feat: add task bootstrap CRN lab

2026-07-30 06:15:45 +08:00
parent f041c15e81
commit 975ed3dce2
11 changed files with 1199 additions and 29 deletions
@@ -14,7 +14,7 @@
 | 表示、位置与残差高速公路 | 完成首版 | 81% | 真实 hidden-state / norm traces、长上下文位置外推与深层稳定性消融 |
 | Scaling Laws | 完成首版 | 74% | 真实拟合复现、置信区间与更多模型族对照 |
 | 数据工程与预训练配方 | 完成首版 | 73% | FineWeb / DCLM 逐图精读、真实去重误伤与 mixture traces |
-| DeepSeek 专题 | 七轮实证进行中 | 99% | 扩大到可做 task-level bootstrap，加入 per-row RNG，再推进干预式 mediation、SM90 FlashMLA、FP8/pipeline 与 R1-like RL |
+| DeepSeek 专题 | 八轮实证进行中 | 99% | 推进干预式 mediation、SM90 FlashMLA、FP8/pipeline 与 R1-like RL |
 | 指令微调与人类偏好 | 完成首版 | 75% | 真实偏好分歧、RM 长度偏置与 PPO/DPO 小模型复现 |
 | 推理与测试时扩展 | 完成首版 | 76% | 真实模型采样曲线、PRM 案例与逐篇图表精读 |
 | 工具使用与长程 Agent | 完成首版 | 74% | 真实环境 traces、cross-harness 对照、Agent RL 曲线与安全案例 |
@@ -41,7 +41,7 @@
 - [x] 完成 486 篇关键论文索引，覆盖 16 个标签专题与 Kimi/DeepSeek 聚光主线。
 - [x] 完成可检索、可按专题筛选的论文库页面。
 - [x] 完成 K3、语言模型前史、Transformer 基础、表示/位置/残差、DeepSeek 谱系、Scaling Laws、数据工程、长上下文、MoE、指令微调与人类偏好、推理、Agent、原生多模态、训练系统、推理服务、数值优化与评测安全十七篇首版长文。
- [x] 完成 K3 三轴架构、八联报告实验与四联开放工件实验、语言模型前史四联实验、Transformer 四联实验、表示深度四联实验、DeepSeek 二十一联实验、长上下文、MoE 路由、推理三页签，以及训练系统、推理服务、Scaling、数据工程、数值、Alignment、Agent、原生多模态与评测安全专题各四页签等八十八个原创交互视图。
+- [x] 完成 K3 三轴架构、八联报告实验与四联开放工件实验、语言模型前史四联实验、Transformer 四联实验、表示深度四联实验、DeepSeek 二十二联实验、长上下文、MoE 路由、推理三页签，以及训练系统、推理服务、Scaling、数据工程、数值、Alignment、Agent、原生多模态与评测安全专题各四页签等八十九个原创交互视图。
 - [x] 完成长上下文首版：五张成本账、26 篇一手论文、10+ 机制图与 8 策略交互实验室。
 - [x] 核验 FlashAttention、DeepSeek-V2/V3.2/V4、Kimi Linear/K3 等六份论文原文，并建立长上下文研究账本。
 - [x] 核验 Switch、ST-MoE、DeepSeekMoE、Loss-Free、V3、LatentMoE 与 K3 原文，并建立 MoE 研究账本。
@@ -264,11 +264,17 @@
 - [x] 第二十一个 DeepSeek 交互实验以四页签讲解 source→seed 层级、Math/Code 4×4 task matrix、source-level 方向与均值反例、64 格八字段复现及五段 hash chain；协议、完整审计、探针、独立 evaluator、analysis 与 compact builder 全部落盘。
 - [x] Round 07 本地闸门通过：83 个 Astro 文件零诊断，21 个页面、1,151 个站内引用、12 个跨页锚点零失败；Python 探针/评测/分析编译通过，compact builder 重建 SHA-256 不变。跨来源专项与 DeepSeek 全量真实 Chrome 回归通过，4×4 矩阵、English 反例、键盘页签、桌面/390px 移动端无溢出及零运行时异常均有自动断言。
 - [x] DeepSeek Round 07 以源提交 `9211333`、不可变镜像 `20260729T195952Z-9211333` 发布；OCI index digest `sha256:6c92883b…5584e2`，复用 NAS `12010→8080`、NPM host 31 / cert 41 与门户 `LLM ATLAS / projects / 180`。容器 healthy、0 次重启，NAS / VPS Tailscale / HTTPS / gzip / immutable assets、跨来源专项与 DeepSeek 全量生产 Chrome 回归通过；保留 `20260729T180228Z-580f696` 回滚。
 - [x] DeepSeek Round 08 在任何正式输出前冻结 HumanEval / GSM8K 各 32 条任务、四条件、T0 主分析与 T1–T3 诊断，并把共同随机数实现为 SHA-256 派生的显式 uniform tape；四个条件对同一 `u_t` 各自计算 float32 CDF，不再把 generator seed 重置冒充 CRN。
 - [x] 64 条短 smoke 的 prompt 64/64 exact、同进程重放 32/32 exact、CPU/CUDA RNG 16/16 不变；正式执行 64 tasks × T0 × 4 conditions 与 8 tasks × T1–T3 × 4 conditions，共 352 条输出，343 条 natural EOS、9 条截断、320 个完整 trajectory hashes。
 - [x] 独立 evaluator 对 256 条 T0 输出逐题评分：Code 四格 pass 为 `16/32 · 16/32 · 15/32 · 12/32`，Math 为 `19/32 · 19/32 · 17/32 · 16/32`；不把 59/128 与 71/128 写成 benchmark accuracy。
 - [x] 两域分别做 10,000 次选定任务 paired bootstrap，八个正确性 contrast 区间都跨零；长度 system-at-period 在 Code 为 `−130.875 [−178.688,−83.218]`，Math 为 `+25.125 [7.874,44.531]`，揭示任务域方向相反。
 - [x] 共同随机数对照的 256 / 256 个 contrast 全部共享相同 uniform tape；新进程重放 64 条结果，uniform hashes、token IDs、文本、停止状态与 RNG 等十二字段全部 64 / 64 exact。审计同时公开运行器在生成进程结束前已加载 gold、但 gold 没有进入 prompt/tokenizer/logits/warpers/CDF/tape/selection 的流程偏差。
 - [x] 第二十二个 DeepSeek 交互实验用五页签讲解显式 CRN sampler、选定任务 bootstrap、32 题浏览器、4×4 tape 诊断与十二字段复现/失败/偏差账；正式协议、manifest、runner、独立 evaluator、分析、复现与完整/compact 数据均已进入开源树。
 ## 正在进行
 - [ ] K3 三轮下一闸门：获得真实 token hidden states、expert load 与 cache traces，解释或修订 `A_log [128]` 工件冲突，再做 Figure 3/4/5 数值重绘和独立小模型复现。
- [ ] DeepSeek 七轮下一闸门：扩大到可做 task-level bootstrap 的预注册 source 抽样框，加入 per-row RNG/common-random-number 对照与 failure taxonomy；再推进干预式 mediation、SM90 FlashMLA、FP8 / pipeline traces 与 R1-like RL 小模型复现。
+- [ ] DeepSeek 八轮下一闸门：推进干预式 mediation、SM90 FlashMLA、FP8 / pipeline traces 与 R1-like RL 小模型复现。
 - [ ] 表示、位置与残差二轮：真实 hidden-state / norm traces、长上下文位置外推复现与 mHC / AttnRes 深层稳定性消融。
 - [ ] 评测安全二轮：真实 cross-harness / pass@k 复跑、Judge 元评测、动态污染与过拒案例。
 - [ ] 推理服务二轮：真实 GPU kernel / workload traces、功耗与成本、跨 vLLM / SGLang / TensorRT-LLM 复现。
@@ -451,6 +457,10 @@
 | 2026-07-30 | 域均值必须与 source 方向同时展示 | English 句点 contrast 的均值 −8.5，但 3/4 source 为正；Code task interaction 的 +1 与 −1 在域均值 0 中抵消 |
 | 2026-07-30 | Round 07 随机分叉与复现同时过闸 | R0/R1 63/64 同格 trajectory 分叉；新进程 R0 的八项预注册字段 64/64 exact，五段 artifact hash chain 闭合 |
 | 2026-07-30 | DeepSeek Round 07 跨来源采样里程碑发布 | 源提交 `9211333`、镜像 `20260729T195952Z-9211333`、OCI `sha256:6c92883b…5584e2`；生产专项/全量 Chrome、HTTPS 与门户通过，保留 Round 06 回滚点 |
 | 2026-07-30 | Round 08 用显式 uniform tape 实现真正的共同随机数 | 64 tasks × T0 × 4 conditions，加 8 tasks × 3 诊断 tapes × 4 conditions；352 条正式输出的四条件 CDF 共享逐步 `u_t` |
 | 2026-07-30 | bootstrap 只描述冻结任务集的敏感性 | HumanEval / GSM8K 分开做 10,000 次 task-paired resamples；正确性带全跨零，不报告总体/seed CI 或 p-value |
 | 2026-07-30 | 输出长度揭示强任务域交互 | system-at-period 在 Code 为负、Math 为正，两个选定任务带都不跨零；不从长度外推能力 |
 | 2026-07-30 | Round 08 十二字段重放过闸 | 64/64 exact；uniform hash、完整 token IDs、文本、stop 与 RNG 一并进入复现合同，评分 gold 提前加载的流程偏差公开保留 |
 ## 未决问题
@@ -19,7 +19,7 @@
 当前里程碑包含 17 专题学习地图、486 篇关键论文索引、Kimi K3 完整导读，
 语言模型前史、Transformer 基础、表示/位置/残差、DeepSeek 技术谱系、Scaling Laws、数据工程、长上下文、MoE、指令微调与人类偏好、推理、工具使用与长程 Agent、原生多模态、训练系统、推理服务、数值优化，以及评测与安全深度专题，
-以及 88 个覆盖核心机制的原创交互视图。K3 二轮导读以 32 张问题账、16 图 / 5 表审计、
+以及 89 个覆盖核心机制的原创交互视图。K3 二轮导读以 32 张问题账、16 图 / 5 表审计、
 8 个交互实验和 100 个一手/官方节点，完整覆盖架构、预训练、后训练、系统、评测、案例与附录。
 第三轮已完成开放工件与首个真实 kernel 里程碑：固定官方模型与 FlashKDA revisions，审计 96 个 checkpoint shards、
 497,220 个 tensor entries、真实 KDA / MLA / MoE / MoonViT shapes 与小范围参数统计，并用 4 个新视图
@@ -27,8 +27,8 @@
 `sm_120a` wheel，在 RTX 5090 上完成 6/6 官方参考 exact-match 和 K3 fixed / varlen 形状计时。详见
 [K3_ARTIFACT_AUDIT.md](./research/K3_ARTIFACT_AUDIT.md) 与
 [checkpoint_probe.py](./experiments/k3/checkpoint_probe.py)、[FlashKDA probe](./experiments/k3/flashkda/)。
-DeepSeek 七轮专题以 24 张问题账、10 次技术转向、
+DeepSeek 八轮专题以 24 张问题账、10 次技术转向、
-21 个交互实验和 60 个一手/官方节点，串起 Dense、MoE、MLA、V3 协同、R1 与 V4；
+22 个交互实验和 60 个一手/官方节点，串起 Dense、MoE、MLA、V3 协同、R1 与 V4；
 并固定官方 V2-Lite revision，在 RTX 5090 上连续执行 7/27 层，记录 3,240 次真实专家选择、
 MLA/HF eager cache shapes 与 `31/31` exact 独立复跑；进一步用真实 layer-1 权重执行官方 V3
 naive/absorb 路径，实际写入 576 元素 latent cache，并以 FP32 将两种结合顺序的最大误差压到
@@ -85,6 +85,14 @@ Round 07 保持 256 条输出预算不变，改为 16 条预先冻结的 source
 `16/16 · 8/16 · 12/16 · 16/16`。句点在 11 / 16 条 source 上缩短平均长度，但
 English 的首条 source 为 `−121`、其余三条为正，证明单题均值会与多数 source
 方向相反；新进程 R0 的八项合同字段仍为 `64 / 64` exact。
 Round 08 再把覆盖扩大到 HumanEval / GSM8K 各 32 条冻结任务，并以 SHA-256 显式
 uniform tape 代替“重置同一 seed”的伪共同随机数：T0 全任务四条件 256 条、额外三条
 随机带诊断 96 条，共 352 条正式输出，343 条 natural EOS、320 个完整 trajectory hashes。
 两个域分别做 10,000 次选定任务 paired bootstrap；八个正确性区间全部跨零，但
 system-at-period 的输出长度在 Code 为 `−130.9 [−178.7,−83.2]`、在 Math 为
 `+25.1 [7.9,44.5]`，说明干预方向依赖任务域。全新进程对 64 条输出的 uniform hash、
 token IDs、文本与停止等十二字段逐一重放，`64 / 64` exact；同时公开评分金标准被运行器
 提前加载、但没有进入 prompt / logits / CDF / tape / selection 的流程偏差。
 详见
 [DEEPSEEK_V2_LITE_TRACE.md](./research/DEEPSEEK_V2_LITE_TRACE.md) 与
 [DEEPSEEK_MLA_ABSORB_AUDIT.md](./research/DEEPSEEK_MLA_ABSORB_AUDIT.md)、
@@ -102,7 +110,9 @@ English 的首条 source 为 `−121`、其余三条为正，证明单题均值
 [DEEPSEEK_V2_LITE_CHAT_SAMPLING_PROTOCOL.md](./research/DEEPSEEK_V2_LITE_CHAT_SAMPLING_PROTOCOL.md) 与
 [DEEPSEEK_V2_LITE_CHAT_SAMPLING_AUDIT.md](./research/DEEPSEEK_V2_LITE_CHAT_SAMPLING_AUDIT.md)，以及
 [DEEPSEEK_V2_LITE_CHAT_CROSS_SOURCE_SAMPLING_PROTOCOL.md](./research/DEEPSEEK_V2_LITE_CHAT_CROSS_SOURCE_SAMPLING_PROTOCOL.md) 与
-[DEEPSEEK_V2_LITE_CHAT_CROSS_SOURCE_SAMPLING_AUDIT.md](./research/DEEPSEEK_V2_LITE_CHAT_CROSS_SOURCE_SAMPLING_AUDIT.md)。
+[DEEPSEEK_V2_LITE_CHAT_CROSS_SOURCE_SAMPLING_AUDIT.md](./research/DEEPSEEK_V2_LITE_CHAT_CROSS_SOURCE_SAMPLING_AUDIT.md)，以及
 [DEEPSEEK_V2_LITE_CHAT_TASK_BOOTSTRAP_PROTOCOL.md](./research/DEEPSEEK_V2_LITE_CHAT_TASK_BOOTSTRAP_PROTOCOL.md) 与
 [DEEPSEEK_V2_LITE_CHAT_TASK_BOOTSTRAP_CRN_AUDIT.md](./research/DEEPSEEK_V2_LITE_CHAT_TASK_BOOTSTRAP_CRN_AUDIT.md)。
 其余专题按进度账本持续扩建。
 ## 本地开发
@@ -37,6 +37,7 @@
    "check:deepseek-browser": "node scripts/check-deepseek-browser.mjs",
    "check:deepseek-sampling-browser": "node scripts/check-deepseek-sampling-browser.mjs",
    "check:deepseek-cross-source-sampling-browser": "node scripts/check-deepseek-cross-source-sampling-browser.mjs",
    "check:deepseek-task-bootstrap-browser": "node scripts/check-deepseek-task-bootstrap-browser.mjs",
    "check:k3-browser": "node scripts/check-k3-browser.mjs"
  },
  "dependencies": {
@@ -1212,7 +1212,7 @@ console.log(JSON.stringify(report, null, 2));
 const numeric = (text) => Number.parseFloat(text.replaceAll(",", ""));
 const failures = [];
 if (!overview.title.includes("为什么转向")) failures.push("专题标题异常");
-if (overview.sections !== 30 || overview.tocLinks !== 30) failures.push("二十九个编号专题加阅读链的目录结构异常");
+if (overview.sections !== 31 || overview.tocLinks !== 31) failures.push("三十个编号专题加阅读链的目录结构异常");
 if (overview.ledgers !== 24 || overview.waves !== 10) failures.push("二十四张问题账或十次转向结构异常");
 if (overview.paperLinks !== 60 || overview.branches !== 5 || overview.followups !== 1) failures.push("论文链、旁支或公开后续标记异常");
 if (overview.labTabs !== 4 || overview.labPanels !== 4) failures.push("四联实验结构异常");
@@ -1220,7 +1220,7 @@ if (overview.artifactTabs !== 13 || overview.artifactPanels !== 13 || overview.a
 if (overview.behaviorTabs !== 4 || overview.behaviorPanels !== 4 || overview.behaviorSources !== 16 || overview.behaviorEdges !== 10) failures.push("Chat 行为实验结构异常");
 if (overview.completionDepthTabs !== 4 || overview.completionDepthPanels !== 4 || overview.hiddenStages !== 29 || overview.routerLayers !== 26) failures.push("Chat 完成度与全深度实验结构异常");
 if (overview.crossSourceTabs !== 4 || overview.crossSourcePanels !== 4) failures.push("跨来源采样实验结构异常");
-if (overview.heroLabs !== "21 个可操作实验") failures.push("DeepSeek 实验总数账异常");
+if (overview.heroLabs !== "22 个可操作实验") failures.push("DeepSeek 实验总数账异常");
 if (overview.navLinks !== 20 || home.navLinks !== 20 || mobile.mobileLinks !== 20 || overview.activeNav !== "DeepSeek") failures.push("全站导航未同步 DeepSeek");
 if (overview.documentOverflow > 1 || mobile.documentOverflow > 1) failures.push("桌面或移动端存在文档级横向溢出");
 if (capacity.initial.panel !== "capacity" || capacity.initial.total !== "32.1× FFN" || capacity.initial.active !== "1.13× FFN") failures.push("V3 稀疏容量初始账异常");
@@ -240,8 +240,8 @@ assert(overview.facts["SAMPLED OUTPUTS"] === "256", "output headline 异常");
 assert(overview.facts["NATURAL EOS"] === "250 / 256", "EOS headline 异常");
 assert(overview.facts["MATH · STRICT"] === "47 / 64", "Math headline 异常");
 assert(overview.facts["CODE · TESTS"] === "52 / 64", "Code headline 异常");
-assert(overview.labs === "21 个可操作实验", "DeepSeek LABS 总账异常");
+assert(overview.labs === "22 个可操作实验", "DeepSeek LABS 总账异常");
-assert(overview.status === "七轮 · 512 条采样", "DeepSeek STATUS 总账异常");
+assert(overview.status === "八轮 · 864 条采样", "DeepSeek STATUS 总账异常");
 assert(overview.overflow <= 1, `桌面横向溢出 ${overview.overflow}px`);
 assert(hierarchySwitch.cards.length === 4, "Code source cards 数量异常");
@@ -223,7 +223,7 @@ assert(overview.panels === 4, `sampling panels=${overview.panels}`);
 assert(overview.seedBars === 8, `initial seed bars=${overview.seedBars}`);
 assert(overview.conditions === 8, `condition rows=${overview.conditions}`);
 assert(overview.edges === 10, `edge rows=${overview.edges}`);
-assert(overview.heroLabs?.startsWith("20"), `hero labs=${overview.heroLabs}`);
+assert(overview.heroLabs === "22 个可操作实验", `hero labs=${overview.heroLabs}`);
 assert(overview.activePanel === "trajectories", `active=${overview.activePanel}`);
 assert(overview.unique === "8 / 8", `initial unique=${overview.unique}`);
 assert(overview.eos === "5 / 8", `initial eos=${overview.eos}`);
@@ -0,0 +1,302 @@
 import { writeFileSync } from "node:fs";
 const cdpPort = process.env.CDP_PORT ?? "9231";
 const baseUrl = process.env.SITE_URL ?? "http://127.0.0.1:4327";
 const pages = await fetch(`http://127.0.0.1:${cdpPort}/json/list`)
  .then((response) => response.json());
 const page = pages.find((entry) => entry.type === "page");
 if (!page) throw new Error(`CDP ${cdpPort} 没有可用页面`);
 const socket = new WebSocket(page.webSocketDebuggerUrl);
 await new Promise((resolve, reject) => {
  socket.addEventListener("open", resolve, { once: true });
  socket.addEventListener("error", reject, { once: true });
 });
 let nextId = 0;
 const pending = new Map();
 const exceptions = [];
 socket.addEventListener("message", (event) => {
  const message = JSON.parse(event.data);
  if (message.id && pending.has(message.id)) {
    const { resolve, reject } = pending.get(message.id);
    pending.delete(message.id);
    if (message.error) reject(new Error(message.error.message));
    else resolve(message.result);
  }
  if (message.method === "Runtime.exceptionThrown") {
    exceptions.push(
      message.params.exceptionDetails.exception?.description
      ?? message.params.exceptionDetails.text,
    );
  }
 });
 const command = (method, params = {}) => new Promise((resolve, reject) => {
  const id = ++nextId;
  pending.set(id, { resolve, reject });
  socket.send(JSON.stringify({ id, method, params }));
 });
 const pause = (milliseconds) => new Promise(
  (resolve) => setTimeout(resolve, milliseconds),
 );
 const evaluate = async (expression) => {
  const result = await command("Runtime.evaluate", {
    expression,
    returnByValue: true,
    awaitPromise: true,
  });
  if (result.exceptionDetails) {
    throw new Error(
      result.exceptionDetails.exception?.description
      ?? result.exceptionDetails.text,
    );
  }
  return result.result.value;
 };
 const screenshot = async (path) => {
  const result = await command("Page.captureScreenshot", {
    format: "png",
    captureBeyondViewport: false,
  });
  writeFileSync(path, Buffer.from(result.data, "base64"));
 };
 const assert = (condition, message) => {
  if (!condition) throw new Error(message);
 };
 await command("Page.enable");
 await command("Runtime.enable");
 await command("Emulation.setDeviceMetricsOverride", {
  width: 1440,
  height: 1100,
  deviceScaleFactor: 1,
  mobile: false,
 });
 await command("Page.navigate", {
  url: `${baseUrl}/deepseek/#task-bootstrap-crn`,
 });
 for (let attempt = 0; attempt < 100; attempt += 1) {
  await pause(100);
  if (await evaluate("document.readyState === 'complete'")) break;
 }
 const overview = await evaluate(`(() => {
  const root = document.querySelector("[data-task-bootstrap-lab]");
  if (!root) return null;
  document.documentElement.style.scrollBehavior = "auto";
  window.scrollTo(0, root.getBoundingClientRect().top + window.scrollY);
  const facts = Object.fromEntries(
    [...root.querySelectorAll(".tb-ledger article")].map((node) => [
      node.querySelector("span").textContent.trim(),
      node.querySelector("b").textContent.trim(),
    ]),
  );
  return {
    tabs: root.querySelectorAll("[data-tb-tab]").length,
    panels: root.querySelectorAll("[data-tb-panel]").length,
    active: root.querySelector("[data-tb-panel]:not([hidden])")?.dataset.tbPanel,
    bars: root.querySelectorAll(".uniform-bars article").length,
    lanes: root.querySelectorAll(".token-lanes article").length,
    laneTokens: root.querySelectorAll(".token-lanes article i").length,
    facts,
    labs: [...document.querySelectorAll(".page-facts > div")]
      .find((node) => node.querySelector("dt")?.textContent.trim() === "LABS")
      ?.querySelector("dd")?.textContent.trim(),
    status: [...document.querySelectorAll(".page-facts > div")]
      .find((node) => node.querySelector("dt")?.textContent.trim() === "STATUS")
      ?.querySelector("dd")?.textContent.trim(),
    documentOverflow: document.documentElement.scrollWidth
      - document.documentElement.clientWidth,
  };
 })()`);
 await pause(250);
 await screenshot("/tmp/llm-atlas-task-bootstrap-sampler-desktop.png");
 const bootstrap = await evaluate(`(() => {
  const root = document.querySelector("[data-task-bootstrap-lab]");
  root.querySelector('[data-tb-tab="bootstrap"]').click();
  const read = () => ({
    active: root.querySelector("[data-tb-panel]:not([hidden])").dataset.tbPanel,
    conditionCards: [...root.querySelectorAll("[data-tb-condition-cards] article")]
      .map((node) => node.querySelector("b").textContent.trim()),
    forest: [...root.querySelectorAll("[data-tb-forest] > article")].map((node) => ({
      label: node.querySelector("span").textContent.trim(),
      point: node.querySelector(":scope > b").textContent.trim(),
      band: node.querySelector("small").textContent.trim(),
      clear: node.querySelector("u").classList.contains("clear"),
    })),
    robust: root.querySelector("[data-tb-robust-count]").textContent.trim(),
  });
  const codeCorrectness = read();
  root.querySelector('[data-tb-metric="generated_tokens"]').click();
  const codeLength = read();
  root.querySelector('[data-tb-domain="math"]').click();
  const mathLength = read();
  return { codeCorrectness, codeLength, mathLength };
 })()`);
 await pause(150);
 await screenshot("/tmp/llm-atlas-task-bootstrap-forest-desktop.png");
 const taskExplorer = await evaluate(`(() => {
  const root = document.querySelector("[data-task-bootstrap-lab]");
  root.querySelector('[data-tb-tab="tasks"]').click();
  const domain = root.querySelector("[data-tb-task-domain]");
  const contrast = root.querySelector("[data-tb-task-contrast]");
  domain.value = "math";
  domain.dispatchEvent(new Event("change", { bubbles: true }));
  contrast.value = "system_at_eos";
  contrast.dispatchEvent(new Event("change", { bubbles: true }));
  root.querySelector('[data-tb-task-page="2"]').click();
  return {
    active: root.querySelector("[data-tb-panel]:not([hidden])").dataset.tbPanel,
    rows: root.querySelectorAll("[data-tb-task-rows] > article").length,
    page: root.querySelector("[data-tb-task-page-label]").textContent.trim(),
    selectedPage: root.querySelector('[data-tb-task-page][aria-pressed="true"]')
      .dataset.tbTaskPage,
    firstTask: root.querySelector("[data-tb-task-rows] > article span b")
      .textContent.trim(),
    transition: [...root.querySelectorAll("[data-tb-transition-cards] article")]
      .map((node) => node.querySelector("b").textContent.trim()),
  };
 })()`);
 const tape = await evaluate(`(() => {
  const root = document.querySelector("[data-task-bootstrap-lab]");
  root.querySelector('[data-tb-tab="tapes"]').click();
  const domain = root.querySelector("[data-tb-tape-domain]");
  const contrast = root.querySelector("[data-tb-tape-contrast]");
  domain.value = "math";
  domain.dispatchEvent(new Event("change", { bubbles: true }));
  contrast.value = "system_at_eos";
  contrast.dispatchEvent(new Event("change", { bubbles: true }));
  return {
    active: root.querySelector("[data-tb-panel]:not([hidden])").dataset.tbPanel,
    rows: root.querySelectorAll("[data-tb-tape-rows] > article").length,
    cells: root.querySelectorAll("[data-tb-tape-rows] > article > b").length,
    means: [...root.querySelectorAll("[data-tb-tape-means] > b")]
      .map((node) => node.textContent.trim()),
    taskRange: root.querySelector("[data-tb-task-range]").textContent.trim(),
    tapeRange: root.querySelector("[data-tb-tape-range]").textContent.trim(),
  };
 })()`);
 const audit = await evaluate(`(() => {
  const root = document.querySelector("[data-task-bootstrap-lab]");
  root.querySelector('[data-tb-tab="audit"]').click();
  return {
    active: root.querySelector("[data-tb-panel]:not([hidden])").dataset.tbPanel,
    fields: [...root.querySelectorAll(".replay-fields article")]
      .map((node) => node.querySelector("b").textContent.trim()),
    codeOutcomes: root.querySelectorAll(".failure-ledger > article:first-child > div").length,
    mathOutcomes: root.querySelectorAll(".failure-ledger > article:last-child > div").length,
    deviations: root.querySelectorAll(".deviation-ledger article").length,
    artifacts: root.querySelectorAll(".artifact-chain article").length,
  };
 })()`);
 const keyboard = await evaluate(`(() => {
  const root = document.querySelector("[data-task-bootstrap-lab]");
  const first = root.querySelector('[data-tb-tab="sampler"]');
  first.click();
  first.focus();
  first.dispatchEvent(new KeyboardEvent("keydown", {
    key: "ArrowRight",
    bubbles: true,
  }));
  return {
    selected: root.querySelector('[data-tb-tab][aria-selected="true"]').dataset.tbTab,
    active: root.querySelector("[data-tb-panel]:not([hidden])").dataset.tbPanel,
    focused: document.activeElement.dataset.tbTab,
  };
 })()`);
 await command("Emulation.setDeviceMetricsOverride", {
  width: 390,
  height: 844,
  deviceScaleFactor: 1,
  mobile: true,
 });
 await pause(300);
 const mobile = await evaluate(`(() => {
  const root = document.querySelector("[data-task-bootstrap-lab]");
  root.querySelector('[data-tb-tab="tasks"]').click();
  root.scrollIntoView();
  return {
    documentOverflow: document.documentElement.scrollWidth
      - document.documentElement.clientWidth,
    rootOverflow: root.scrollWidth - root.clientWidth,
    tableWidth: root.querySelector(".task-table").getBoundingClientRect().width,
    tableScrollWidth: root.querySelector(".task-table").scrollWidth,
    viewport: window.innerWidth,
  };
 })()`);
 await screenshot("/tmp/llm-atlas-task-bootstrap-mobile.png");
 assert(overview, "找不到任务 bootstrap 实验");
 assert(overview.tabs === 5 && overview.panels === 5, "五页签/面板合同异常");
 assert(overview.active === "sampler", "初始面板不是 sampler");
 assert(overview.bars === 8, "uniform tape 前八步渲染异常");
 assert(overview.lanes === 4 && overview.laneTokens === 32, "四条件 token lane 异常");
 assert(overview.facts.TASKS === "32 + 32", "任务 headline 异常");
 assert(overview.facts["FORMAL GRID"] === "352", "formal headline 异常");
 assert(overview.facts["UNIFORM AUDIT"] === "352 / 352", "uniform headline 异常");
 assert(overview.facts["FRESH PROCESS"] === "64 / 64", "replay headline 异常");
 assert(overview.labs === "22 个可操作实验", "DeepSeek LABS 总账异常");
 assert(overview.status === "八轮 · 864 条采样", "DeepSeek STATUS 总账异常");
 assert(overview.documentOverflow <= 1, `桌面横向溢出 ${overview.documentOverflow}px`);
 assert(bootstrap.codeCorrectness.active === "bootstrap", "bootstrap 面板切换异常");
 assert(bootstrap.codeCorrectness.conditionCards.join("|") === "16 / 32 pass|16 / 32 pass|15 / 32 pass|12 / 32 pass", "Code 条件 pass 表异常");
 assert(bootstrap.codeCorrectness.forest.length === 4, "forest contrast 数异常");
 assert(bootstrap.codeCorrectness.robust === "0 / 4 bands 不跨 0", "Code correctness 带数异常");
 assert(bootstrap.codeLength.robust === "2 / 4 bands 不跨 0", "Code length 带数异常");
 assert(bootstrap.codeLength.forest.at(-1).point === "-130.9 tok", "Code system-period 长度异常");
 assert(bootstrap.mathLength.robust === "1 / 4 bands 不跨 0", "Math length 带数异常");
 assert(bootstrap.mathLength.forest.at(-1).point === "+25.1 tok", "Math system-period 长度异常");
 assert(taskExplorer.active === "tasks", "逐题面板切换异常");
 assert(taskExplorer.rows === 8 && taskExplorer.page === "17–24 / 32", "逐题分页异常");
 assert(taskExplorer.selectedPage === "2", "逐题页按钮状态异常");
 assert(taskExplorer.firstTask === "gsm8k/test/1050", "逐题页首任务异常");
 assert(taskExplorer.transition.join("|") === "3 / 32|3 / 32|16 / 32|10 / 32", "Math system-EOS 转移异常");
 assert(tape.active === "tapes", "tape 面板切换异常");
 assert(tape.rows === 4 && tape.cells === 16, "4×4 tape matrix 异常");
 assert(tape.means.join("|") === "0.00|+0.50|-0.25|+0.25", "Math system-EOS tape means 异常");
 assert(tape.taskRange === "1.25" && tape.tapeRange === "1.00", "tape/task range 异常");
 assert(audit.active === "audit", "审计面板切换异常");
 assert(audit.fields.length === 12, "重放字段数不为 12");
 assert(audit.fields.every((value) => value === "64 / 64"), "重放字段未全部 exact");
 assert(audit.codeOutcomes === 6 && audit.mathOutcomes === 2, "失败分类数量异常");
 assert(audit.deviations === 2, "偏离账数量异常");
 assert(audit.artifacts === 4, "工件 hash 数量异常");
 assert(
  keyboard.selected === "bootstrap"
    && keyboard.active === "bootstrap"
    && keyboard.focused === "bootstrap",
  "页签键盘导航异常",
 );
 assert(mobile.documentOverflow <= 1, `移动端 document 横向溢出 ${mobile.documentOverflow}px`);
 assert(mobile.rootOverflow <= 1, `移动端实验横向溢出 ${mobile.rootOverflow}px`);
 assert(mobile.tableWidth <= mobile.viewport, "移动端任务表容器超出 viewport");
 assert(mobile.tableScrollWidth > mobile.tableWidth, "移动端任务表没有内部横向滚动");
 assert(exceptions.length === 0, `浏览器异常：${exceptions.join(" | ")}`);
 console.log(JSON.stringify({
  overview,
  bootstrap,
  taskExplorer,
  tape,
  audit,
  keyboard,
  mobile,
  screenshots: [
    "/tmp/llm-atlas-task-bootstrap-sampler-desktop.png",
    "/tmp/llm-atlas-task-bootstrap-forest-desktop.png",
    "/tmp/llm-atlas-task-bootstrap-mobile.png",
  ],
 }, null, 2));
 socket.close();
@@ -0,0 +1,827 @@
 ---
 import rawLab from "@/data/deepseek-v2-lite-chat-task-bootstrap-crn-compact.json";
 const lab = rawLab as any;
 const json = JSON.stringify(lab).replaceAll("<", "\\u003c");
 const example = lab.uniformExample;
 const conditions = ["s0_eos", "s1_eos", "s0_period", "s1_period"];
 const conditionLabels: Record<string, string> = {
  s0_eos: "无 system · EOS",
  s1_eos: "有 system · EOS",
  s0_period: "无 system · 句点",
  s1_period: "有 system · 句点",
 };
 ---
 <figure class="task-bootstrap-lab" data-task-bootstrap-lab>
  <header class="tb-head">
    <div>
      <p>ROUND 08 / TASK BOOTSTRAP × EXPLICIT CRN</p>
      <h3>把“换题”和“换随机数”拆开，再问 prompt 到底改变了什么</h3>
    </div>
    <p>
      主分析固定 T0，在 HumanEval 与 GSM8K 各 32 道预选题上逐题配对；
      另取每域 4 题跑 T0–T3。四格在第 <code>t</code> 步读取同一个显式
      <code>uₜ</code>，再各自穿过不同的 token CDF。
    </p>
  </header>
  <div class="tb-ledger">
    <article><span>TASKS</span><b>32 + 32</b><p>Code / Math 始终分开</p></article>
    <article><span>FORMAL GRID</span><b>352</b><p>256 主分析 + 96 额外 tape</p></article>
    <article class="pass"><span>PROMPT HASH</span><b>256 / 256</b><p>输出前冻结并逐格 exact</p></article>
    <article><span>UNIFORM AUDIT</span><b>352 / 352</b><p>每条消费前缀重新派生</p></article>
    <article><span>TASK BOOTSTRAP</span><b>10,000×</b><p>固定 32 题框，不外推总体</p></article>
    <article class="pass"><span>FRESH PROCESS</span><b>64 / 64</b><p>十二项字段全部 exact</p></article>
  </div>
  <div class="tb-tabs" role="tablist" aria-label="选择任务 bootstrap 实验视图">
    <button type="button" role="tab" data-tb-tab="sampler" aria-selected="true">
      <span>01</span><b>真正的共同随机数</b><small>uniform tape → four CDFs</small>
    </button>
    <button type="button" role="tab" data-tb-tab="bootstrap" aria-selected="false" tabindex="-1">
      <span>02</span><b>32 题重采样带</b><small>paired task bootstrap</small>
    </button>
    <button type="button" role="tab" data-tb-tab="tasks" aria-selected="false" tabindex="-1">
      <span>03</span><b>逐题看正负抵消</b><small>task × condition explorer</small>
    </button>
    <button type="button" role="tab" data-tb-tab="tapes" aria-selected="false" tabindex="-1">
      <span>04</span><b>换题还是换 tape</b><small>4 tasks × 4 tapes</small>
    </button>
    <button type="button" role="tab" data-tb-tab="audit" aria-selected="false" tabindex="-1">
      <span>05</span><b>重放、失败与偏离</b><small>evidence boundary</small>
    </button>
  </div>
  <section class="tb-panel" data-tb-panel="sampler">
    <div class="tb-panel-lead">
      <div><span>I / COMMON RANDOM NUMBERS</span><h4>同一个 seed，不一定是同一个随机冲击</h4></div>
      <p>
        旧实验把四行放在同一个 seeded batch，<code>torch.multinomial</code> 为不同
        行消费不同 RNG 子流。本轮直接定义每一步的均匀数，所以配对对象终于可见、可重建。
      </p>
    </div>
    <div class="pairing-compare">
      <article>
        <span>ROUND 06–07 · BATCH SEED</span>
        <div class="stream-row"><i>seed</i><b>→</b><u>r₀</u><u>r₁</u><u>r₂</u><u>r₃</u></div>
        <p>同一 seed 与调用时序，但四行不是同一概率分位。</p>
      </article>
      <article class="active">
        <span>ROUND 08 · EXPLICIT TAPE</span>
        <div class="stream-row"><i>uₜ</i><b>→</b><u>uₜ</u><u>uₜ</u><u>uₜ</u><u>uₜ</u></div>
        <p>同题、同 tape、同 step 的四格读取完全相同的 <code>uₜ</code>。</p>
      </article>
    </div>
    <div class="sampler-pipeline" aria-label="显式共同随机数采样流程">
      <article><span>01 / HASH</span><b>SHA-256</b><p>protocol · tape · source · step</p></article>
      <i>→</i>
      <article class="uniform"><span>02 / SHARED</span><b>uₜ ∈ (0,1)</b><p>四格同一个概率分位</p></article>
      <i>→</i>
      <article><span>03 / FOUR DISTRIBUTIONS</span><b>T .3 · P .95</b><p>prompt 改变各自 logits / CDF</p></article>
      <i>→</i>
      <article><span>04 / TOKEN</span><b>searchsorted</b><p>同 uₜ 可以落入不同 token</p></article>
    </div>
    <div class="uniform-demo">
      <header>
        <div><span>REAL T0 TAPE / {example.sourceId}</span><b>前 8 个生成步</b></div>
        <p>柱高是 float32 <code>uₜ</code>；hex 是冻结的 uint64 前缀。</p>
      </header>
      <div class="uniform-bars">
        {example.uniformFloat32FirstEight.map((value: number, index: number) => (
          <article style={`--u:${Math.max(0.025, value)}`}>
            <i></i>
            <span>t{index}</span>
            <b>{value.toFixed(3)}</b>
            <code>{example.uniformUint64FirstEightHex[index].slice(0, 6)}</code>
          </article>
        ))}
      </div>
      <div class="token-lanes">
        {conditions.map((condition) => (
          <article>
            <header><span>{condition}</span><b>{conditionLabels[condition]}</b></header>
            <div>
              {example.conditions[condition].generatedTokenIds.map((token: number, index: number) => (
                <i><small>t{index}</small>{token}</i>
              ))}
            </div>
          </article>
        ))}
      </div>
    </div>
    <aside class="tb-note">
      <b>读图关键：四条 lane 上方的随机柱完全相同，token ID 却会分叉</b>
      <p>
        共同随机数控制的是 sampling noise，不是把四个条件钉成同一输出。prompt 一旦改变
        概率分布，同一分位自然可以映射到不同 token。
      </p>
    </aside>
  </section>
  <section class="tb-panel" data-tb-panel="bootstrap" hidden>
    <div class="tb-panel-lead">
      <div><span>II / SELECTED-TASK BOOTSTRAP</span><h4>带宽回答“换这 32 道题的权重会怎样”</h4></div>
      <p>
        每次在固定 32 题中有放回抽 32 题，四条件保持题级配对。它不包含换随机带的不确定性，
        也不是完整 benchmark population confidence interval。
      </p>
    </div>
    <div class="tb-switch-row">
      <div role="group" aria-label="选择 bootstrap 任务域">
        <button type="button" data-tb-domain="code" aria-pressed="true">CODE · HUMANEVAL</button>
        <button type="button" data-tb-domain="math" aria-pressed="false">MATH · GSM8K</button>
      </div>
      <div role="group" aria-label="选择 bootstrap 指标">
        <button type="button" data-tb-metric="fixed_budget_success" aria-pressed="true">CORRECTNESS</button>
        <button type="button" data-tb-metric="generated_tokens" aria-pressed="false">LENGTH</button>
      </div>
    </div>
    <div class="condition-cards" data-tb-condition-cards></div>
    <div class="forest">
      <header><span>RIGHT LOWER / SHORTER</span><b>0 · NO MEAN DIFFERENCE</b><span>RIGHT HIGHER / LONGER</span></header>
      <div data-tb-forest></div>
    </div>
    <div class="bootstrap-reading">
      <article>
        <span>WHAT IS RESAMPLED</span>
        <b>32 selected tasks</b>
        <p>同一次抽样中，四个 prompt condition 保持配对。</p>
      </article>
      <article>
        <span>WHAT IS FIXED</span>
        <b>T0 · checkpoint · prompt</b>
        <p>这条带不覆盖 generation-tape uncertainty。</p>
      </article>
      <article class="result">
        <span data-tb-robust-label>LENGTH / CODE</span>
        <b data-tb-robust-count>—</b>
        <p data-tb-robust-copy>—</p>
      </article>
    </div>
    <aside class="tb-note dark">
      <b>正确率的八条带都跨 0；长度出现 domain 反向交互</b>
      <p>
        Code 的 system-at-period 为 −130.9 tokens，Math 为 +25.1 tokens，两个
        selected-task bands 都不跨 0、方向却相反。“system 会让输出更短”不是可跨域外推的结论。
      </p>
    </aside>
  </section>
  <section class="tb-panel" data-tb-panel="tasks" hidden>
    <div class="tb-panel-lead">
      <div><span>III / TASK EXPLORER</span><h4>平均差为 0，也可能是 fail→pass 与 pass→fail 抵消</h4></div>
      <p>
        每页 8 道题。P/F 是 T0 上的独立 evaluator 结果；末列显示所选 contrast 的
        success 差、长度差与共同 token 前缀。
      </p>
    </div>
    <div class="task-controls">
      <label><span>DOMAIN</span>
        <select data-tb-task-domain aria-label="选择逐题任务域">
          <option value="code">Code · HumanEval</option>
          <option value="math">Math · GSM8K</option>
        </select>
      </label>
      <label><span>CONTRAST</span>
        <select data-tb-task-contrast aria-label="选择逐题 contrast">
          <option value="period_at_s0">句点 − EOS · 无 system</option>
          <option value="period_at_s1">句点 − EOS · 有 system</option>
          <option value="system_at_eos">system on − off · EOS</option>
          <option value="system_at_period">system on − off · 句点</option>
        </select>
      </label>
      <article><span>VISIBLE TASKS</span><b data-tb-task-page-label>01–08 / 32</b></article>
    </div>
    <div class="task-pages" role="group" aria-label="选择逐题页">
      {[0, 1, 2, 3].map((page) => (
        <button type="button" data-tb-task-page={page} aria-pressed={page === 0 ? "true" : "false"}>
          {String(page * 8 + 1).padStart(2, "0")}–{String(page * 8 + 8).padStart(2, "0")}
        </button>
      ))}
    </div>
    <div class="task-table">
      <header><b>TASK</b>{conditions.map((condition) => <b>{condition}</b>)}<b>SELECTED CONTRAST</b></header>
      <div data-tb-task-rows></div>
    </div>
    <div class="transition-cards" data-tb-transition-cards></div>
    <aside class="tb-note">
      <b>四格合计 pass 不是模型标准分数</b>
      <p>
        Code 的 59/128 与 Math 的 71/128 都来自 <code>32 tasks × 4 conditions</code>；
        同一道题出现四次。逐题转移表才保留条件改变的方向。
      </p>
    </aside>
  </section>
  <section class="tb-panel" data-tb-panel="tapes" hidden>
    <div class="tb-panel-lead">
      <div><span>IV / CROSSED TAPE DIAGNOSTIC</span><h4>4 道题 × 4 条 tape，不是 16 道独立题</h4></div>
      <p>
        行是预先固定的题，列是 T0–T3。先在题内横向看 tape range，再在 tape 内纵向看
        task range；两种变化不能揉成一个普通样本方差。
      </p>
    </div>
    <div class="tape-controls">
      <label><span>DOMAIN</span>
        <select data-tb-tape-domain aria-label="选择随机带诊断域">
          <option value="code">Code · HumanEval</option>
          <option value="math">Math · GSM8K</option>
        </select>
      </label>
      <label><span>CONTRAST</span>
        <select data-tb-tape-contrast aria-label="选择随机带诊断 contrast">
          <option value="period_at_s0">句点 − EOS · 无 system</option>
          <option value="period_at_s1">句点 − EOS · 有 system</option>
          <option value="system_at_eos">system on − off · EOS</option>
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          <option value="tokens">Length Δ</option>
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    </div>
    <div class="tape-matrix">
      <header><b>TASK ↓ / TAPE →</b><b>T0</b><b>T1</b><b>T2</b><b>T3</b><b>TAPE RANGE</b></header>
      <div data-tb-tape-rows></div>
      <footer data-tb-tape-means></footer>
    </div>
    <div class="tape-reading">
      <article><span>MEAN TASK RANGE WITHIN TAPE</span><b data-tb-task-range>—</b><p>固定一条 tape，四题之间的 contrast 跨度</p></article>
      <article><span>MEAN TAPE RANGE WITHIN TASK</span><b data-tb-tape-range>—</b><p>固定一道题，四条 tape 之间的 contrast 跨度</p></article>
      <article class="result"><span>INDEPENDENCE</span><b>4 crossed tasks</b><p>不是 16 个独立观测；只做敏感性诊断</p></article>
    </div>
    <aside class="tb-note dark">
      <b>Code 长度方向更稳，Math correctness 对 tape 更敏感</b>
      <p>
        在四题诊断子集上，Code 的 system-at-period 四条 tape 都为负；Math 同一长度
        contrast 四条都为正。但 Math correctness 的 system-at-EOS 在 T0–T3 间从 −0.25 到 +0.50。
      </p>
    </aside>
  </section>
  <section class="tb-panel" data-tb-panel="audit" hidden>
    <div class="tb-panel-lead">
      <div><span>V / EVIDENCE AUDIT</span><h4>先锁 trajectory，再打开 gold；偏离也写进证据链</h4></div>
      <p>
        formal、独立 evaluator、replay 与 analysis 各自有文件 hash。重放检查的不只是
        headline，而是每格 12 个冻结字段。
      </p>
    </div>
    <div class="evidence-pipeline">
      <article><span>01 / FREEZE</span><b>64 tasks · 256 prompts</b><p>source、tape、contrast、bootstrap seed</p></article>
      <i>→</i>
      <article><span>02 / GENERATE</span><b>352 trajectories</b><p>88/88 runs 不消费 PyTorch RNG</p></article>
      <i>→</i>
      <article><span>03 / EVALUATE</span><b>networkless sandbox</b><p>停止、覆盖、正确、失败分账</p></article>
      <i>→</i>
      <article class="result"><span>04 / REPLAY</span><b>64 / 64 exact</b><p>全新进程 · 十二字段</p></article>
    </div>
    <div class="replay-fields">
      {Object.entries(lab.reproduction.by_field).map(([field, count]) => (
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          <b>{String(count)} / 64</b><i>EXACT</i>
        </article>
      ))}
    </div>
    <div class="failure-ledger">
      <article>
        <header><span>CODE / 128 T0 OUTPUTS</span><b>59 pass</b></header>
        {Object.entries(lab.outcomes.code).map(([name, count]) => (
          <div><span>{name.replaceAll("_", " ")}</span><i><u style={`--share:${Number(count) / 128}`}></u></i><b>{String(count)}</b></div>
        ))}
      </article>
      <article>
        <header><span>MATH / 128 T0 OUTPUTS</span><b>71 exact</b></header>
        {Object.entries(lab.outcomes.math).map(([name, count]) => (
          <div><span>{name.replaceAll("_", " ")}</span><i><u style={`--share:${Number(count) / 128}`}></u></i><b>{String(count)}</b></div>
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      </article>
    </div>
    <div class="deviation-ledger">
      {lab.deviations.map((deviation: any, index: number) => (
        <article class={deviation.severity}>
          <span>{String(index + 1).padStart(2, "0")} / {deviation.severity.replaceAll("-", " ").toUpperCase()}</span>
          <b>{deviation.id.replaceAll("-", " ")}</b>
          <p>{deviation.summary}</p>
        </article>
      ))}
    </div>
    <div class="artifact-chain">
      {Object.entries(lab.artifactHashes).map(([name, hash], index) => (
        <article>
          <span>{String(index + 1).padStart(2, "0")} / {name.toUpperCase()}</span>
          <b>{String(hash).slice(0, 12)}…{String(hash).slice(-8)}</b>
        </article>
      ))}
    </div>
    <aside class="tb-note">
      <b>gold 加载时机是本轮明确报告的流程偏离</b>
      <p>
        复用 runner 在生成进程开始前加载 gold，只用于文本生成结束后的窄
        <code>task_score</code>；gold 不进入 prompt、logits、CDF、tape 或任务选择，权威
        evaluator 仍独立运行。它没有已知 trajectory 因果路径，但后续 runner 应彻底删除这条依赖。
      </p>
    </aside>
  </section>
  <figcaption>
    <b>证据边界</b>
    <span>
      两个 domain 各 32 道预选题，不是完整 benchmark；selected-task band 固定 T0，
      不覆盖 generation-tape uncertainty；句点是 counterfactual，不是官方聊天格式。
    </span>
    <code>FORMAL ea0607…1809 · EVAL 82b2fc…77ab · REPLAY 64/64</code>
  </figcaption>
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  [data-task-bootstrap-lab] .sampler-pipeline p, [data-task-bootstrap-lab] .evidence-pipeline p { margin: .32rem 0 0; opacity: .7; font-size: .43rem; }
  [data-task-bootstrap-lab] .sampler-pipeline > i, [data-task-bootstrap-lab] .evidence-pipeline > i { color: var(--orange); font: 760 .72rem/1 var(--font-mono); }
  [data-task-bootstrap-lab] .uniform-demo { margin-top: 1rem; overflow: hidden; border: 1px solid var(--line); background: #eee9df; }
  [data-task-bootstrap-lab] .uniform-demo > header { display: flex; justify-content: space-between; gap: 1rem; padding: .75rem .85rem; color: #e8efed; background: #29434a; }
  [data-task-bootstrap-lab] .uniform-demo > header span { display: block; color: #91c9be; font: .4rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .uniform-demo > header b { display: block; margin-top: .3rem; font: 710 .58rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .uniform-demo > header p { margin: 0; font-size: .45rem; }
  [data-task-bootstrap-lab] .uniform-bars { display: grid; grid-template-columns: repeat(8,1fr); gap: 1px; height: 9rem; padding: .75rem; background: #d9d4ca; }
  [data-task-bootstrap-lab] .uniform-bars article { display: grid; grid-template-rows: 1fr auto auto auto; min-width: 0; padding: .3rem; background: #f7f3ea; text-align: center; }
  [data-task-bootstrap-lab] .uniform-bars i { align-self: end; width: 58%; height: calc(var(--u) * 100%); min-height: .25rem; margin: 0 auto; background: linear-gradient(#8fc9bd,#2f776c); }
  [data-task-bootstrap-lab] .uniform-bars span, [data-task-bootstrap-lab] .uniform-bars code { margin-top: .18rem; color: #6a7773; font: .35rem/1 var(--font-mono); }
  [data-task-bootstrap-lab] .uniform-bars b { margin-top: .18rem; font: 700 .43rem/1 var(--font-mono); }
  [data-task-bootstrap-lab] .token-lanes { display: grid; gap: 1px; padding: 0 .75rem .75rem; background: #d9d4ca; }
  [data-task-bootstrap-lab] .token-lanes article { display: grid; grid-template-columns: 1.2fr 4fr; gap: 1px; background: #d9d4ca; }
  [data-task-bootstrap-lab] .token-lanes header { padding: .55rem; background: #eee9df; }
  [data-task-bootstrap-lab] .token-lanes header span, [data-task-bootstrap-lab] .token-lanes header b { display: block; }
  [data-task-bootstrap-lab] .token-lanes header span { color: var(--orange); font: .38rem/1 var(--font-mono); }
  [data-task-bootstrap-lab] .token-lanes header b { margin-top: .3rem; font-size: .48rem; }
  [data-task-bootstrap-lab] .token-lanes article > div { display: grid; grid-template-columns: repeat(8,1fr); gap: 1px; background: #d9d4ca; }
  [data-task-bootstrap-lab] .token-lanes i { display: grid; place-items: center; min-width: 0; padding: .45rem .15rem; background: #fffaf2; font: 680 .39rem/1 var(--font-mono); font-style: normal; }
  [data-task-bootstrap-lab] .token-lanes small { display: block; margin-bottom: .23rem; color: #77817d; font-size: .31rem; }
  [data-task-bootstrap-lab] .tb-note { margin-top: 1rem; padding: .85rem 1rem; border-left: .24rem solid var(--orange); background: #eee9df; }
  [data-task-bootstrap-lab] .tb-note.dark { color: #e8efed; border-left-color: #e0a17c; background: #29434a; }
  [data-task-bootstrap-lab] .tb-note b { font: 710 .58rem/1.3 var(--font-mono); }
  [data-task-bootstrap-lab] .tb-note p { margin: .42rem 0 0; opacity: .76; font-size: .51rem; line-height: 1.6; }
  [data-task-bootstrap-lab] .tb-switch-row { display: flex; justify-content: space-between; gap: 1rem; padding: .65rem; border: 1px solid var(--line); background: #eee9df; }
  [data-task-bootstrap-lab] .tb-switch-row > div { display: flex; gap: .35rem; }
  [data-task-bootstrap-lab] .tb-switch-row button, [data-task-bootstrap-lab] .task-pages button { padding: .55rem .75rem; color: #53625f; border: 1px solid var(--line); background: #fffaf2; font: 690 .45rem/1 var(--font-mono); cursor: pointer; }
  [data-task-bootstrap-lab] .tb-switch-row button[aria-pressed="true"], [data-task-bootstrap-lab] .task-pages button[aria-pressed="true"] { color: #edf3f1; border-color: #2f776c; background: #2f776c; }
  [data-task-bootstrap-lab] .condition-cards { display: grid; grid-template-columns: repeat(4,1fr); gap: 1px; margin-top: 1rem; border: 1px solid var(--line); background: var(--line); }
  [data-task-bootstrap-lab] .condition-cards article { padding: .75rem; background: #eee9df; }
  [data-task-bootstrap-lab] .condition-cards span { color: #66736f; font: .41rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .condition-cards b { display: block; margin-top: .38rem; color: #2d6d63; font: 740 .63rem/1.1 var(--font-mono); }
  [data-task-bootstrap-lab] .condition-cards p { margin: .3rem 0 0; color: #717b77; font-size: .44rem; }
  [data-task-bootstrap-lab] .forest { margin-top: 1rem; overflow: hidden; border: 1px solid var(--line); }
  [data-task-bootstrap-lab] .forest > header { display: grid; grid-template-columns: 1fr auto 1fr; padding: .6rem .8rem; color: #e9f0ee; background: #29434a; font: .4rem/1 var(--font-mono); }
  [data-task-bootstrap-lab] .forest > header span:last-child { text-align: right; }
  [data-task-bootstrap-lab] .forest > header b { color: #a8d2c9; }
  [data-task-bootstrap-lab] .forest [data-tb-forest] > article { display: grid; grid-template-columns: 1.25fr 2.8fr .65fr 1.2fr; gap: .65rem; align-items: center; padding: .72rem .8rem; border-bottom: 1px solid var(--line); background: #eee9df; }
  [data-task-bootstrap-lab] .forest [data-tb-forest] > article:last-child { border-bottom: 0; }
  [data-task-bootstrap-lab] .forest article > span { font: 650 .46rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .forest article > i { position: relative; height: .38rem; background: linear-gradient(to right,#deb194 0 49.7%,#29434a 49.7% 50.3%,#a9d3ca 50.3% 100%); }
  [data-task-bootstrap-lab] .forest article u { position: absolute; top: 50%; left: var(--band-left); width: calc(var(--band-right) - var(--band-left)); height: .44rem; background: #596d68; transform: translateY(-50%); text-decoration: none; }
  [data-task-bootstrap-lab] .forest article u.clear { background: #2f776c; }
  [data-task-bootstrap-lab] .forest article em { position: absolute; top: 50%; left: var(--point); width: .75rem; height: .75rem; border: .13rem solid #eee9df; border-radius: 50%; background: #bd6c46; box-shadow: 0 0 0 1px #29434a; transform: translate(-50%,-50%); }
  [data-task-bootstrap-lab] .forest article > b { font: 730 .48rem/1.2 var(--font-mono); text-align: right; }
  [data-task-bootstrap-lab] .forest article > small { color: #687570; font: .37rem/1.3 var(--font-mono); }
  [data-task-bootstrap-lab] .bootstrap-reading, [data-task-bootstrap-lab] .tape-reading { display: grid; grid-template-columns: repeat(3,1fr); gap: 1px; margin-top: 1rem; border: 1px solid var(--line); background: var(--line); }
  [data-task-bootstrap-lab] .bootstrap-reading article, [data-task-bootstrap-lab] .tape-reading article { padding: .8rem; background: #eee9df; }
  [data-task-bootstrap-lab] .bootstrap-reading article.result, [data-task-bootstrap-lab] .tape-reading article.result { color: #edf4f1; background: #2f776c; }
  [data-task-bootstrap-lab] .bootstrap-reading span, [data-task-bootstrap-lab] .tape-reading span { font: .4rem/1.2 var(--font-mono); opacity: .7; }
  [data-task-bootstrap-lab] .bootstrap-reading b, [data-task-bootstrap-lab] .tape-reading b { display: block; margin-top: .4rem; font: 730 .62rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .bootstrap-reading p, [data-task-bootstrap-lab] .tape-reading p { margin: .35rem 0 0; opacity: .7; font-size: .46rem; line-height: 1.45; }
  [data-task-bootstrap-lab] .task-controls, [data-task-bootstrap-lab] .tape-controls { display: grid; grid-template-columns: repeat(3,1fr); gap: 1px; border: 1px solid var(--line); background: var(--line); }
  [data-task-bootstrap-lab] .task-controls label, [data-task-bootstrap-lab] .task-controls article, [data-task-bootstrap-lab] .tape-controls label { padding: .72rem; background: #eee9df; }
  [data-task-bootstrap-lab] .task-controls span, [data-task-bootstrap-lab] .tape-controls span { display: block; color: #6e7975; font: .4rem/1 var(--font-mono); }
  [data-task-bootstrap-lab] .task-controls select, [data-task-bootstrap-lab] .tape-controls select { width: 100%; margin-top: .38rem; padding: .42rem; border: 1px solid var(--line); background: #fffaf2; font: 650 .5rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .task-controls b { display: block; margin-top: .45rem; font: 730 .61rem/1.1 var(--font-mono); }
  [data-task-bootstrap-lab] .task-pages { display: grid; grid-template-columns: repeat(4,1fr); gap: .35rem; margin-top: .7rem; }
  [data-task-bootstrap-lab] .task-table, [data-task-bootstrap-lab] .tape-matrix { margin-top: .75rem; overflow: hidden; border: 1px solid var(--line); }
  [data-task-bootstrap-lab] .task-table > header, [data-task-bootstrap-lab] .task-table [data-tb-task-rows] > article { display: grid; grid-template-columns: 1.4fr repeat(4,.55fr) 1.75fr; gap: 1px; background: var(--line); }
  [data-task-bootstrap-lab] .task-table > header > * { padding: .55rem .35rem; color: #e9f0ee; background: #29434a; font: 620 .36rem/1.2 var(--font-mono); text-align: center; }
  [data-task-bootstrap-lab] .task-table > header > *:first-child, [data-task-bootstrap-lab] .task-table > header > *:last-child { text-align: left; }
  [data-task-bootstrap-lab] .task-table [data-tb-task-rows] article > span { display: flex; gap: .5rem; align-items: center; min-width: 0; padding: .55rem; background: #eee9df; }
  [data-task-bootstrap-lab] .task-table article > span small { color: var(--orange); font: .36rem/1 var(--font-mono); }
  [data-task-bootstrap-lab] .task-table article > span b { overflow: hidden; font: 650 .43rem/1.2 var(--font-mono); text-overflow: ellipsis; white-space: nowrap; }
  [data-task-bootstrap-lab] .task-table article > i { display: grid; place-items: center; padding: .4rem; background: #f0dfd5; font-style: normal; }
  [data-task-bootstrap-lab] .task-table article > i.pass { background: #dcebe5; }
  [data-task-bootstrap-lab] .task-table article > i b { font: 760 .52rem/1 var(--font-mono); }
  [data-task-bootstrap-lab] .task-table article > i small { margin-top: .22rem; color: #6b7773; font: .32rem/1 var(--font-mono); }
  [data-task-bootstrap-lab] .task-table article > strong { display: grid; align-content: center; padding: .45rem .55rem; background: #eee9df; }
  [data-task-bootstrap-lab] .task-table article > strong.positive { background: #dcebe5; }
  [data-task-bootstrap-lab] .task-table article > strong.negative { background: #f0dfd5; }
  [data-task-bootstrap-lab] .task-table article > strong b { font: 700 .43rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .task-table article > strong small { margin-top: .25rem; color: #67736f; font: .33rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .transition-cards { display: grid; grid-template-columns: repeat(4,1fr); gap: 1px; margin-top: .75rem; border: 1px solid var(--line); background: var(--line); }
  [data-task-bootstrap-lab] .transition-cards article { padding: .7rem; background: #eee9df; }
  [data-task-bootstrap-lab] .transition-cards article.positive { background: #dcebe5; }
  [data-task-bootstrap-lab] .transition-cards article.negative { background: #f0dfd5; }
  [data-task-bootstrap-lab] .transition-cards span { color: #65736e; font: .4rem/1 var(--font-mono); }
  [data-task-bootstrap-lab] .transition-cards b { display: block; margin-top: .35rem; font: 730 .6rem/1.1 var(--font-mono); }
  [data-task-bootstrap-lab] .tape-matrix > header, [data-task-bootstrap-lab] .tape-matrix [data-tb-tape-rows] > article, [data-task-bootstrap-lab] .tape-matrix > footer { display: grid; grid-template-columns: 1.45fr repeat(4,.75fr) 1fr; gap: 1px; background: var(--line); }
  [data-task-bootstrap-lab] .tape-matrix > header > *, [data-task-bootstrap-lab] .tape-matrix > footer > * { padding: .58rem .4rem; color: #e8efed; background: #29434a; font: 650 .4rem/1.2 var(--font-mono); text-align: center; }
  [data-task-bootstrap-lab] .tape-matrix > header > *:first-child, [data-task-bootstrap-lab] .tape-matrix > footer > *:first-child { text-align: left; }
  [data-task-bootstrap-lab] .tape-matrix [data-tb-tape-rows] article > span, [data-task-bootstrap-lab] .tape-matrix [data-tb-tape-rows] article > strong { padding: .7rem .55rem; background: #eee9df; font: 650 .43rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .tape-matrix [data-tb-tape-rows] article > b { display: grid; place-items: center; color: #29433e; background: color-mix(in srgb,#8fc8bd calc(var(--strength) * 72%),#f7f3ea); font: 730 .5rem/1 var(--font-mono); }
  [data-task-bootstrap-lab] .tape-matrix [data-tb-tape-rows] article > b.negative { color: #5e3427; background: color-mix(in srgb,#dc9d78 calc(var(--strength) * 72%),#f7f3ea); }
  [data-task-bootstrap-lab] .tape-matrix [data-tb-tape-rows] article > b.zero { background: #f7f3ea; }
  [data-task-bootstrap-lab] .tape-matrix [data-tb-tape-rows] article > strong { text-align: center; }
  [data-task-bootstrap-lab] .replay-fields { display: grid; grid-template-columns: repeat(4,1fr); gap: 1px; margin-top: 1rem; border: 1px solid var(--line); background: var(--line); }
  [data-task-bootstrap-lab] .replay-fields article { padding: .7rem; background: #dfece7; }
  [data-task-bootstrap-lab] .replay-fields span { display: block; color: #58716b; font: .36rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .replay-fields b { display: block; margin-top: .32rem; color: #28675c; font: 740 .58rem/1.1 var(--font-mono); }
  [data-task-bootstrap-lab] .replay-fields i { display: block; margin-top: .23rem; color: #568178; font: .34rem/1 var(--font-mono); }
  [data-task-bootstrap-lab] .failure-ledger { display: grid; grid-template-columns: 1fr 1fr; gap: .8rem; margin-top: 1rem; }
  [data-task-bootstrap-lab] .failure-ledger > article { overflow: hidden; border: 1px solid var(--line); background: #eee9df; }
  [data-task-bootstrap-lab] .failure-ledger header { display: flex; justify-content: space-between; padding: .7rem; color: #e8efed; background: #29434a; }
  [data-task-bootstrap-lab] .failure-ledger header span { font: .4rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .failure-ledger header b { font: 720 .5rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .failure-ledger article > div { display: grid; grid-template-columns: 1.2fr 2fr .35fr; gap: .55rem; align-items: center; padding: .55rem .7rem; border-bottom: 1px solid var(--line); }
  [data-task-bootstrap-lab] .failure-ledger article > div:last-child { border-bottom: 0; }
  [data-task-bootstrap-lab] .failure-ledger div > span { font: .4rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .failure-ledger div > i { height: .36rem; background: #d8d2c8; }
  [data-task-bootstrap-lab] .failure-ledger div > i u { display: block; width: calc(var(--share) * 100%); height: 100%; background: #2f776c; text-decoration: none; }
  [data-task-bootstrap-lab] .failure-ledger div > b { font: 700 .44rem/1 var(--font-mono); text-align: right; }
  [data-task-bootstrap-lab] .deviation-ledger { display: grid; grid-template-columns: 1fr 1fr; gap: .8rem; margin-top: 1rem; }
  [data-task-bootstrap-lab] .deviation-ledger article { padding: .85rem; border: 1px solid var(--line); background: #eee9df; }
  [data-task-bootstrap-lab] .deviation-ledger article.reported-process-deviation { border-left: .25rem solid #bd6c46; }
  [data-task-bootstrap-lab] .deviation-ledger article.corrected-before-output { border-left: .25rem solid #2f776c; }
  [data-task-bootstrap-lab] .deviation-ledger span { color: var(--orange); font: .39rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .deviation-ledger b { display: block; margin-top: .38rem; font: 710 .58rem/1.2 var(--font-mono); text-transform: uppercase; }
  [data-task-bootstrap-lab] .deviation-ledger p { margin: .38rem 0 0; color: #69746f; font-size: .48rem; line-height: 1.55; }
  [data-task-bootstrap-lab] .artifact-chain { display: grid; grid-template-columns: repeat(4,1fr); gap: 1px; margin-top: 1rem; border: 1px solid var(--line); background: var(--line); }
  [data-task-bootstrap-lab] .artifact-chain article { min-width: 0; padding: .65rem; background: #eee9df; }
  [data-task-bootstrap-lab] .artifact-chain span { color: var(--orange); font: .36rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] .artifact-chain b { display: block; margin-top: .32rem; overflow: hidden; font: 650 .4rem/1.2 var(--font-mono); text-overflow: ellipsis; }
  [data-task-bootstrap-lab] > figcaption { display: grid; grid-template-columns: auto 1fr auto; gap: 1rem; align-items: center; padding: .9rem 1.1rem; color: #e2eae8; background: #203a42; }
  [data-task-bootstrap-lab] > figcaption b { color: #8dc8bd; font: 720 .48rem/1.2 var(--font-mono); }
  [data-task-bootstrap-lab] > figcaption span { font-size: .5rem; line-height: 1.5; }
  [data-task-bootstrap-lab] > figcaption code { color: #dda988; font: .38rem/1.3 var(--font-mono); }
  @media (max-width: 900px) {
    [data-task-bootstrap-lab] .tb-head, [data-task-bootstrap-lab] .tb-panel-lead { grid-template-columns: 1fr; }
    [data-task-bootstrap-lab] .tb-ledger { grid-template-columns: repeat(3,1fr); }
    [data-task-bootstrap-lab] .tb-tabs { grid-template-columns: repeat(3,1fr); }
    [data-task-bootstrap-lab] .sampler-pipeline, [data-task-bootstrap-lab] .evidence-pipeline { grid-template-columns: 1fr; }
    [data-task-bootstrap-lab] .sampler-pipeline > i, [data-task-bootstrap-lab] .evidence-pipeline > i { transform: rotate(90deg); text-align: center; }
    [data-task-bootstrap-lab] .replay-fields, [data-task-bootstrap-lab] .artifact-chain { grid-template-columns: 1fr 1fr; }
    [data-task-bootstrap-lab] > figcaption { grid-template-columns: 1fr; }
  }
  @media (max-width: 640px) {
    [data-task-bootstrap-lab] .tb-panel { padding: .9rem; }
    [data-task-bootstrap-lab] .tb-head { padding: 1.2rem; }
    [data-task-bootstrap-lab] .tb-tabs, [data-task-bootstrap-lab] .pairing-compare,
    [data-task-bootstrap-lab] .tb-switch-row, [data-task-bootstrap-lab] .task-controls,
    [data-task-bootstrap-lab] .tape-controls, [data-task-bootstrap-lab] .bootstrap-reading,
    [data-task-bootstrap-lab] .tape-reading, [data-task-bootstrap-lab] .failure-ledger,
    [data-task-bootstrap-lab] .deviation-ledger { display: grid; grid-template-columns: 1fr; }
    [data-task-bootstrap-lab] .tb-switch-row > div { display: grid; grid-template-columns: 1fr 1fr; }
    [data-task-bootstrap-lab] .condition-cards, [data-task-bootstrap-lab] .transition-cards { grid-template-columns: 1fr 1fr; }
    [data-task-bootstrap-lab] .uniform-demo > header { display: grid; }
    [data-task-bootstrap-lab] .uniform-bars { grid-template-columns: repeat(4,1fr); height: auto; }
    [data-task-bootstrap-lab] .uniform-bars article { min-height: 6.5rem; }
    [data-task-bootstrap-lab] .token-lanes article { grid-template-columns: 1fr; }
    [data-task-bootstrap-lab] .token-lanes article > div { overflow-x: auto; }
    [data-task-bootstrap-lab] .forest [data-tb-forest] > article { grid-template-columns: 1fr; }
    [data-task-bootstrap-lab] .forest article > i { margin: .45rem 0; }
    [data-task-bootstrap-lab] .task-table, [data-task-bootstrap-lab] .tape-matrix { overflow-x: auto; }
    [data-task-bootstrap-lab] .task-table > header, [data-task-bootstrap-lab] .task-table [data-tb-task-rows] > article { min-width: 44rem; }
    [data-task-bootstrap-lab] .tape-matrix > header, [data-task-bootstrap-lab] .tape-matrix [data-tb-tape-rows] > article, [data-task-bootstrap-lab] .tape-matrix > footer { min-width: 36rem; }
  }
 </style>
@@ -7,6 +7,7 @@ import DeepSeekBehaviorLab from "@/components/DeepSeekBehaviorLab.astro";
 import DeepSeekCompletionDepthLab from "@/components/DeepSeekCompletionDepthLab.astro";
 import DeepSeekSamplingLab from "@/components/DeepSeekSamplingLab.astro";
 import DeepSeekCrossSourceSamplingLab from "@/components/DeepSeekCrossSourceSamplingLab.astro";
 import DeepSeekTaskBootstrapLab from "@/components/DeepSeekTaskBootstrapLab.astro";
 import { deepseekBranches, deepseekLedgers, deepseekPaperChain, deepseekWaves } from "@/data/deepseek";
 const toc = [
@@ -37,21 +38,22 @@ const toc = [
  ["24", "completion-depth", "Chat：完成度与全深度"],
  ["25", "sampling", "Chat：多种子采样稳健性"],
  ["26", "cross-source-sampling", "Chat：跨题采样与统计单位"],
-  ["27", "branches", "别漏掉旁支"],
+  ["27", "task-bootstrap-crn", "Chat：任务 bootstrap 与共同随机数"],
-  ["28", "audit", "事实、推导与教学模型"],
+  ["28", "branches", "别漏掉旁支"],
  ["29", "audit", "事实、推导与教学模型"],
  ["↳", "papers", "六十节点阅读链"],
 ];
 ---
 <BaseLayout
  title="DeepSeek 技术谱系与真实权重深读：从 Dense、MoE、MLA 到 R1 与 V4"
-  description="用二十四张问题账、十次技术转向、二十一个交互实验、真实 V2-Lite Base / Chat 权重、512-token 完成度评测、29 阶段隐藏状态、26 层 MoE 路由追踪，以及单题与跨题两轮各 256 条采样，完整理解 DeepSeek 的 MoE、MLA、FP8、DualPipe、GRPO、R1、V3.2 与 V4。"
+  description="用二十四张问题账、十次技术转向、二十二个交互实验、真实 V2-Lite Base / Chat 权重、512-token 完成度评测、29 阶段隐藏状态、26 层 MoE 路由追踪，以及 864 条分层采样与显式共同随机数审计，完整理解 DeepSeek 的 MoE、MLA、FP8、DualPipe、GRPO、R1、V3.2 与 V4。"
  section="deepseek"
 >
  <header class="page-hero deepseek-hero">
    <div class="page-hero-inner">
      <div>
-        <p class="eyebrow"><span>SPOTLIGHT / DEEPSEEK · ROUND 07</span> SOURCE COVERAGE × SAMPLING × FULL DEPTH</p>
+        <p class="eyebrow"><span>SPOTLIGHT / DEEPSEEK · ROUND 08</span> TASK BOOTSTRAP × COMMON RANDOM NUMBERS × FULL DEPTH</p>
        <h1>不要背模型名<br />要看懂每次为什么转向</h1>
        <p class="lead">
          这不是七篇报告的摘要，而是一套可追问、可计算、可反驳的技术谱系：
@@ -63,9 +65,9 @@ const toc = [
        <div><dt>SPAN</dt><dd>2024.01 → 2026.06</dd></div>
        <div><dt>LEDGERS</dt><dd>24 张问题账</dd></div>
        <div><dt>LINEAGE</dt><dd>10 次技术转向</dd></div>
-        <div><dt>LABS</dt><dd>21 个可操作实验</dd></div>
+        <div><dt>LABS</dt><dd>22 个可操作实验</dd></div>
        <div><dt>EVIDENCE</dt><dd>60 个一手 / 官方节点</dd></div>
-        <div><dt>STATUS</dt><dd>七轮 · 512 条采样</dd></div>
+        <div><dt>STATUS</dt><dd>八轮 · 864 条采样</dd></div>
      </dl>
    </div>
  </header>
@@ -840,8 +842,22 @@ const toc = [
        <DeepSeekCrossSourceSamplingLab />
      </section>
      <section class="article-section" id="task-bootstrap-crn">
        <p class="eyebrow"><span>27</span> TASKS ARE NOT RANDOM TAPES</p>
        <h2>换一道题与换一条随机带，不是同一种不确定性：把 32 题 bootstrap 与真正的共同随机数接起来</h2>
        <p class="lede">
          Round 07 把 source 提升为覆盖单位，却仍只有每域四题；四行也只是共享 batch
          seed，不是真正共享同一概率分位。Round 08 在 HumanEval 与 GSM8K 各冻结 32 题，
          主分析统一使用 T0；另取每域四题跑 T0–T3。每个 source、tape、step 的
          <code>uₜ</code> 由 SHA-256 显式派生，四个 prompt 条件读取同一个
          <code>uₜ</code>，再穿过各自的 <code>temperature=.3 / top_p=.95</code>
          CDF。这样可以把任务差异、sampling tape 差异与 prompt 条件差异放进不同账本。
        </p>
        <DeepSeekTaskBootstrapLab />
      </section>
      <section class="article-section" id="branches">
-        <p class="eyebrow"><span>27</span> THE MAIN LINE IS NOT THE WHOLE TREE</p>
+        <p class="eyebrow"><span>28</span> THE MAIN LINE IS NOT THE WHOLE TREE</p>
        <h2>如果只读 V2 → V3 → R1 → V4，会漏掉五条反过来影响主线的旁支</h2>
        <div class="branch-grid">
          {deepseekBranches.map(([name, line, text, url]) => (
@@ -861,7 +877,7 @@ const toc = [
      </section>
      <section class="article-section" id="audit">
-        <p class="eyebrow"><span>28</span> EVIDENCE AUDIT</p>
+        <p class="eyebrow"><span>29</span> EVIDENCE AUDIT</p>
        <h2>同一张页面里有三种知识，它们的语气必须不同</h2>
        <div class="audit-grid">
          <article class="reported">
@@ -145,18 +145,18 @@ const paths = [
      </a>
      <a class="release-card deepseek-release" href="/deepseek/">
        <div>
-          <p class="eyebrow"><span>NEW / DEEPSEEK ROUND 07</span> CROSS-SOURCE SAMPLING · SOURCE-BLOCKED AUDIT</p>
+          <p class="eyebrow"><span>NEW / DEEPSEEK ROUND 08</span> TASK BOOTSTRAP · EXPLICIT COMMON RANDOM NUMBERS</p>
          <h2>从 Dense 到百万上下文：每次创新都在偿还上一代最贵的一张账</h2>
          <p>
-            单题抽 64 次仍然只有一道题。新一轮保持 256 条预算不变，改用 16 条预先冻结的
+            从每域四题扩到 HumanEval / GSM8K 各 32 题，并把“同 seed”升级为显式共享
-            source：Math 四题从 8 / 16 到 16 / 16，Code 四题也从 8 / 16 到 16 / 16；
+            uniform tape：352 条正式输出、10,000 次选定任务配对 bootstrap 与 64 条
-            English 甚至出现域均值与 3 / 4 source 方向相反。新进程 R0 仍 64 / 64 格 exact。
+            十二字段新进程重放。正确性区间都跨零，但输出长度揭示 Code 与 Math 方向相反。
          </p>
        </div>
        <dl>
          <div><dt>LINEAGE</dt><dd>1991 → 2026 · 10 次转向</dd></div>
          <div><dt>NODES</dt><dd>60 个一手 / 官方节点</dd></div>
-          <div><dt>LAB</dt><dd>21 · Base / Chat / sampling</dd></div>
+          <div><dt>LAB</dt><dd>22 · Base / Chat / sampling</dd></div>
        </dl>
        <span class="release-arrow" aria-hidden="true">进入 DeepSeek 完整技术谱系 →</span>
      </a>
@@ -15,7 +15,7 @@ const workstreams = [
  { label: "表示、位置与残差高速公路", value: 81, next: "加入真实 hidden-state / norm traces、长上下文位置外推复现与更多深层稳定性消融" },
  { label: "Scaling Laws", value: 74, next: "加入真实拟合复现、置信区间与更多模型族对照" },
  { label: "数据工程与预训练配方", value: 73, next: "逐图精读 FineWeb / DCLM，加入真实去重与 mixture traces" },
-  { label: "DeepSeek 专题", value: 99, next: "把跨题采样扩大到可做 task-level bootstrap，再推进 per-row RNG、干预式 mediation、SM90 FlashMLA 与 R1-like RL" },
+  { label: "DeepSeek 专题", value: 99, next: "推进干预式 mediation、SM90 FlashMLA、FP8 / pipeline traces 与 R1-like RL 小模型复现" },
  { label: "指令微调与人类偏好", value: 75, next: "加入真实偏好分歧样本、RM 长度偏置与 PPO/DPO 小模型复现" },
  { label: "推理与测试时扩展", value: 76, next: "真实模型采样曲线、PRM 案例与逐篇图表精读" },
  { label: "工具使用与长程 Agent", value: 74, next: "补真实环境 traces、cross-harness 对照、Agent RL 训练曲线与安全案例" },
@@ -97,12 +97,12 @@ const workstreams = [
      <article><span>✓</span><h3>K3 报告已结构化拆解</h3><p>47 页报告目录、151 条参考来源和架构/后训练/系统主线已经提取。</p></article>
      <article><span>✓</span><h3>17 专题知识图</h3><p>从语言模型基础到评测安全，包含先修依赖和三条贯穿案例。</p></article>
      <article><span>✓</span><h3>编辑式网站系统</h3><p>响应式导航、章节模板、侧栏、进度、论文链和证据提示组件。</p></article>
-      <article><span>✓</span><h3>八十八个原创交互视图</h3><p>K3 三轴图、八联报告实验与四联开放工件实验，DeepSeek 四联公式实验、十三联 Base 工件实验、Chat 行为、completion/full-depth、multi-seed 与 cross-source sampling 四轮实验，以及语言模型前史、Transformer、表示深度、长上下文、MoE、推理、Agent、多模态、训练系统、推理服务、Scaling、数据工程、数值、Alignment 与评测安全专题。</p></article>
+      <article><span>✓</span><h3>八十九个原创交互视图</h3><p>K3 三轴图、八联报告实验与四联开放工件实验，DeepSeek 四联公式实验、十三联 Base 工件实验、Chat 行为、completion/full-depth、multi-seed、cross-source 与 task-bootstrap CRN 五轮实验，以及语言模型前史、Transformer、表示深度、长上下文、MoE、推理、Agent、多模态、训练系统、推理服务、Scaling、数据工程、数值、Alignment 与评测安全专题。</p></article>
      <article><span>✓</span><h3>十七篇首版长文</h3><p>K3、语言模型前史、Transformer、表示/位置/残差、DeepSeek、Scaling、数据工程、长上下文、MoE、后训练、推理、Agent、原生多模态、训练系统、推理服务、数值优化与评测安全专题。</p></article>
      <article><span>✓</span><h3>语言模型前史深度专题</h3><p>八张独立问题账、33 个正式节点、20 段长文与概率—向量—记忆—对齐四联实验。</p></article>
      <article><span>✓</span><h3>Transformer 深度专题</h3><p>十张独立问题账、40 个正式节点、21 段正文与 QKV—Mask—多头位置—Block 成本四联实验。</p></article>
      <article><span>✓</span><h3>表示、位置与残差高速公路深度专题</h3><p>二十张问题账、66 个一手节点、DeepSeek/Kimi 双谱系，以及 Token—位置—Norm—Residual/FFN 四联实验。</p></article>
-      <article><span>✓</span><h3>DeepSeek 七轮真实权重里程碑</h3><p>继单题多 seed 之后，保持 256 条预算并把覆盖扩大到 16 条预先冻结的 source：250 条 natural EOS、247 个 unique trajectories；Math 四题为 14/16、16/16、8/16、9/16，Code 四题为 16/16、8/16、12/16、16/16。source-blocked 方向揭示 English 均值与多数题相反，新进程 R0 八项合同字段 64 / 64 exact。</p></article>
+      <article><span>✓</span><h3>DeepSeek 八轮真实权重里程碑</h3><p>把覆盖扩到 HumanEval / GSM8K 各 32 条冻结任务，用显式 SHA-256 uniform tape 驱动四个条件的共同随机数采样：352 条正式输出中 343 条 natural EOS、320 个 unique trajectories；10,000 次选定任务配对 bootstrap 的正确性区间均跨零，长度则揭示 Code 与 Math 的相反方向。新进程十二字段重放 64 / 64 exact。</p></article>
      <article><span>✓</span><h3>Kimi K3 技术报告二轮深读</h3><p>三十二张问题账、Figure 1–16 / Table 1–5 审计、100 节点阅读链，以及 Delta—Decay—AttnRes—LatentMoE—SiTU—QB—MOPD—Cache 八联实验。</p></article>
      <article><span>✓</span><h3>Kimi K3 三轮开放工件里程碑</h3><p>固定官方 revisions，审计 96 个 shards、497,220 个 tensor entries 与真实 KDA / MLA / MoE / MoonViT shapes；四联实验分开显示层型、tensor anatomy、参数范围和复现边界。</p></article>
      <article><span>✓</span><h3>FlashKDA RTX 5090 执行闸门</h3><p>隔离 CUDA 13.0 / glibc 2.39 编译 sm_120a wheel；6/6 官方参考逐元素相等，并完成 fixed / varlen、三种 state mode 的 1,800 个 CUDA Event samples。</p></article>
@@ -134,7 +134,7 @@ const workstreams = [
    <div class="queue-table">
      <div class="head"><b>优先级</b><b>专题</b><b>本轮交付</b><b>完成闸门</b></div>
      <div><span>P0</span><strong>K3 三轮</strong><p>开放权重 traces → FlashKDA / AttnRes / MoE 真实行为 → Figure 1–16 数值重绘与独立复现</p><em>运行证据 + 逐图复现</em></div>
-      <div><span>P0</span><strong>DeepSeek 七轮后续</strong><p>扩大到 task-level bootstrap → per-row RNG 对照 → 干预式 mediation → SM90 FlashMLA / FP8 / pipeline traces → R1-like RL 小模型复现</p><em>运行证据 + 独立复现</em></div>
+      <div><span>P0</span><strong>DeepSeek 八轮后续</strong><p>干预式 mediation → SM90 FlashMLA / FP8 / pipeline traces → R1-like RL 小模型复现</p><em>运行证据 + 独立复现</em></div>
      <div><span>P0</span><strong>Transformer 二轮</strong><p>多头电路逐图 → Pre/Post-LN 真实 traces → Flash/KV 配置与 kernel 对照</p><em>逐图笔记 + 实测边界</em></div>
      <div><span>P0</span><strong>表示、位置与残差二轮</strong><p>真实 hidden-state / norm traces → 长上下文位置外推 → mHC / AttnRes 深层稳定性消融</p><em>可复现实验 + 逐图笔记</em></div>
      <div><span>P0</span><strong>语言模型前史二轮</strong><p>Kneser–Ney / LSTM / Bahdanau 逐图 → 真实小语料复现 → tokenizer 公平性</p><em>可复现实验 + 逐图笔记</em></div>
@@ -237,6 +237,10 @@ const workstreams = [
      <div><time>2026-07-30</time><b>任务总数必须展开为逐题矩阵</b><p>Math 与 Code 的四题都从 8/16 跨到 16/16；跨不同 GSM8K gold 的 final-answer frequency 禁止聚合。</p></div>
      <div><time>2026-07-30</time><b>均值与 source 方向同时展示</b><p>English 句点 contrast 均值 −8.5，但 3/4 source 为正；Code 两道题 +1/−1 interaction 在域均值 0 中抵消。</p></div>
      <div><time>2026-07-30</time><b>跨题 sampling 仍要求精确复跑</b><p>R0/R1 63/64 同格分叉；新进程 R0 的 seed、prompt、token、text、stop 与 CPU/CUDA RNG pre-state 八字段 64/64 exact。</p></div>
      <div><time>2026-07-30</time><b>共同随机数必须共享 uniform tape</b><p>仅把生成器 seed 重置为同一个值并不等于 CRN；Round 08 用 SHA-256 逐 task/tape/step 生成显式 u，并让四个条件把同一 u 映射到各自 token CDF。</p></div>
      <div><time>2026-07-30</time><b>bootstrap 的统计单位是任务</b><p>HumanEval 与 GSM8K 各 32 题分开做 10,000 次成对任务重采样；这是冻结题集的敏感性带，不冒充 benchmark 总体或跨 seed 置信区间。</p></div>
      <div><time>2026-07-30</time><b>正确性未定，不等于没有行为效应</b><p>八个正确性 contrast 区间全部跨零；system-at-period 的长度带在 Code 为 −130.9 [−178.7,−83.2]，在 Math 为 +25.1 [7.9,44.5]，明确显示任务域交互。</p></div>
      <div><time>2026-07-30</time><b>复现合同扩到随机带本身</b><p>64 条新进程重放逐字段核对 uniform hash、token IDs、文本、停止状态与 RNG；十二字段全部 64/64 exact，并公开评分文件提前加载这一流程偏差。</p></div>
    </div>
  </section>