Beyond generation: replayable design\n\nAnother detail worth noting is Agent Design Replay: the system faithfully records the creative and reasoning trajectory of the agent, replayable like the working process of a professional human designer. The repository comparison table lists this explicitly—image generation has no creation path, direct coding is only partially replayable, while this approach turns \"how the design was made step by step\" into part of the artifact. The repo also ships deterministic checks covering the canvas, fonts, layer contracts, rendering, and editor round trips.\n\n## Shipping: two Codex skills, plus editable PPT export\n\nOn the engineering side the delivery is two independent Codex skills: editable-design generates polished designs from a brief, producing HTML, PNG, a mouse editor, an animated layer breakdown, and Agent Design Replay; html-to-pptx converts clean designs into editable PowerPoint files with independently selectable elements. The README recommends GPT-5.6 with high reasoning effort or above for the strongest results. The gallery provides 14 prompts across six visual-design categories—from a tea-launch campaign poster and a skincare e-commerce hero to an academic poster for the paper \"Attention Is All You Need\". The repository is open source under Apache 2.0, and the authors note it is a community project, not an official OpenAI project.\n\n## So what\n\nThe point worth remembering is not \"prettier generated images\" but a demotion of the generative model inside the visual pipeline: it no longer delivers; it only supplies assets and art direction. The deliverable becomes a software artifact with layer contracts—versionable, composable, replayable. Once a design draft turns from a bitmap into a code artifact, revision, reuse, and collaboration can finally be engineered. For teams building design tools and agent products, this division-of-labor paradigm is more instructive than any single benchmark score.\n\nReferences: arxiv.org\u002Fabs\u002F2609.04034; github.com\u002Fyejy53\u002FEditable-Design","tencent-editable-visual-design-agent","2026-09-06T13:08:40Z","2026-09-06T13:09:01.930576Z","2026-09-06T13:09:01.930585Z",true,"agent",122,[39,48],{"slug":40,"tag_slug":40,"title_zh":41,"title_en":42,"intro_zh":43,"intro_en":44,"id":45,"is_active":35,"created_at":46,"modified_at":47},"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":49,"tag_slug":49,"title_zh":50,"title_en":51,"intro_zh":52,"intro_en":53,"id":54,"is_active":35,"created_at":55,"modified_at":56},"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":58},[59,64,69,74,79,84],{"id":60,"title":61,"news_slug":62,"published_at":63},"089f56f3-32ff-4036-89b5-728d5f5a9359","边聊边干活:腾讯混元开源全模态交互 Agent Gander,小脑管对话、大脑管执行","hunyuan-gander-omni-interaction-agent","2026-09-09T21:07:00+00:00",{"id":65,"title":66,"news_slug":67,"published_at":68},"051084dc-3da0-451e-9c6b-a267d5b0e77f","给机器人技能装上门禁:EmbodiedSkills 预检+验证闭环,RoboTwin 50 任务冲到 86.2%","embodiedskills-vla-verify-loop","2026-09-08T17:10:00+00:00",{"id":70,"title":71,"news_slug":72,"published_at":73},"7cc1b87c-fe06-495a-9c01-9516d0c16354","腾讯混元 HunyuanImage-3.0 全面开源：80B 总参 \u002F 13B 激活的自回归 MoE，把多模态理解和生图拉到同一框架","tencent-hunyuanimage-3-moe-autoregressive","2026-08-05T01:00:00+00:00",{"id":75,"title":76,"news_slug":77,"published_at":78},"f1397080-206a-469f-846c-932a4b3ab8f9","京东开源 JoyAI-Image：统一多模态基础模型，把「理解-生成-编辑」拧成一个闭环","jd-joyai-image","2026-07-20T06:00:00+00:00",{"id":80,"title":81,"news_slug":82,"published_at":83},"ab4cf67e-175a-470b-9512-9d767be79fc6","Boogu-Image-0.1 开源家族：用比对手少一个数量级的数据，把\"理解+生成\"统一做到闭源水平","boogu-image-0-1-10b-unified-turbo","2026-06-26T14:00:00+00:00",{"id":85,"title":86,"news_slug":87,"published_at":88},"d8187b9a-e9b4-4a14-98c9-bb545849304e","InterleaveThinker：Planner+Critic 让图像生成器交错生成","interleavethinker-planner-critic-nano-banana","2026-06-14T08:30:00+00:00"]