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    "title": "WebUniverse: Scaling Verified Training Data for Agentic Web Development",
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      "title": "Official project page and released Task 000020 model outputs",
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      "role": "problem",
      "locator": "1. Motivation：训练数据要展示开发过程与检查证据"
    },
    {
      "role": "related-work",
      "locator": "2. Related Work：从静态代码对扩展到可重用的开发任务"
    },
    {
      "role": "method",
      "locator": "3. Method：怎样生成、验证并训练一条开发轨迹"
    },
    {
      "role": "experiments",
      "locator": "4. Experiments：哪些能力改善，哪些比较不能扩大解释"
    },
    {
      "role": "future-work",
      "locator": "5. Discussion：哪些经验可复用，哪些研究还要另做"
    },
    {
      "role": "summary",
      "locator": "6. Conclusion：验证过的多步演示有效，但训练仍是SFT"
    }
  ],
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      "term": "WebUniverse-Mix",
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      "article_quote": "作者发布的 WebUniverse-Mix 是15,000条已通过检查的开发对话记录。"
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      "term": "WebUniverse-Env",
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      "article_quote": "WebUniverse-Env 保存与这些轨迹对应的起始工作区、任务输入和检查条件"
    },
    {
      "term": "harness",
      "kind": "prompt-context",
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      "source_locators": [
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      "article_quote": "这里的 harness 是给模型提供读写文件、终端和浏览器操作的编码环境。"
    },
    {
      "term": "trajectory refinement",
      "kind": "operation",
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          "locator": "§3.2"
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      "article_quote": "作者继续失败或中断的尝试，让模型修复已有实现，称为 trajectory refinement。"
    },
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      "term": "visual judge",
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      "article_quote": "作者用看图模型对照成品截图与参考截图，称为 visual judge。"
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          "quote": "作者因此把任务、起始项目和验证条件一起保存，让模型在可执行环境中完成应用，再保留开发记录。这既提供模仿对象，也让后续研究可以从同样的状态重新采样。中心贡献是可扩展的数据构建与发布，本文不是新RL算法，也不是新提出的排行榜。来源：§1、§3、Figure 1。"
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      "evidence": [
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          "artifact": "article",
          "quote": "文字任务从2022年以后创建、许可明确的GitHub仓库中人工选择。GPT-5.5读代码和文档，排除第三方库、压缩代码与二进制后，总结功能、用户动作和界面变化。例如jKanban种子记录添加卡片、在看板列之间拖动卡片。任务生成模型更换应用场景，保留这些交互机制；这不是把原仓库直接作为待修复项目。"
        },
        {
          "artifact": "article",
          "quote": "视觉任务从FineWeb候选URL出发，经CrUX列表、元数据、可访问性与robots检查筛选，2026-07-17至09-13爬取。作者触发延迟加载、冻结动画、去除广告与同意浮层，保存整页截图、图片、字体和来源记录；超过8,000像素高或缺可见图片的页面被拒绝，再做人审。多页组含3至7页并记录导航，多视口组使用1920×1080、1024×768、375×812。任务由固定模板生成， 这个步骤不调用模型合成需求 。视觉任务只做独立开发，没有中间改版状态的截图序列。来源：§3.1、A.2。"
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          "artifact": "article",
          "quote": "独立采样每次新开对话，从指定起始工作区开发，不导入其他尝试的对话或反馈。连续变更任务在当前任务通过后保存代码，作为下一步起点；若当前任务没有通过的尝试，序列结束。最后只保留从首任务开始、至少三步的连续通过前缀，减少初期任务过多的偏差。"
        },
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          "artifact": "article",
          "quote": "作者继续失败或中断的尝试，让模型修复已有实现，称为 trajectory refinement。 workspace handover 把失败代码与已有验证反馈交给新对话； conversation continuation 还保留旧对话。遇到持续重复工具调用时，作者中断并保存状态，接续前压缩连续重复消息。 旧模型的assistant内容和工具调用只作上下文，不计入训练损失；只监督接续后的部分。 最终验证通过的修复段，仍可包含错误、再改、再检查的过程。来源：§3.2、A.5。"
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          "quote": "保留轨迹的教师池为GPT-5.6 Sol（75.3%）、Qwen3.8-27B（8.4%）、GPT-5.5（6.9%）、MiniMax-M3（4.3%）、GPT-5.4（4.2%），以及Qwen3.5-35B-A3B直接通过的初始尝试（1.0%）。比例分别四舍五入。任务生成、开发演示、验证模型的角色不同，不能把整个数据集都归因于一个教师。来源：A.5、B.1。"
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          "quote": "§4.1与D.1给出的训练是：从Qwen3.5-35B-A3B开始，使用Megatron-LM与ms-swift，冻结vision tower，微调其余参数，两轮训练，对assistant输出施加损失。8张H100 NVL，global batch 16、micro batch 1，学习率2×10⁻⁵，10%warmup后cosine降到0；3%验证切分，106,496长度上限，98,304 token打包。Adam参数、并行设置等完整值见下方原表。"
        },
        {
          "artifact": "article",
          "quote": "论文未报告PPO、GRPO、优势估计、奖励加权策略损失或在线采样更新。 验证通过才保留数据，可以称为基于筛选的轨迹监督学习；有测试、有分数、有失败重试，仍不自动成为RL。大部分数据来自强模型演示，因此也有广义的轨迹蒸馏意义；作者没有报告教师logits、KL蒸馏目标、on-policy distillation或OPSD算法。不能把基座1.0%的直接成功尝试写成在线自蒸馏证据。"
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          "quote": "通常的SFT负对数似然可写成 L = − Σ mₜ log pθ(yₜ | context, y<t) ，其中掩码只选需要监督的输出；这只是本文说明，没有声称原文报告了精确归一化公式。官方Mix说明将 messages 按 role / content / loss 存储， loss:false 排除该消息， loss:null 采用框架默认，导出必须保留掩码。这与正文的接续前缀处理一致。"
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          "quote": "详细任务的测试在求解前生成。GPT-5.4、5.5或5.6 Sol依据需求提取检查项与通过条件，不读参考答案，再生成源码检查或Playwright浏览器脚本。若测试失败，作者用模型复核该测试是否加了需求未规定的假设；有问题便重写测试，并在 未改动的应用 上重跑。这个步骤校正检查器的错误，并不能替应用补齐功能。"
        },
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          "artifact": "article",
          "quote": "高层任务和部分详细任务用GUI agent验证。GPT-5.6 Sol只操作浏览器，不读源码，按需求探索并记录每项pass、fail或blocked与观察证据。作者用看图模型对照成品截图与参考截图，称为 visual judge。视觉验证同样用GPT-5.6 Sol，逐目标页面、逐指定视口使用六级rubric；多页按给定路由或路径访问，不可达记0。"
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          "artifact": "article",
          "quote": "脚本与GUI验证必须 全部测试通过 ，脚本复核也必须完成且无未解决问题。视觉要求每个页面与视口比较至少达到good match（0.75），不能用好页面的高分抵消一个差页面。记录必须完整、验证正常结束、最终求解对话正常结束，图像引用与训练格式检查也要通过。分数和证据用于筛选；可用失败反馈进入后续修复。来源：§3.3、§3.4。"
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          "quote": "一位作者人工审计600次采集尝试，每种验证器各抽100个接受、100个拒绝样本。脚本准确率94.5%，GUI为95.5%，视觉为85.0%。以脚本为例，98个接受被人工认可、2个误接受；91个拒绝被认可、9个误拒绝，故准确率(98+91)/200=94.5%，precision为98/100=98.0%。这是本文按Table 9重算。 只有一位标注者，且按机器结论平衡抽样 ，没有标注者间一致性，不能直接推出自然数据分布上的错误比例。"
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          "quote": "Appendix C给出园地规划器PlotPatch。需求要求用户从调色板选作物，点击网格绘制；Bed Fill填充原材料相同的连通区域；Clear Cell清单格，Clear Plot确认后清全图，Suggest Layout换建议布局。作物占用面积要随网格更新，用户可按名字保存并重新打开计划。这是论文的数据示例，具体源仓库及该样本全部外部验证记录未在本段公开。"
        },
        {
          "artifact": "article",
          "quote": "开发模型先读 /app ，发现只有 .git ，自行选择React与Vite。实现中，绘制动作只更新选中格；填充用队列遍历上下左右相邻且原材料相同的格；作物计数根据更新后的网格重新计算；打开保存计划恢复其网格。随后生产构建成功，但类型检查报TS7016、缺React声明，lint也发现导出和标识生成问题。模型修正导出、用计数器替换标识生成，并安装React类型声明。第二轮build、typecheck和lint通过，再启动服务请求localhost得到HTTP 200，最后报告完成。来源：Figure 6、Appendix C。"
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          "quote": "这些记录证明了开发中发生过修复，HTTP 200只证明服务响应。 它没有单独证明保存、重开、填充和计数均符合需求。对高层任务，外部GUI验证器应按需求在浏览器执行行为检查；脚本则用于更详细的任务。本文没有拿到PlotPatch完整验证日志，所以不把自拟动作当作作者已执行的检查。例如保存“春季”，改动网格，再重开“春季”应恢复旧网格；若只恢复名字而不恢复格子，按需求应失败。这是依据公开要求构造的说明性失败过程，非论文模型日志。"
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          "quote": "WebGen-Bench总体准确率是 100 × Σ检查项得分 / 全部检查项数量 ，没有可用验证结果的应用，其检查仍在分母里计0； 63.9%不是101个应用中63.9%全部通过 。论文未提供这里全部检查项的总数，不能还原一个整数“成功应用数”。Web-Bench则引用官方计分程序，没有在本文给出完整公式；例如主表均值(13.9+23.1)/2=18.5%，只核实算术均值，不等于独立重跑官方pass@k。"
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          "quote": "Figure 5的电话运营商Task 000020是一个具体检查案例。模型生成套餐、客服与账户界面，评测侧记录套餐详情、支持聊天、账户更新、导航和指定颜色五项。基础模型前三项Fail、后两项Pass，共2/5；微调后五项都Pass，为5/5。这份个例可说明交互改善；原始操作参数和完整judge日志没有取得。官方项目另提供两个输出HTML，可以查看成品，但本文未独立重现五项判决。"
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          "quote": "最有辨别力的基线是Qwen3.5-StepFun：同Qwen起点、同超参数、同15K样本。StepFun-Mix由公开Step-3.5-Flash-SFT抽13,500个网页生成对和1,500条软件工程轨迹，后者占10%样本、37.9%tokens。网页输出要求完整单文件HTML且至少2,000字符，再清理、去重、去污染。该基线总体功能成绩31.5%低于原模型34.5%；WebUniverse则63.9%。这支持任务对齐的开发演示有效， 没有单独隔离“验证筛选”这一组件 ：数据长度、模态、工具过程与题型也不同。样本预算相同，不代表token、采集调用或训练计算相同。来源：Table 2、D.2。"
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          "quote": "WebUniverse微调后，Vision2Web平均51.7→68.3，WebGen-Bench总体34.5%→63.9%，Web-Bench pass@2为12.2%→23.1%。其中WebGen-Bench功能检查22.3%→51.3%，说明改善不限于漂亮的静态页面。微调模型在部分指标超过几个大模型，但Web-Bench pass@2仍低于主表四个参考强模型，也没有超过GPT-5.6 Sol的视觉平均75.8或WebGen-Bench总体64.5%。"
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      "evidence": [
        {
          "artifact": "article",
          "quote": "单独训练文字子集，功能开发总体升至56.1%，视觉仅54.1%。单独训练视觉，视觉升至63.9%，功能却降到12.1%，Web-Bench pass@2也从12.2%降至6.8%。完整混合在全部已报告指标上最好。这同时支持互补覆盖，也否定“加视觉轨迹自然改善所有编码能力”的解释；两个子集样本量为10,500与4,500，不是等量或等token的纯模态实验。来源：Table 3。"
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          "quote": "保持家族比例，用严格嵌套的5K、10K、15K子集训练，三项成绩持续上升。WebGen-Bench为49.1→58.2→63.9；视觉63.9→66.2→68.3；Web-Bench均值14.9→17.7→18.5。所有运行固定两轮，增加数据也增加训练量；曲线不能单独证明同计算量的数据质量收益。原图三个y轴都非零起点，不能只看斜率的视觉大小。来源：§4.3、Figure 4、D.1。"
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          "quote": "Vision2Web Level 2要求按图和文字构建多页交互前端，共66任务，每模型跑三次，各指标分别取中位数。视觉32.7→37.6，功能48.3→48.5，综合40.3→42.5。综合先在每轮求视觉与功能的平均，再跨轮取中位， 不能用两个已发布中位数直接算综合分 ：例如(37.6+48.5)/2=43.05，并不是表中的42.5。复杂交互的功能收益很小。来源：D.4、Table 10。"
        },
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          "quote": "微调后仍有明显失败习惯。 Web-Bench第二次尝试中，微调模型23/72（31.9%）没有记录到实现修改，全部失败；基础模型仅2/83（2.4%）。另有微调轨迹查询同一图片尺寸1,311次，或搜索不存在的测试604次。它们限制了“多检查就等于更会修复”的解释。"
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          "quote": "Env公开说明列出Harbor任务描述、运行配置、初始工作区、参考资源、测试、参考实现与SHA-256清单；论文A.7描述原始任务包的 spec.json 、 start/ 、 verifier/task.json 等结构。两者是不同发布/转换层次，不能擅自当成同一目录布局。参考答案不会放入开发模型的求解工作区；验证条件可供研究者重用，不意味着每个隐藏检查文件都对求解模型可见。作者称重建后测试过的参考解全部通过，但没有在正文报告测试数量。"
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          "quote": "与本文最接近的公开网页训练资源，主要覆盖文字需求的独立开发。WebGen-Bench报告600条训练轨迹，WebGen-Agent约700条；FullStack-Agent也收集开发轨迹，但作者比较时未发现其公开轨迹。WebUniverse扩大到视觉参考与连续变更，并附上每条样本的验证记录。以下沿论文§2与Table 1比较资源形态，不能把表中的“未建立”当成某工作永远不支持这项能力。"
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          "quote": "前端视觉任务仍靠选定视口的静态截图，动画、过渡与随时间变化的交互没有完整监督。功能任务也只覆盖所写的要求。作者把移动应用、3D和游戏列为扩展方向；本文的15K前端结果不能直接证明这些领域的环境构建、验证可靠性或训练收益。即使已有build和HTTP证据，仍要明确检查浏览器是否运行、交互状态是否改变、截图是否符合要求。"
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