Classify consumer learning and health products with preorder and revenue-scope exclusions

Reviewed-on: #45

AGENTS.md

@@ -4,6 +4,7 @@
 - Web 技术栈为 TypeScript、React、Tailwind CSS、Next.js Pages Router；不要切换到 App Router。
 - 使用 pnpm 管理 JavaScript workspace，使用 uv 管理 `tools/figures` 的 Python 项目，保留两套锁文件。
 - 文档正文、目录元数据与修订记录以站点现有 SQLite 数据库为唯一权威源，使用文档 API 或 docs 管理工具直接更新；不得把新正文、目录 JSON、数据库文件或日常内容快照提交到仓库。历史 Git 文档只用于显式的一次性迁移，不作为运行时种子。Python 图形源码放在 `tools/figures/src/endor_figures`，可复现的 SVG 输出放在 `apps/docs/public/figures`。
+- 编辑已有文章时优先使用章节 API：先读取章节目录与内部摘要，据此选择需要读取正文的章节，再提交单章或多章操作。摘要缺失时先读对应正文并补充；修改观点前必须核对该章原文，检查读取版本与提交 revision 一致。摘要只写入内部章节数据，不放入网页正文、目录摘要或 Git；改写正文时同步更新摘要。完整操作与 Python 客户端用法见 README 的「先读摘要，再按章节编辑」。
 - 未确认的架构提议明确标注为草案，不能表述为现有实现。
 - 本地开发与预览只监听 `127.0.0.1`。部署容器监听 `0.0.0.0:3000`，服务器入口默认 18135，可通过独立 production.env 配置。
 - 对 gitea.entroia.com 仅使用 codex-bot，提交身份为 Codex Bot <codex-bot@gitea.entroia.local>。SSH 使用隔离配置：`ssh -F /dev/null -o IdentitiesOnly=yes -i /Users/zzyang/.codex/credentials/gitea-entroia/codex_entroia_ed25519 -p 22222`。

README.md

@@ -75,6 +75,10 @@ sudo /opt/endorphin-tabularium-docs/docs save < "$doc_work_dir/document.json"
 | `GET /api/documents/item/<slug>?revision=1` | 读取指定修订的完整正文 |
 | `POST /api/documents` | 保存一个完整文档 JSON；字段与 `docs save` 相同 |
 | `POST /api/documents/batch` | 保存 `{ "documents": [...] }`，每个请求 1—100 篇 |
+| `GET /api/documents/sections/<slug>` | 读取章节 ID、标题和内部摘要，不返回正文 |
+| `GET /api/documents/sections/<slug>?section=<id>` | 读取选中章节正文；重复 `section` 可选多章 |
+| `GET /api/documents/sections/<slug>?revision=2` | 读取对应修订的章节目录；也可搭配 `section` |
+| `POST /api/documents/sections/<slug>` | 原子执行章节新增、更新、删除和移动 |
 
 写入使用 `Authorization: Bearer <文档 API Key>`。新建传 `revision: 0`；更新先读取当前全文，再使用读到的 revision。每篇正文最多 2,000,000 个 JavaScript 字符单位；每次请求最多 8 MiB。目录和修订分页的 `limit` 范围为 1—100，响应包含 `total` 和 `nextOffset`。
 
@@ -82,6 +86,52 @@ sudo /opt/endorphin-tabularium-docs/docs save < "$doc_work_dir/document.json"
 
 建议写入时提供 `Idempotency-Key`，由 1—128 个字母、数字或 `._:-` 组成。同一个 Key、相同请求 ID 和相同输入重试时，返回原回执并标记 `replayed: true`，不追加修订；相同 ID 改换输入会冲突。回执描述原请求，不代表之后没有其他修改；需要当前内容时重新读取文档。改过正文或 revision 后，应使用新的请求 ID。
 
+### 先读摘要，再按章节编辑
+
+章节接口使用相同的文档 Key 和读写权限。编辑流程为：先读章节目录及摘要，选择需要深入的章节，再读取这些章节的正文，最后提交一组操作。不要只凭摘要修改未读正文；目录与正文响应的 `revision` 应一致，否则重新读取。接口不调用模型生成摘要：由编辑 Agent 阅读正文、撰写摘要，并在编辑请求中保存。
+
+章节按行首 Markdown ATX 二级标题（`##`）划分，三级至六级标题随所属章节一起编辑；代码围栏内的标题不会切章。二级标题之前的文档标题及导言属于固定 ID `preamble`，可以修改但不能删除或移动。Setext 标题、带缩进或引用、列表标记的标题不作为章节边界。正文无行首二级标题时，全文作为导言处理。拆分及组合保持换行和未编辑内容原样，不自动格式化 Markdown。
+
+目录中的每章包含 `id`、`title`、`position`、`summary`、`summaryStatus` 和 `contentHash`。摘要为编辑内部数据，单独保存在 SQLite 的 `document_sections` 和 `document_section_revisions` 中，不加入普通文档响应、网页 props、搜索索引或页面正文。完整数据库备份包含这些表；`docs export` 仍只导出当前文档，不包含内部摘要和修订。
+
+旧文章无需重写，首次读取即可按原文返回目录，初始摘要为空，`summaryStatus` 为 `missing`。Agent 应按需读取这些章节，生成摘要并通过 `update` 保存；保存后状态为 `current`。这里的 `current` 仅表示摘要与该版正文一起保存，不保证模型的总结正确。全文接口仍兼容；未变化章节保留摘要与 ID，正文变化的章节清空摘要，等待更新。全文改名且改写章节时可能产生新 ID，因此全文保存后应重新读取目录。
+
+章节编辑请求示例（示例 ID 需要替换为目录实际返回的值）：
+
+```json
+{
+  "revision": 3,
+  "operations": [
+    {
+      "op": "update",
+      "id": "s_existing",
+      "content": "## 原有章节\n\n修改后的正文。\n\n",
+      "summary": "本章说明修改后的核心观点与边界。"
+    },
+    {
+      "op": "insert",
+      "after": "s_existing",
+      "content": "## 新章节\n\n新增论证。\n\n",
+      "summary": "补充上一章尚未覆盖的论证。"
+    }
+  ]
+}
+```
+
+`update` 可只改 `summary`，也可改正文；正文改变时若不提供新摘要，旧摘要会清空。`insert` 必须提供正文和非空摘要；摘要最多 8000 个 JavaScript 字符单位。`delete` 使用 `{ "op": "delete", "id": "s_existing" }`；`move` 使用 `{ "op": "move", "id": "s_existing", "after": "preamble" }`。按数组顺序执行，最多 100 个操作；新章 ID 由服务器生成，在回执目录中返回。章正文应从自己的 `##` 标题开始，只包含一个二级标题，并保留与下一章之间的换行；跨章代码围栏或章节边界改变会被拒绝，拆章请使用多个操作。
+
+章节变更、内部摘要、组合后的全文和历史快照在同一事务内提交。每批操作只增加一次文章 `revision`；摘要单独更新也增加版本号。后续操作失败或全文版本过期时全部回滚。写入沿用幂等规则，回执包含新版本、章节目录和 `replayed`；章节接口与全文接口的幂等记录分开。修改章节不会改变其 ID，移动亦保留 ID；删除后的正文和摘要仍可从对应的旧修订读取。启用前的历史修订只有原文，摘要显示缺失。
+
+Python 客户端对应命令：
+
+```sh
+python3 scripts/documents-client.py outline endor/endor-purpose-draft
+python3 scripts/documents-client.py sections endor/endor-purpose-draft preamble s_existing
+python3 scripts/documents-client.py edit-sections endor/endor-purpose-draft /private/path/edit.json --request-id chapter-edit-1
+```
+
+`outline` 和 `sections` 支持 `--revision`；请求文件只包含 `revision` 和 `operations`，不要把内部摘要文件提交到 Git。
+
 ### 创建、查看与撤销 Key
 
 管理员只需通过已验证的服务器连接进行 Key 管理：

apps/docs/scripts/auth-cli.ts

@@ -182,10 +182,10 @@ async function main() {
   assert.equal(researchResponse.status, 200, "authenticated research API");
   assert.ok(researchResponse.headers.get("cache-control")?.includes("no-store"));
   const research = await researchResponse.json();
-  assert.equal(research.companies.length, 1021);
+  assert.equal(research.companies.length, 1307);
   assert.equal(research.categories.length, 11);
-  assert.equal(research.evidence.length, 1215);
-  assert.equal(research.observations.length, 189);
+  assert.equal(research.evidence.length, 5331);
+  assert.equal(research.observations.length, 495);
   assert.equal((await request("/api/research/ai-companies", { method: "POST", headers })).status, 405);
   const researchPage = await request("/docs/other-research/ai-company-research/overview", { headers });
   assert.equal(researchPage.status, 200);

apps/docs/src/components/AICompanyResearch.tsx

@@ -1,15 +1,18 @@
 import { useEffect, useMemo, useRef, useState } from "react";
 import { useRouter } from "next/router";
 import IncomeDistribution from "./IncomeDistribution";
+import ArrDisclosures from "./ArrDisclosures";
+import BusinessModelAssessment from "./BusinessModelAssessment";
+import ResearchCandidates from "./ResearchCandidates";
 import {
   categorySummary, evidenceUrl, technicalReviewLabels, reviewCompanyIds, type ResearchEvidence, type ResearchReview, type ResearchSnapshot,
 } from "@/lib/ai-research";
 
 const pageSize = 12;
 const evidencePageSize = 30;
-const revenueLabels = { self: "公司披露，待核口径", estimate: "仅第三方估算", platform: "仅支付平台记录", unknown: "暂无可用收入数字" };
+const revenueLabels = { self: "企业披露，待核口径", estimate: "仅第三方估算", platform: "仅支付平台记录", unknown: "暂无可用收入数字" };
 const useLabels: Record<string, string> = {
-  platform: "支付平台记录", signal: "公司披露信号", estimate: "第三方估算", historical: "历史里程碑",
+  platform: "支付平台记录", signal: "企业披露信号", estimate: "第三方估算", historical: "历史里程碑",
   "historical-estimate": "其他时点估算", excluded: "排除收入比较", scale: "团队规模", adoption: "采用规模",
   funding: "融资", technical: "第三方技术评测", crosscheck: "同一里程碑的其他资料",
 };
@@ -55,7 +58,7 @@ export default function AICompanyResearch() {
     : router.isReady && typeof linkedCompany === "string" && data?.companies.some((c) => c.id === linkedCompany)
       ? linkedCompany : null;
   function setSelected(id: string | null) { setSelection({ route: router.asPath, id }); }
-  const [tab, setTab] = useState<"companies" | "revenue" | "sources" | "reviews">("companies");
+  const [tab, setTab] = useState<"companies" | "candidates" | "revenue" | "models" | "sources" | "reviews">("companies");
   const companySection = useRef<HTMLElement>(null);
   const detailSection = useRef<HTMLElement>(null);
   useEffect(() => {
@@ -127,7 +130,7 @@ export default function AICompanyResearch() {
     <section ref={companySection} className="research-section" aria-labelledby="research-records-title">
       <h2 id="research-records-title">研究数据</h2>
       <div className="research-tabs" role="group" aria-label="研究数据视图">
-        {([['companies','公司与产品'],['revenue','收入观测'],['sources','原始证据'],['reviews','核查记录']] as const).map(([id,label]) =>
+        {([['companies','公司与产品'],['candidates','候选与缺项'],['revenue','收入观测'],['models','商业模式初评'],['sources','原始证据'],['reviews','核查记录']] as const).map(([id,label]) =>
           <button key={id} aria-pressed={tab === id} onClick={() => setTab(id)}>{label}</button>)}
       </div>
       {tab === "companies" && <>
@@ -138,7 +141,7 @@ export default function AICompanyResearch() {
           <label>资料深度<select value={depth} onChange={(e) => { setDepth(e.target.value); setPage(0); }}><option value="">全部资料</option><option value="case">原有个案研究</option><option value="profile">目录基础资料</option></select></label>
           <label>记录团队规模<select value={teamBand} onChange={(e) => { setTeamBand(e.target.value); setPage(0); }}><option value="">全部规模</option><option value="small">1—5 人</option><option value="medium">6—20 人</option><option value="large">21—50 人</option><option value="xl">至少 51 人</option><option value="unknown">未披露或分组不确定</option></select></label>
           <label>技术验证证据<select value={technicalLevel} onChange={(e) => { setTechnicalLevel(e.target.value); setPage(0); }}><option value="">全部验证层级</option>{Object.entries(technicalReviewLabels).map(([value, label]) => <option key={value} value={value}>{label}</option>)}</select></label>
-          <label>收入资料<select value={income} onChange={(e) => { setIncome(e.target.value); setPage(0); }}><option value="">全部状态</option><option value="self">找到公司披露</option><option value="estimate">仅第三方估算</option><option value="platform">仅支付平台记录</option><option value="unknown">暂无可用数字</option></select></label>
+          <label>收入资料<select value={income} onChange={(e) => { setIncome(e.target.value); setPage(0); }}><option value="">全部状态</option><option value="self">找到企业披露</option><option value="estimate">仅第三方估算</option><option value="platform">仅支付平台记录</option><option value="unknown">暂无可用数字</option></select></label>
         </div>
         <p className="research-muted">技术验证层级记录我们查到了什么，不是能力强弱评分。技术专项评审与业务分类复核分别记录。</p>
         <div className="research-result-count" role="status">{filtered.length} 条匹配记录<button onClick={() => { setCategory(""); setQuery(""); setCohort(""); setIncome(""); setDepth(""); setTeamBand(""); setTechnicalLevel(""); setPage(0); }}>清除筛选</button></div>
@@ -152,16 +155,19 @@ export default function AICompanyResearch() {
         {!filtered.length && <p className="research-message">没有匹配公司，请调整筛选条件。</p>}
         {filtered.length > pageSize && <nav className="research-pagination" aria-label="公司数据分页"><button disabled={page === 0} onClick={() => setPage((n) => n - 1)}>上一页</button><span>{page + 1} / {Math.ceil(filtered.length / pageSize)}</span><button disabled={(page + 1) * pageSize >= filtered.length} onClick={() => setPage((n) => n + 1)}>下一页</button></nav>}
       </>}
+      {tab === "models" && <BusinessModelAssessment data={data} onSelect={setSelected} onChooseCategory={chooseCategory} />}
+      {tab === "candidates" && <ResearchCandidates data={data} onSelect={setSelected} onChooseCategory={chooseCategory} />}
       {tab === "revenue" && <>
         <IncomeDistribution data={data} onSelect={setSelected} />
-        <h3>公司披露与估算的历史里程碑</h3>
-        <p className="research-notice">这一组包含 {disclosedCompanies} 家公司的披露或估算信号，多为异时点历史里程碑，ARR 与年化收入口径仍待核对，暂不计算其同期领域均值。币种未确认的记录仅显示原值，不绘制美元长度条。上面的平台MRR统计与这一组分开。</p>
+        <ArrDisclosures data={data} onSelect={setSelected} />
+        <h3>企业披露与估算的历史里程碑</h3>
+        <p className="research-notice">这一组包含 {disclosedCompanies} 家公司的披露或估算信号；企业披露含公司或交易当事方公开材料，披露主体与审计状态见证据。多为异时点历史里程碑，期间收入不直接年化，ARR 与年化收入口径仍待核对，暂不计算其同期领域均值。币种未确认的记录仅显示原值，不绘制美元长度条。上面的平台MRR统计与这一组分开。</p>
         <div className="research-revenue-chart" aria-label="不同时间的公开收入里程碑，线性长度，非同行排名">{observed.map((o) => <div key={o.id}>
           <div><button onClick={() => setSelected(o.company)}>{o.name}</button><strong>{o.bound !== "=" && o.bound}{money(o.value, o.unit)}</strong></div>
           {o.unit === "USD" && <span className="research-bar" data-estimate={o.selection === "estimate" || undefined} aria-hidden="true"><i style={{width: `${o.value / Math.max(1, ...observed.filter((v) => v.unit === "USD").map((v) => v.value)) * 100}%`}} /></span>}
           <small>{o.kind} · {useLabels[o.selection]} · {o.timing}</small>
         </div>)}</div>
-        <div className="research-table-wrap"><table><caption>当前研究数据中的收入可得性，目录项未逐一检索；未建立同期、同龄、同口径比较组</caption><thead><tr><th>业务分类</th><th>公司披露数</th><th>仅估算数</th><th>仅平台数</th><th>暂无可用数字</th><th>同期均值 / 离散度</th></tr></thead><tbody>{summaries.map((c) => <tr key={c.id}><td><button onClick={() => chooseCategory(c.id)}>{c.name}</button></td><td>{c.self}</td><td>{c.estimate}</td><td>{c.platform}</td><td>{c.unknown}</td><td>条件不足</td></tr>)}</tbody></table></div>
+        <div className="research-table-wrap"><table><caption>当前研究数据中的收入可得性，目录项未逐一检索；未建立同期、同龄、同口径比较组</caption><thead><tr><th>业务分类</th><th>企业披露数</th><th>仅估算数</th><th>仅平台数</th><th>暂无可用数字</th><th>同期均值 / 离散度</th></tr></thead><tbody>{summaries.map((c) => <tr key={c.id}><td><button onClick={() => chooseCategory(c.id)}>{c.name}</button></td><td>{c.self}</td><td>{c.estimate}</td><td>{c.platform}</td><td>{c.unknown}</td><td>条件不足</td></tr>)}</tbody></table></div>
         <p className="research-muted">离散度用于筛选值得核查的异常，还需排除公司年龄、口径和披露偏差；不能据此认定财务造假。</p>
       </>}
       {tab === "sources" && <><p className="research-muted">来源链接保留原始位置。相同 URL 的记录可能属于同一披露，不能按记录数计算独立证据数量。</p><ul className="research-evidence-list">{data.evidence.slice(sourcePage * evidencePageSize, (sourcePage + 1) * evidencePageSize).map((e) => <Evidence key={e.id} item={e} />)}</ul>{data.evidence.length > evidencePageSize && <nav className="research-pagination" aria-label="原始证据分页"><button disabled={sourcePage === 0} onClick={() => setSourcePage((n) => n - 1)}>上一页</button><span>{sourcePage + 1} / {Math.ceil(data.evidence.length / evidencePageSize)}</span><button disabled={(sourcePage + 1) * evidencePageSize >= data.evidence.length} onClick={() => setSourcePage((n) => n + 1)}>下一页</button></nav>}</>}

apps/docs/src/components/ArrDisclosures.tsx

@@ -0,0 +1,69 @@
+import { useState } from "react";
+import { evidenceUrl, type ResearchSnapshot } from "@/lib/ai-research";
+import { arrDisclosureCoverage, arrDisclosures, arrFieldGaps, type ArrFieldGap } from "@/lib/research-statistics";
+
+const labels: Record<string, string> = {
+  signal: "企业或当事方披露", estimate: "第三方估算",
+  historical: "历史披露", "historical-estimate": "历史估算",
+};
+const gapLabels: Record<ArrFieldGap, string> = {
+  date: "缺有效财务截至日", currency: "USD 未确认（含其他币种）",
+  amount: "下界、近似或非精确金额", estimate: "第三方估算",
+};
+
+export default function ArrDisclosures({ data, onSelect }: { data: ResearchSnapshot; onSelect: (id: string) => void }) {
+  const [category, setCategory] = useState("");
+  const [year, setYear] = useState("");
+  const [gap, setGap] = useState<ArrFieldGap | "">("");
+  const claims = arrDisclosures(data, { category, foundedYear: year ? Number(year) : undefined });
+  const rows = claims.filter(({ observation }) => !gap || arrFieldGaps(observation, data.asOf).includes(gap));
+  const gapCounts = (Object.keys(gapLabels) as ArrFieldGap[]).map((id) => ({
+    id, count: claims.filter(({ observation }) => arrFieldGaps(observation, data.asOf).includes(id)).length,
+  }));
+  const completeFields = claims.filter(({ observation }) => !arrFieldGaps(observation, data.asOf).length).length;
+  const coverage = arrDisclosureCoverage(data, year ? Number(year) : undefined);
+  const companies = new Set(claims.map((row) => row.company.id));
+  const categories = new Map(data.categories.map((item) => [item.id, item.name]));
+  const evidence = new Map(data.evidence.map((item) => [item.id, item]));
+  return <section aria-label="ARR披露分类表">
+    <h3>2025—2026 年成立公司的 ARR 披露</h3>
+    <p className="research-notice">这是跨时点证据表，共 {companies.size} 家公司、{claims.length} 条记录；同一公司的历史里程碑逐条保留，不能相加或视为独立公司。ARR 标签沿用原始披露，未证明经常性收入定义或审计状态；不纳入 MRR 图表，不计算同期行业均值或造假概率。仅收录明确标注的 ARR；定义冲突、目标、承诺、客户和前雇主收入排除，缺失不作零。</p>
+    <div className="research-filters">
+      <label>业务分类<select value={category} onChange={(event) => setCategory(event.target.value)}><option value="">全部分类</option>{data.categories.map((item) => <option key={item.id} value={item.id}>{item.name}</option>)}</select></label>
+      <label>成立年份<select value={year} onChange={(event) => setYear(event.target.value)}><option value="">2025 与 2026</option><option value="2025">2025</option><option value="2026">2026</option></select></label>
+      <label>补证项目<select value={gap} onChange={(event) => setGap(event.target.value as ArrFieldGap | "")}><option value="">全部记录</option>{gapCounts.map((item) => <option key={item.id} value={item.id}>{gapLabels[item.id]}（{item.count} 条）</option>)}</select></label>
+    </div>
+    <div className="research-table-wrap"><table>
+      <caption>ARR 披露覆盖：按公司当前分类及所选成立年份、每家公司计一次。点击分类筛选下方证据表。</caption>
+      <thead><tr><th>业务分类</th><th>已采集公司</th><th>企业或当事方披露</th><th>仅第三方估算</th><th>缺少 ARR</th><th>披露覆盖率</th></tr></thead>
+      <tbody>{coverage.filter((item) => item.companies > 0).map((item) => <tr key={item.id}>
+        <td><button onClick={() => setCategory(item.id)} aria-pressed={category === item.id}>{item.name}</button></td>
+        <td>{item.companies}</td><td>{item.reported}</td><td>{item.estimatedOnly}</td><td>{item.missing}</td>
+        <td>{new Intl.NumberFormat("zh-CN", { style: "percent", maximumFractionDigits: 1 }).format(item.disclosed / item.companies)}</td>
+      </tr>)}</tbody>
+    </table></div>
+    <p className="research-muted">分母含仅有目录资料的公司，覆盖率反映本次采集深度，不代表行业披露率。分类及收费模式采用公司当前资料；公司转向或同时经营多个产品时，历史 ARR 未必属于当前业务，需打开公司核对产品、主体与期间，不能据当前分类重归历史收入。企业披露含历史里程碑，同一公司兼有披露和估算时归企业披露；缺少 ARR 的公司仍可能有其他收入。披露时点、币种与合同定义未对齐，不能将已披露少数公司的均值外推至整个分类，也不能由离散性判断财务造假。</p>
+    <aside className="research-notice" aria-label="ARR比较前的补证检查">
+      <strong>ARR 比较前的补证检查</strong>
+      <p>当前分类及成立年下有 {claims.length} 条披露，{completeFields} 条同时具有有效日期、明确 USD、精确金额且不是第三方估算。补证筛选后显示 {rows.length} 条。</p>
+      <div className="income-options">{gapCounts.map((item) => <button key={item.id} aria-pressed={gap === item.id} onClick={() => setGap(gap === item.id ? "" : item.id)}>{gapLabels[item.id]}：{item.count}</button>)}</div>
+      <p>各项可重叠，不能相加为独立公司数。日期必须是经营指标截至日，网页访问、发布或更新时间不能代替；其他币种保留原值，不擅自换算。基础字段齐全也不证明可比，还需对齐收入定义、业务期间、收费模式、主体与独立验证；这些缺项不作为造假判定。</p>
+    </aside>
+    <div className="research-table-wrap"><table>
+      <caption>金额保留原币种、下界和估算身份；日期栏保留业务截至日缺项</caption>
+      <thead><tr><th>公司 / 当前分类</th><th>当前收费与交付模式</th><th>原始 ARR 声明</th><th>时点</th><th>身份 / 限制</th><th>证据</th></tr></thead>
+      <tbody>{rows.map(({ company, observation: item }) => {
+        const source = evidence.get(item.evidence);
+        const url = source && evidenceUrl(source.url);
+        return <tr key={item.id}>
+          <td><button onClick={() => onSelect(company.id)}>{company.name}</button><small>{categories.get(company.cat)} · {company.year}</small></td>
+          <td>{company.businessModel ?? company.price}</td>
+          <td>{item.bound !== "=" && item.bound}{new Intl.NumberFormat("zh-CN", { maximumFractionDigits: 2 }).format(item.value)} {item.unit}<small>{item.kind}</small></td>
+          <td>{item.timing}</td><td>{labels[item.selection]}<small>{item.reason}</small><small>{arrFieldGaps(item, data.asOf).map((id) => gapLabels[id]).join("；") || "基础字段齐全；定义、期间与主体仍须核实"}</small></td>
+          <td>{url ? <a href={url} target="_blank" rel="noopener noreferrer">{item.evidence} ↗</a> : item.evidence}</td>
+        </tr>;
+      })}</tbody>
+    </table></div>
+    {!rows.length && <p>当前分类、成立年与补证筛选没有符合条件的已记录 ARR 声明；不代表这些公司收入为零。</p>}
+  </section>;
+}

apps/docs/src/components/BusinessModelAssessment.tsx

@@ -0,0 +1,32 @@
+import { businessModelReviewType, evidenceUrl, reviewCompanyIds, type ResearchSnapshot } from "@/lib/ai-research";
+
+export default function BusinessModelAssessment({ data, onSelect, onChooseCategory }: {
+  data: ResearchSnapshot; onSelect: (id: string) => void; onChooseCategory: (id: string) => void;
+}) {
+  const reviews = data.reviews.filter((review) => review.type === businessModelReviewType && review.scope === "companies");
+  const evidence = new Map(data.evidence.map((item) => [item.id, item]));
+  return <section aria-labelledby="research-models-title">
+    <h3 id="research-models-title">商业模式初评</h3>
+    <p>从已核个案提炼收费方式、成本约束与下一步验证条件。已知事实与研究判断分列；同一种收费方式可以跨业务分类，以下个案不能代表整个行业。</p>
+    <p className="research-muted">这是基于当前证据的初步评估，不是收入预测或盈利排名。判断能否用于我们的业务，还需用实际客户任务、付费留存与交付成本验证；ARR 或平台 MRR 单独不能证明利润。</p>
+    {reviews.length ? <div className="research-table-wrap"><table>
+      <caption>收费模式与业务分类、公司明细及原始证据相互关联</caption>
+      <thead><tr><th scope="col">模式 / 业务分类</th><th scope="col">已核事实</th><th scope="col">初步判断 / 验证条件</th><th scope="col">参考个案与证据</th></tr></thead>
+      <tbody>{reviews.map((review) => {
+        const companyIds = new Set(reviewCompanyIds(data, review));
+        const companies = data.companies.filter((company) => companyIds.has(company.id));
+        const categories = data.categories.filter((category) => companies.some((company) => company.cat === category.id));
+        return <tr key={review.id}>
+          <td><strong>{review.company}</strong><div>{categories.map((category) => <button key={category.id} onClick={() => onChooseCategory(category.id)}>{category.name}</button>)}</div></td>
+          <td>{review.summary}</td><td>{review.handling}</td>
+          <td><div>{companies.map((company) => <button key={company.id} className="research-company-name" onClick={() => onSelect(company.id)}>{company.name}</button>)}</div>
+            <div>{review.evidenceIds.map((id) => {
+              const item = evidence.get(id); const url = item && evidenceUrl(item.url);
+              return item && url ? <p key={id}><a href={url} target="_blank" rel="noopener noreferrer">{item.title} ↗</a></p> : null;
+            })}</div>
+          </td>
+        </tr>;
+      })}</tbody>
+    </table></div> : <p>当前数据版本尚未记录商业模式初评。</p>}
+  </section>;
+}

apps/docs/src/components/IncomeDistribution.tsx

@@ -1,6 +1,6 @@
 import { useMemo, useState } from "react";
 import type { ResearchSnapshot } from "@/lib/ai-research";
-import { groupIncome, platformIncomeSample } from "@/lib/research-statistics";
+import { compareIncomeGroups, groupIncome, platformIncomeSample } from "@/lib/research-statistics";
 
 const usd = (value: number | null) => value === null ? "—" : new Intl.NumberFormat("zh-CN", {
   style: "currency", currency: "USD", maximumFractionDigits: 0,
@@ -23,6 +23,7 @@ export default function IncomeDistribution({ data, onSelect }: {
   const categoryGroups = [...new Set(groups.map((group) => group.category))].map((category) => ({
     category, rows: groups.filter((group) => group.category === category),
   }));
+  const comparisons = compareIncomeGroups(groups);
   const categories = new Map(data.categories.map((category) => [category.id, category.name]));
   const metric = annualize ? "MRR×12参考年化" : "平台MRR";
   const values = sample.points.map((point) => point.value);
@@ -47,6 +48,20 @@ export default function IncomeDistribution({ data, onSelect }: {
     <p>{splitModels ? "分组：业务类别 × 支付平台 × 收费与交付模式。同一类别下的多行是不同统计组，各自计算均值和中位数。" : "分组：业务类别 × 支付平台。同一平台内的收费模式已合并，不同支付平台仍分别统计。"}</p>
     <p className="research-notice">资料截至 {data.asOf}，纳入 {sample.points.length} 个产品，另有 {sample.excluded.length} 个 MRR 观测未纳入。{annualize ? "乘12只改变显示单位，不证明全年收入、续约或ARR；CV与相对敏感性不变。" : "MRR不等于近30日收款；平台数据连接也不等于财务审计。"}{dateWindow === "all" && " 当前包含不同时间的历史观测，不能称为同期对照。"}</p>
     {sample.points.length ? <>
+      <aside aria-label="当前窗口样本解读" className="research-notice">
+        <strong>当前窗口的样本解读</strong>
+        {comparisons.length ? comparisons.map((comparison) => <div key={comparison.key}>
+          <p>{comparison.provider} · {comparison.model}（仅比较各组 n≥3）：
+            {comparison.leastDispersed.stats.cv === comparison.mostDispersed.stats.cv
+              ? <>各组 CV 相同（{comparison.leastDispersed.stats.cv?.toFixed(3)}），没有离散度高低差异。</>
+              : <>{categories.get(comparison.mostDispersed.category)}更离散（CV {comparison.mostDispersed.stats.cv?.toFixed(3)}），{categories.get(comparison.leastDispersed.category)}较集中（CV {comparison.leastDispersed.stats.cv?.toFixed(3)}）。</>}
+            {comparison.leastSensitive.stats.largestRemovalDrop === comparison.mostSensitive.stats.largestRemovalDrop
+              ? <>各组移除最大值后的均值下降幅度相同（{percent(comparison.leastSensitive.stats.largestRemovalDrop)}）。</>
+              : <>{categories.get(comparison.mostSensitive.category)}的均值更受最大值影响，移除后下降 {percent(comparison.mostSensitive.stats.largestRemovalDrop)}；{categories.get(comparison.leastSensitive.category)}下降 {percent(comparison.leastSensitive.stats.largestRemovalDrop)}。</>}
+          </p>
+        </div>) : <p>当前没有至少两个可对照的统计组。需要相同支付平台、明确的收费模式标签，且各组至少 3 个产品；可调整同步窗口或创立年，不能将单个产品或两点均值当作领域典型。</p>}
+        <p>这些是所选产品样本内的描述，较集中或较不敏感也不证明行业均值可靠。最后同步日并非统一财务结账日，产品年龄、主体独立性、成本和留存仍待核；参考年化也不是已披露 ARR。</p>
+      </aside>
       <figure>
         <div className="research-table-wrap"><svg className="income-distribution-chart" viewBox={`0 0 900 ${groups.length * 70 + 65}`} role="group" aria-label={`${metric}分布，${logScale ? "对数" : "线性"}横轴；圆点为产品，竖线为均值，菱形为中位数`}>
           {ticks.map((tick) => <g key={tick}><line x1={x(tick)} x2={x(tick)} y1={15} y2={groups.length * 70 + 10} stroke="#dfe6e0" /><text x={x(tick)} y={groups.length * 70 + 35} textAnchor="middle" fontSize="11" fill="#637066">{usd(tick)}</text></g>)}
@@ -75,7 +90,7 @@ export default function IncomeDistribution({ data, onSelect }: {
         </svg></div>
         <figcaption>圆点：产品观测　竖线：样本均值　菱形：样本中位数。业务类别标题下各行按支付平台{splitModels && "及收费与交付模式"}分别统计；n为该组产品数。点击点或下表产品名可看原始证据。</figcaption>
       </figure>
-      <div className="research-table-wrap"><table><caption>{metric}样本统计；每行是类别下的一个统计组，CV使用样本标准差</caption><thead><tr><th scope="col">业务类别</th><th scope="col">支付平台 / 收费模式</th><th scope="col">n</th><th scope="col">均值</th><th scope="col">中位数</th><th scope="col">均值/中位数</th><th scope="col">CV</th><th scope="col">去除最大值后的均值 / 下降幅度</th><th scope="col">产品与限制</th></tr></thead>{categoryGroups.map(({ category, rows }) => <tbody key={category}>{rows.map((group, index) => <tr key={group.key}>
+      <div className="research-table-wrap"><table className="income-statistics"><caption>{metric}样本统计；每行是类别下的一个统计组，CV使用样本标准差</caption><thead><tr><th scope="col">业务类别</th><th scope="col">支付平台 / 收费模式</th><th scope="col">n</th><th scope="col">均值</th><th scope="col">中位数</th><th scope="col">均值/中位数</th><th scope="col">CV</th><th scope="col">去除最大值后的均值 / 下降幅度</th><th scope="col">产品与限制</th></tr></thead>{categoryGroups.map(({ category, rows }) => <tbody key={category}>{rows.map((group, index) => <tr key={group.key}>
         {index === 0 && <th scope="rowgroup" rowSpan={rows.length}>{categories.get(category)}<small>{rows.length}个统计组</small></th>}
         <td>{group.provider}<small>{group.model}</small></td><td>{group.stats.n}</td><td>{usd(group.stats.mean)}</td><td>{usd(group.stats.median)}</td><td>{group.stats.meanMedianRatio?.toFixed(2) ?? "—"}</td><td>{group.stats.cv?.toFixed(2) ?? "—"}</td><td>{usd(group.stats.meanWithoutLargest)}<small>{percent(group.stats.largestRemovalDrop)}</small></td>
         <td>{group.points.map((point) => <span key={point.company.id}><button className="research-company-name" onClick={() => onSelect(point.company.id)}>{point.company.name}</button>{" "}</span>)}<small>{group.stats.n < 3 ? "样本少于3，不能据此判断领域典型收入。" : "自选的小样本，须结合产品年龄、成本与留存评估。"}</small></td>

apps/docs/src/components/ResearchCandidates.tsx

@@ -0,0 +1,75 @@
+import { useMemo, useState } from "react";
+import { evidenceUrl, type ResearchCandidateBatch, type ResearchSnapshot } from "@/lib/ai-research";
+
+type CandidateStatus = ResearchCandidateBatch["records"][number]["status"];
+const statusLabels: Record<CandidateStatus, string> = {
+  "pending-year": "成立年或主体待核", "pending-ai": "AI 主营待核", linked: "已关联公司",
+  "existing-outside-cohort": "已关联范围外对照", "outside-cohort": "范围外候选", promotion: "推广活动",
+};
+const pageSize = 20;
+const sourceNames: Record<string, string> = {
+  S01: "Y Combinator", S02: "TechCrunch AI", S04: "Product Hunt", S05: "Sacra",
+  S10: "机器之心", S11: "36氪", S13: "TrustMRR", S14: "a16z Speedrun",
+};
+const sourceLabel = (id: string) => `${id} · ${sourceNames[id] ?? "其他来源"}`;
+
+function SourceLink({ url, label }: { url: string | null; label: string }) {
+  const safe = url && evidenceUrl(url);
+  return safe ? <a href={safe} target="_blank" rel="noopener noreferrer">{label} ↗</a> : <span>{label}（链接未取得）</span>;
+}
+
+export default function ResearchCandidates({ data, onSelect, onChooseCategory }: {
+  data: ResearchSnapshot; onSelect: (id: string) => void; onChooseCategory: (id: string) => void;
+}) {
+  const [source, setSource] = useState("");
+  const [batchId, setBatchId] = useState("");
+  const [category, setCategory] = useState("");
+  const [status, setStatus] = useState<CandidateStatus | "pending" | "">("pending");
+  const [query, setQuery] = useState("");
+  const [page, setPage] = useState(0);
+  const all = useMemo(() => (data.candidateBatches ?? []).flatMap((batch) => batch.records.map((record) => ({ batch, record }))), [data.candidateBatches]);
+  const sources = [...new Set(all.map(({ batch }) => batch.sourceId))].sort();
+  const batches = (data.candidateBatches ?? []).filter((batch) => !source || batch.sourceId === source);
+  const matched = all.filter(({ batch, record }) => (!source || batch.sourceId === source)
+    && (!batchId || batch.id === batchId) && (!category || record.categoryId === category)
+    && `${record.name} ${record.id} ${record.note}`.toLocaleLowerCase().includes(query.trim().toLocaleLowerCase()));
+  const pending = (value: CandidateStatus) => value === "pending-year" || value === "pending-ai";
+  const rows = matched.filter(({ record }) => !status || (status === "pending" ? pending(record.status) : record.status === status));
+  const companies = new Map(data.companies.map((company) => [company.id, company]));
+  const evidence = new Map(data.evidence.map((item) => [item.id, item]));
+  const categories = new Map(data.categories.map((item) => [item.id, item.name]));
+  return <section aria-labelledby="research-candidates-title">
+    <h3 id="research-candidates-title">候选与缺项</h3>
+    <p>展示已登记的有限来源批次及后续核查。默认显示成立年、主体或 AI 主营待核的条目；待核不是确认排除，也不计入公司表。每行是一次发现位置，同一公司或产品可以重复出现。</p>
+    <p className="research-muted">候选初分类与公司表数量分开，来源批次不等全年或全站名单。原始发现范围及后续结论见每行说明；暂无收入资料不作零，推广活动也不算公司。</p>
+    <div className="research-filters">
+      <label className="research-query">搜索候选<input type="search" value={query} placeholder="名称、编号或核查说明" onChange={(event) => { setQuery(event.target.value); setPage(0); }} /></label>
+      <label>发现来源<select value={source} onChange={(event) => { setSource(event.target.value); setBatchId(""); setPage(0); }}><option value="">全部已登记来源</option>{sources.map((id) => <option key={id} value={id}>{sourceLabel(id)}</option>)}</select></label>
+      <label>发现批次<select value={batchId} onChange={(event) => { setBatchId(event.target.value); setPage(0); }}><option value="">全部批次</option>{batches.map((batch) => <option key={batch.id} value={batch.id}>{batch.id}</option>)}</select></label>
+      <label>候选初分类<select value={category} onChange={(event) => { setCategory(event.target.value); setPage(0); }}><option value="">全部分类</option>{data.categories.map((item) => <option key={item.id} value={item.id}>{item.name}</option>)}</select></label>
+      <label>核查状态<select value={status} onChange={(event) => { setStatus(event.target.value as typeof status); setPage(0); }}>
+        <option value="">全部状态（{matched.length}）</option><option value="pending">全部待核（{matched.filter(({ record }) => pending(record.status)).length}）</option>
+        {Object.entries(statusLabels).map(([id, label]) => <option key={id} value={id}>{label}（{matched.filter(({ record }) => record.status === id).length}）</option>)}
+      </select></label>
+    </div>
+    <p role="status">{rows.length} 个发现位置匹配；不是独立公司数。</p>
+    <div className="research-table-wrap"><table>
+      <caption>有限来源候选与处理记录；选择关联公司查看主数据，原始证据直接链接来源</caption>
+      <thead><tr><th scope="col">候选</th><th scope="col">初分类 / 状态</th><th scope="col">来源及范围</th><th scope="col">核查结论 / 缺项</th><th scope="col">关联公司 / 证据</th></tr></thead>
+      <tbody>{rows.slice(page * pageSize, (page + 1) * pageSize).map(({ batch, record }) => {
+        const company = record.companyId && companies.get(record.companyId);
+        return <tr key={record.id}>
+          <td><SourceLink url={record.url} label={record.name} /><small>{record.id}</small></td>
+          <td><button onClick={() => onChooseCategory(record.categoryId)} aria-label={`查看${categories.get(record.categoryId)}分类内的公司`}>{categories.get(record.categoryId)}</button><small>{statusLabels[record.status]}</small></td>
+          <td><SourceLink url={batch.url} label={sourceLabel(batch.sourceId)} /><small>{batch.id} · 读取 {batch.accessedAt}</small><details><summary>发现批次范围</summary><p>{batch.scope}</p><p>{batch.complete ? "该批次标记已完成；不代表来源全站覆盖。" : "该批次仍保留覆盖缺项。"}</p></details></td>
+          <td>{record.note}</td>
+          <td>{company ? <button className="research-company-name" onClick={() => onSelect(company.id)}>{company.name}</button> : <small>尚未关联公司主记录</small>}
+            {record.evidenceIds?.map((id) => { const item = evidence.get(id); return item ? <p key={id}><SourceLink url={item.url} label={id} /></p> : null; })}
+          </td>
+        </tr>;
+      })}</tbody>
+    </table></div>
+    {!rows.length && <p>当前筛选没有已登记的候选记录；不代表该来源或分类没有公司。</p>}
+    {rows.length > pageSize && <nav className="research-pagination" aria-label="候选与缺项分页"><button disabled={page === 0} onClick={() => setPage((value) => value - 1)}>上一页</button><span>{page + 1} / {Math.ceil(rows.length / pageSize)}</span><button disabled={(page + 1) * pageSize >= rows.length} onClick={() => setPage((value) => value + 1)}>下一页</button></nav>}
+  </section>;
+}

apps/docs/src/lib/ai-research.ts

@@ -1,4 +1,5 @@
 export type ResearchCategory = { id: string; name: string; description: string };
+export const businessModelReviewType = "商业模式初评";
 export const technicalReviewLabels = {
   unreviewed: "技术证据尚未专项评审",
   description: "已读技术自述",
@@ -48,7 +49,7 @@ export type ResearchCandidateBatch = {
   records: {
     id: string; name: string; url: string | null; categoryId: string;
     kind: "product" | "company" | "promotion"; profileStatus: "readable" | "unavailable" | "archive-only";
-    status: "pending-year" | "linked" | "existing-outside-cohort" | "outside-cohort" | "promotion";
+    status: "pending-year" | "pending-ai" | "linked" | "existing-outside-cohort" | "outside-cohort" | "promotion";
     companyId?: string; evidenceIds?: string[]; note: string;
   }[];
 };
@@ -125,13 +126,13 @@ export function validateResearch(snapshot: ResearchSnapshot) {
       if (!row.name.trim() || !row.note.trim() || !categories.has(row.categoryId) ||
           !["product", "company", "promotion"].includes(row.kind) ||
           !["readable", "unavailable", "archive-only"].includes(row.profileStatus) ||
-          !["pending-year", "linked", "existing-outside-cohort", "outside-cohort", "promotion"].includes(row.status) ||
+          !["pending-year", "pending-ai", "linked", "existing-outside-cohort", "outside-cohort", "promotion"].includes(row.status) ||
           (row.kind !== "promotion" && (!row.url || !evidenceUrl(row.url))) ||
           (row.url !== null && !evidenceUrl(row.url)) ||
           linked !== !!row.companyId || (row.companyId && !companies.has(row.companyId)) ||
           (row.kind === "promotion") !== (row.status === "promotion") ||
           (row.evidenceIds ?? []).some((id) => !evidence.has(id)) ||
-          (row.status === "outside-cohort" && !row.evidenceIds?.length))
+          (["outside-cohort", "pending-ai"].includes(row.status) && !row.evidenceIds?.length))
         throw new Error(`Invalid research candidate: ${row.id}`);
     }
   }

apps/docs/src/lib/docs.ts

@@ -1,5 +1,9 @@
 import { randomUUID, createHash } from "node:crypto";
 import { openSiteDatabase } from "./site-database";
+import { DocumentValidationError, DocumentConflictError } from "./document-errors";
+import { sectionSchema, loadSections, reconcileSections, persistSections, splitSections, outline,
+  applySectionOperations, parseSectionEdit, type DocumentSection } from "./document-sections";
+export { DocumentValidationError, DocumentConflictError } from "./document-errors";
 
 export type DocLink = {
   slug: string; sourceSlug: string; category: string; title: string; searchText?: string;
@@ -9,8 +13,6 @@ export type DocLink = {
 export type Doc = DocLink & { content: string };
 export type DocInput = Omit<Doc, "revision" | "searchText">;
 export type DocumentExport = { format: "tabularium-documents-v1"; documents: Doc[] };
-export class DocumentValidationError extends Error {}
-export class DocumentConflictError extends Error {}
 export type DocumentReceipt = Pick<Doc, "slug" | "sourceSlug" | "revision">;
 export const validSlug = /^[a-z0-9]+(?:-[a-z0-9]+)*(?:\/[a-z0-9]+(?:-[a-z0-9]+)*)*$/;
 
@@ -36,6 +38,7 @@ function database() {
       digest TEXT PRIMARY KEY, document_count INTEGER NOT NULL, created_at TEXT NOT NULL
     );
   `);
+  sectionSchema(db);
   return db;
 }
 
@@ -57,12 +60,13 @@ export function validateDocument(input: DocInput): void {
     throw new DocumentValidationError("Use compressed WebP images instead of original rasters");
 }
 
-function write(db: ReturnType<typeof database>, input: DocInput, expectedRevision: number): Doc {
+function write(db: ReturnType<typeof database>, input: DocInput, expectedRevision: number, editedSections?: DocumentSection[]): Doc {
   validateDocument(input);
-  const existing = db.prepare("SELECT id, revision FROM documents WHERE source_slug=?")
-    .get(input.sourceSlug) as { id: string; revision: number } | undefined;
+  const existing = db.prepare("SELECT id, revision, payload FROM documents WHERE source_slug=?")
+    .get(input.sourceSlug) as { id: string; revision: number; payload: string } | undefined;
   if ((existing?.revision ?? 0) !== expectedRevision) throw new DocumentConflictError("Document revision conflict");
   const id = existing?.id ?? randomUUID();
+  const previous = existing ? loadSections(db, id, (JSON.parse(existing.payload) as Doc).content) : [];
   const doc: Doc = { slug: input.slug, sourceSlug: input.sourceSlug, category: input.category,
     title: input.title, group: input.group, collection: input.collection, description: input.description,
     draft: input.draft, featured: input.featured, content: input.content, revision: expectedRevision + 1 };
@@ -80,9 +84,75 @@ function write(db: ReturnType<typeof database>, input: DocInput, expectedRevisio
     db.prepare("INSERT OR IGNORE INTO document_aliases VALUES (?,?)").run(alias, id);
   db.prepare("INSERT INTO document_revisions VALUES (?,?,?,?)")
     .run(id, doc.revision, payload, new Date().toISOString());
+  persistSections(db, id, doc.revision, editedSections ?? reconcileSections(doc.content, previous));
   return doc;
 }
 
+// Internal summaries never enter Doc, catalog, search or browser page props.
+export function readDocumentSections(slug: string, revision?: number) {
+  if (!validSlug.test(slug)) throw new DocumentValidationError("Invalid document slug");
+  if (revision !== undefined && (!Number.isSafeInteger(revision) || revision < 1)) throw new DocumentValidationError("Invalid revision");
+  const db = database();
+  try {
+    db.exec("BEGIN");
+    const row = db.prepare(`SELECT d.id,d.payload FROM documents d JOIN document_aliases a
+      ON a.document_id=d.id WHERE a.alias=?`).get(slug) as { id: string; payload: string } | undefined;
+    if (!row) { db.exec("COMMIT"); return null; }
+    let doc = JSON.parse(row.payload) as Doc;
+    let sections: DocumentSection[];
+    if (revision === undefined || revision === doc.revision) sections = loadSections(db, row.id, doc.content);
+    else {
+      const prior = db.prepare("SELECT payload FROM document_revisions WHERE document_id=? AND revision=?")
+        .get(row.id, revision) as { payload: string } | undefined;
+      if (!prior) { db.exec("COMMIT"); return null; }
+      doc = JSON.parse(prior.payload) as Doc;
+      const snapshot = db.prepare("SELECT payload FROM document_section_revisions WHERE document_id=? AND revision=?")
+        .get(row.id, revision) as { payload: string } | undefined;
+      sections = snapshot ? JSON.parse(snapshot.payload) as DocumentSection[] : splitSections(doc.content);
+    }
+    db.exec("COMMIT");
+    return { slug: doc.slug, sourceSlug: doc.sourceSlug, title: doc.title, revision: doc.revision, sections };
+  } finally { db.close(); }
+}
+
+export function editDocumentSections(slug: string, value: unknown, request?: { namespace: string; id: string }) {
+  if (!validSlug.test(slug)) throw new DocumentValidationError("Invalid document slug");
+  const edit = parseSectionEdit(value);
+  if (request && (!request.namespace || !/^[A-Za-z0-9._:-]{1,128}$/.test(request.id))) throw new DocumentValidationError("Invalid Idempotency-Key");
+  const digest = createHash("sha256").update(JSON.stringify({ slug, edit })).digest("hex");
+  const namespace = request ? request.namespace + ":sections" : undefined;
+  const db = database();
+  try {
+    db.exec("BEGIN IMMEDIATE");
+    try {
+      if (request) {
+        const prior = db.prepare("SELECT digest,response FROM document_write_requests WHERE namespace=? AND request_id=?")
+          .get(namespace!, request.id) as { digest: string; response: string } | undefined;
+        if (prior) {
+          if (prior.digest !== digest) throw new DocumentConflictError("Idempotency-Key was used with different content");
+          db.exec("COMMIT");
+          return { ...JSON.parse(prior.response) as { slug: string; sourceSlug: string; revision: number; sections: ReturnType<typeof outline> }, replayed: true };
+        }
+      }
+      const row = db.prepare(`SELECT d.id,d.payload FROM documents d JOIN document_aliases a
+        ON a.document_id=d.id WHERE a.alias=?`).get(slug) as { id: string; payload: string } | undefined;
+      if (!row) { db.exec("COMMIT"); return null; }
+      const doc = JSON.parse(row.payload) as Doc;
+      if (doc.revision !== edit.revision) throw new DocumentConflictError("Document revision conflict");
+      const previous = loadSections(db, row.id, doc.content);
+      const sections = applySectionOperations(previous, edit.operations);
+      // Preserve the initial outline when upgrading an existing document for the first time.
+      db.prepare("INSERT OR IGNORE INTO document_section_revisions VALUES (?,?,?)").run(row.id, doc.revision, JSON.stringify(previous));
+      const saved = write(db, { ...doc, content: sections.map((s) => s.content).join("") }, doc.revision, sections);
+      const receipt = { slug: saved.slug, sourceSlug: saved.sourceSlug, revision: saved.revision, sections: outline(sections) };
+      if (request) db.prepare("INSERT INTO document_write_requests VALUES (?,?,?,?)")
+        .run(namespace!, request.id, digest, JSON.stringify(receipt));
+      db.exec("COMMIT");
+      return { ...receipt, replayed: false };
+    } catch (error) { db.exec("ROLLBACK"); throw error; }
+  } finally { db.close(); }
+}
+
 export function saveDocument(input: DocInput, expectedRevision: number): Doc {
   if (!Number.isSafeInteger(expectedRevision) || expectedRevision < 0) throw new DocumentValidationError("Provide expected revision");
   const db = database();

apps/docs/src/lib/document-errors.ts

@@ -0,0 +1,2 @@
+export class DocumentValidationError extends Error {}
+export class DocumentConflictError extends Error {}

apps/docs/src/lib/document-sections.ts

@@ -0,0 +1,144 @@
+import { createHash, randomUUID } from "node:crypto";
+import type { DatabaseSync } from "node:sqlite";
+import { DocumentValidationError } from "./document-errors";
+
+export type DocumentSection = {
+  id: string; title: string; content: string; summary: string;
+};
+export type SectionOperation =
+  | { op: "update"; id: string; content?: string; summary?: string }
+  | { op: "insert"; after: string; content: string; summary: string }
+  | { op: "delete"; id: string }
+  | { op: "move"; id: string; after: string };
+export type SectionEdit = { revision: number; operations: SectionOperation[] };
+const hash = (text: string) => createHash("sha256").update(text).digest("hex");
+
+// Preserve every byte, including CRLF and separators. H3-H6 belong to their H2 chapter.
+export function splitSections(content: string): DocumentSection[] {
+  const starts: { offset: number; title: string }[] = [{ offset: 0, title: "导言" }];
+  let offset = 0;
+  let fence: { marker: string; length: number } | undefined;
+  for (const line of content.match(/[^\n]*\n|[^\n]+$/g) ?? []) {
+    const opening = line.match(/^ {0,3}(`{3,}|~{3,})(.*?)(?:\r?\n)?$/);
+    if (fence) {
+      if (new RegExp(`^ {0,3}${fence.marker}{${fence.length},}[ \\t]*\\r?\\n?$`).test(line)) fence = undefined;
+    } else if (opening && !(opening[1][0] === "`" && opening[2].includes("`"))) {
+      fence = { marker: opening[1][0], length: opening[1].length };
+    } else {
+      const heading = line.match(/^##(?:[ \t]+(.*?))?[ \t]*\r?\n?$/);
+      if (heading) starts.push({ offset, title: (heading[1] ?? "").replace(/[ \t]+#+[ \t]*$/, "").trim() });
+    }
+    offset += line.length;
+  }
+  const seen = new Map<string, number>();
+  return starts.map((start, index) => {
+    const occurrence = seen.get(start.title) ?? 0;
+    seen.set(start.title, occurrence + 1);
+    return { id: index === 0 ? "preamble" : "s_" + hash(`${start.title}\0${occurrence}`).slice(0, 24),
+      title: start.title, content: content.slice(start.offset, starts[index + 1]?.offset), summary: "" };
+  });
+}
+
+export function sectionSchema(db: DatabaseSync) {
+  db.exec(`CREATE TABLE IF NOT EXISTS document_sections (
+    document_id TEXT PRIMARY KEY REFERENCES documents(id), payload TEXT NOT NULL
+  );
+  CREATE TABLE IF NOT EXISTS document_section_revisions (
+    document_id TEXT NOT NULL REFERENCES documents(id), revision INTEGER NOT NULL,
+    payload TEXT NOT NULL, PRIMARY KEY(document_id, revision)
+  );`);
+}
+
+export function loadSections(db: DatabaseSync, documentId: string, content: string): DocumentSection[] {
+  const row = db.prepare("SELECT payload FROM document_sections WHERE document_id=?")
+    .get(documentId) as { payload: string } | undefined;
+  if (!row) return splitSections(content);
+  const sections = JSON.parse(row.payload) as DocumentSection[];
+  // A legacy writer must not leave an apparently current outline over different Markdown.
+  return sections.map((s) => s.content).join("") === content ? sections : reconcileSections(content, sections);
+}
+
+export function reconcileSections(content: string, previous: DocumentSection[]): DocumentSection[] {
+  const unused = new Set(previous);
+  return splitSections(content).map((section) => {
+    const prior = section.id === "preamble" ? previous.find((p) => p.id === "preamble") :
+      previous.find((p) => p.id !== "preamble" && unused.has(p) && p.content === section.content) ??
+      previous.find((p) => p.id !== "preamble" && unused.has(p) && p.title === section.title);
+    if (prior) unused.delete(prior);
+    return { ...section, id: prior?.id ?? (section.id === "preamble" ? "preamble" : "s_" + randomUUID()),
+      summary: prior?.content === section.content ? prior.summary : "" };
+  });
+}
+
+export function persistSections(db: DatabaseSync, documentId: string, revision: number, sections: DocumentSection[]) {
+  const payload = JSON.stringify(sections);
+  db.prepare(`INSERT INTO document_sections VALUES (?,?) ON CONFLICT(document_id)
+    DO UPDATE SET payload=excluded.payload`).run(documentId, payload);
+  db.prepare("INSERT INTO document_section_revisions VALUES (?,?,?)").run(documentId, revision, payload);
+}
+
+export function outline(sections: DocumentSection[]) {
+  return sections.map(({ content, ...section }, position) => ({ ...section, position,
+    contentHash: hash(content), summaryStatus: section.summary.trim() ? "current" : "missing" }));
+}
+
+export function parseSectionEdit(value: unknown): SectionEdit {
+  if (!value || typeof value !== "object" || Array.isArray(value)) throw new DocumentValidationError("Expected section edit object");
+  const row = value as Record<string, unknown>;
+  if (Object.keys(row).some((key) => !["revision", "operations"].includes(key)) ||
+      !Number.isSafeInteger(row.revision) || (row.revision as number) < 1 || !Array.isArray(row.operations) ||
+      !row.operations.length || row.operations.length > 100) throw new DocumentValidationError("Provide revision and 1 to 100 operations");
+  for (const raw of row.operations) {
+    if (!raw || typeof raw !== "object" || Array.isArray(raw)) throw new DocumentValidationError("Invalid operation");
+    const op = raw as Record<string, unknown>;
+    const fields: Record<string, string[]> = { update: ["op", "id", "content", "summary"],
+      insert: ["op", "after", "content", "summary"], delete: ["op", "id"], move: ["op", "id", "after"] };
+    if (typeof op.op !== "string" || !Object.hasOwn(fields, op.op) || Object.keys(op).some((key) => !fields[op.op as string].includes(key)))
+      throw new DocumentValidationError("Unknown operation or field");
+    for (const key of fields[op.op].filter((key) => !["content", "summary", "op"].includes(key)))
+      if (typeof op[key] !== "string" || !(op[key] as string).length) throw new DocumentValidationError("Missing section identifier");
+    for (const key of ["content", "summary"])
+      if (op[key] !== undefined && typeof op[key] !== "string") throw new DocumentValidationError(`Invalid ${key}`);
+    if (op.op === "update" && op.content === undefined && op.summary === undefined) throw new DocumentValidationError("Empty update");
+    if (op.op === "insert" && (typeof op.content !== "string" || typeof op.summary !== "string" || !op.summary.trim()))
+      throw new DocumentValidationError("Insert requires content and summary");
+    if (typeof op.summary === "string" && (!op.summary.trim() || op.summary.length > 8000))
+      throw new DocumentValidationError("Summary must contain 1 to 8000 characters");
+  }
+  return { revision: row.revision as number, operations: row.operations as SectionOperation[] };
+}
+
+export function applySectionOperations(previous: DocumentSection[], operations: SectionOperation[]) {
+  const sections = previous.map((section) => ({ ...section }));
+  function index(id: string) {
+    const result = sections.findIndex((s) => s.id === id);
+    if (result < 0) throw new DocumentValidationError("Unknown section id");
+    return result;
+  }
+  for (const op of operations) {
+    if (op.op === "insert") {
+      sections.splice(index(op.after) + 1, 0, { id: "s_" + randomUUID(), title: "", content: op.content, summary: op.summary });
+    } else {
+      const at = index(op.id);
+      if (op.op === "update") {
+        if (op.content !== undefined && op.content !== sections[at].content) sections[at].summary = "";
+        if (op.content !== undefined) sections[at].content = op.content;
+        if (op.summary !== undefined) sections[at].summary = op.summary;
+      } else {
+        if (op.id === "preamble") throw new DocumentValidationError("Cannot delete or move the preamble");
+        if (op.op === "delete") sections.splice(at, 1);
+        else {
+          if (op.id === op.after) throw new DocumentValidationError("Cannot move section after itself");
+          index(op.after);
+          const [section] = sections.splice(at, 1);
+          sections.splice(index(op.after) + 1, 0, section);
+        }
+      }
+    }
+  }
+  const content = sections.map((s) => s.content).join("");
+  const parsed = splitSections(content);
+  if (parsed.length !== sections.length || parsed.some((s, i) => s.content !== sections[i].content))
+    throw new DocumentValidationError("Each chapter must start with one H2 heading; preserve newline boundaries and balanced fences");
+  return sections.map((s, i) => ({ ...s, title: parsed[i].title }));
+}

apps/docs/src/lib/research-statistics.ts

@@ -1,6 +1,50 @@
 import type { ResearchCompany, ResearchObservation, ResearchSnapshot } from "./ai-research";
 
 export type IncomePoint = { company: ResearchCompany; observation: ResearchObservation; value: number };
+
+/** An evidence ledger, never a comparable sample: retain historical claims and estimates separately. */
+export function arrDisclosures(snapshot: ResearchSnapshot, options: { category?: string; foundedYear?: number } = {}) {
+  const companies = new Map(snapshot.companies.map((company) => [company.id, company]));
+  return snapshot.observations.flatMap((observation) => {
+    const company = companies.get(observation.company);
+    if (!company || ![2025, 2026].includes(company.year ?? 0) || observation.metric !== "arr" ||
+        !["signal", "estimate", "historical", "historical-estimate"].includes(observation.selection) ||
+        !Number.isFinite(observation.value) || observation.value < 0 ||
+        (options.category && company.cat !== options.category) ||
+        (options.foundedYear && company.year !== options.foundedYear)) return [];
+    return [{ company, observation }];
+  });
+}
+
+/** Count companies once; missing ARR is a coverage gap, never zero revenue. */
+export function arrDisclosureCoverage(snapshot: ResearchSnapshot, foundedYear?: number) {
+  const rows = arrDisclosures(snapshot, { foundedYear });
+  return snapshot.categories.map((category) => {
+    const companies = snapshot.companies.filter((company) => company.cat === category.id &&
+      [2025, 2026].includes(company.year ?? 0) && (!foundedYear || company.year === foundedYear));
+    const claims = rows.filter((row) => row.company.cat === category.id);
+    const disclosed = new Set(claims.map((row) => row.company.id));
+    const reported = new Set(claims.filter((row) =>
+      ["signal", "historical"].includes(row.observation.selection)).map((row) => row.company.id));
+    return { id: category.id, name: category.name, companies: companies.length,
+      disclosed: disclosed.size, reported: reported.size, estimatedOnly: disclosed.size - reported.size,
+      missing: companies.length - disclosed.size, records: claims.length };
+  });
+}
+
+export type ArrFieldGap = "date" | "currency" | "amount" | "estimate";
+
+/** Prerequisites for inspecting exact, dated USD disclosures; never a comparability or audit verdict. */
+export function arrFieldGaps(observation: ResearchObservation, asOf: string): ArrFieldGap[] {
+  const gaps: ArrFieldGap[] = [];
+  const day = calendarDay(observation.observedAt);
+  const end = calendarDay(asOf);
+  if (day === null || end === null || day > end) gaps.push("date");
+  if (observation.unit !== "USD") gaps.push("currency");
+  if (observation.bound !== "=" || !Number.isFinite(observation.value) || observation.value < 0) gaps.push("amount");
+  if (["estimate", "historical-estimate"].includes(observation.selection)) gaps.push("estimate");
+  return gaps;
+}
 export type IncomeStats = {
   n: number; mean: number; median: number; min: number; max: number;
   sampleDeviation: number | null; cv: number | null; meanMedianRatio: number | null;
@@ -80,3 +124,24 @@ export function groupIncome(points: IncomePoint[], splitModels = true) {
   }
   return [...groups.entries()].map(([key, group]) => ({ key, ...group, stats: describeIncome(group.points.map((point) => point.value))! }));
 }
+
+/** Compare descriptive statistics only within the same provider and delivery model. */
+export function compareIncomeGroups(groups: ReturnType<typeof groupIncome>) {
+  const buckets = new Map<string, typeof groups>();
+  for (const group of groups) {
+    if (group.stats.n < 3 || group.stats.cv === null || group.stats.largestRemovalDrop === null ||
+        group.model === "全部收费模式" || group.model === "收费模式未核验") continue;
+    const key = JSON.stringify([group.provider, group.model]);
+    const bucket = buckets.get(key) ?? [];
+    bucket.push(group);
+    buckets.set(key, bucket);
+  }
+  return [...buckets.entries()].flatMap(([key, rows]) => {
+    if (rows.length < 2) return [];
+    const byCV = [...rows].sort((a, b) => a.stats.cv! - b.stats.cv! || a.key.localeCompare(b.key));
+    const bySensitivity = [...rows].sort((a, b) => a.stats.largestRemovalDrop! - b.stats.largestRemovalDrop! || a.key.localeCompare(b.key));
+    return [{ key, provider: rows[0].provider, model: rows[0].model,
+      leastDispersed: byCV[0], mostDispersed: byCV[byCV.length - 1],
+      leastSensitive: bySensitivity[0], mostSensitive: bySensitivity[bySensitivity.length - 1] }];
+  });
+}

apps/docs/src/pages/api/documents/sections/[...slug].ts

@@ -0,0 +1,37 @@
+import type { NextApiRequest, NextApiResponse } from "next";
+import { DocumentValidationError, readDocumentSections, editDocumentSections } from "@/lib/docs";
+import { outline } from "@/lib/document-sections";
+import { authorizeDocuments, documentApiError, idempotency } from "@/lib/documents-api";
+
+export const config = { api: { bodyParser: { sizeLimit: "8mb" } } };
+export default function handler(request: NextApiRequest, response: NextApiResponse) {
+  try {
+    const key = authorizeDocuments(request, response, request.method === "POST");
+    if (!key) return;
+    if (request.method !== "GET" && request.method !== "POST") {
+      response.setHeader("Allow", "GET, POST");
+      return response.status(405).end();
+    }
+    if (!Array.isArray(request.query.slug)) throw new DocumentValidationError("Invalid document slug");
+    const slug = request.query.slug.join("/");
+    if (Object.keys(request.query).some((k) => !["slug", "section", "revision"].includes(k)))
+      throw new DocumentValidationError("Unknown section query");
+    if (request.method === "POST") {
+      if (request.query.section !== undefined || request.query.revision !== undefined)
+        throw new DocumentValidationError("POST uses revision and operations in the body");
+      const saved = editDocumentSections(slug, request.body, idempotency(request, key.id));
+      return saved ? response.status(200).json(saved) : response.status(404).json({ code: "not_found" });
+    }
+    const revision = request.query.revision;
+    if (revision !== undefined && (typeof revision !== "string" || !/^[1-9]\d*$/.test(revision)))
+      throw new DocumentValidationError("Invalid revision");
+    const doc = readDocumentSections(slug, revision === undefined ? undefined : Number(revision));
+    if (!doc) return response.status(404).json({ code: "not_found" });
+    const selected = request.query.section;
+    if (selected === undefined) return response.status(200).json({ ...doc, sections: outline(doc.sections) });
+    const ids = typeof selected === "string" ? [selected] : selected;
+    if (!ids.length || ids.length > 100 || ids.some((id) => !id)) throw new DocumentValidationError("Select 1 to 100 sections");
+    if (ids.some((id) => !doc.sections.some((s) => s.id === id))) return response.status(404).json({ code: "not_found" });
+    return response.status(200).json({ ...doc, sections: doc.sections.filter((s) => ids.includes(s.id)) });
+  } catch (error) { return documentApiError(response, error); }
+}

apps/docs/src/styles/globals.css

@@ -1473,6 +1473,13 @@ svg { flex-shrink: 0; }
 
 .research-distribution { margin: 24px 0 38px; }
 .research-distribution figure { margin: 20px 0; }
+.ai-research .income-statistics { min-width: 1200px; }
+/* Row-spanning category headings shift cell indexes between rows. */
+.ai-research .income-statistics td { min-width: 0; }
+.ai-research .income-statistics th[scope="rowgroup"] { min-width: 150px; white-space: nowrap; }
+.ai-research .income-statistics th[scope="rowgroup"] small { display: block; margin-top: 5px; }
+.ai-research .income-statistics td:not(:last-child) { white-space: nowrap; }
+.ai-research .income-statistics td:last-child { min-width: 220px; }
 .income-distribution-chart { display: block; width: 100%; min-width: 900px; height: auto; }
 .income-distribution-chart [role="button"] { cursor: pointer; outline: none; }
 .income-distribution-chart [role="button"]:focus circle:last-child { stroke: #b17725; stroke-width: 3; }

apps/docs/tests/documents-api.test.ts

@@ -7,8 +7,11 @@ import type { NextApiRequest, NextApiResponse } from "next";
 import documents from "../src/pages/api/documents/index";
 import batch from "../src/pages/api/documents/batch";
 import item from "../src/pages/api/documents/item/[...slug]";
+import sections from "../src/pages/api/documents/sections/[...slug]";
+import { splitSections } from "../src/lib/document-sections";
+import { openSiteDatabase } from "../src/lib/site-database";
 import { createDocumentKey, authenticateDocumentKey, revokeDocumentKey, listDocumentKeys } from "../src/lib/document-keys";
-import { allDocuments, documentHistory, readDocument, saveDocument, type Doc } from "../src/lib/docs";
+import { allDocuments, documentHistory, readDocument, saveDocument, readDocumentSections, type Doc } from "../src/lib/docs";
 import { newAuthConfig, signSession } from "../src/lib/auth";
 
 const input: Doc = { slug: "endor/example", sourceSlug: "00-example", title: "Example", category: "Endor",
@@ -134,3 +137,122 @@ test("invalid batch members, duplicate identifiers, raw images and malformed fie
   assert.equal(call(documents, { method: "DELETE", headers }).status, 405);
   assert.equal(call(item, { headers, query: { slug: ["missing"] } }).status, 404);
 }));
+
+const chapterContent = "# Example\n\nIntroduction.\n\n## First\n\nFirst body.\n\n### Detail\n\nChild stays in chapter.\n\n## Second\n\nSecond body.\n";
+type SectionResponse = { revision: number; replayed?: boolean;
+  sections: { id: string; title: string; summary: string; summaryStatus?: string; content?: string }[] };
+const chapterQuery = { slug: input.slug.split("/") };
+
+test("chapter parser preserves bytes, fences, duplicate headings and CRLF", () => {
+  const content = "# Title\r\n\r\n## Repeated\r\n~~~text\r\n## Inside fence\r\n~~~\r\n### Nested\r\n```js\r\n## Also inside\r\n````\r\n\r\n## Repeated\r\nTail";
+  const parts = splitSections(content);
+  assert.equal(parts.length, 3);
+  assert.notEqual(parts[1].id, parts[2].id);
+  assert.equal(parts.map((p) => p.content).join(""), content);
+  assert.equal(parts[1].title, "Repeated");
+  assert.ok(parts[1].content.includes("### Nested"));
+  assert.equal(splitSections("").length, 1);
+  assert.equal(splitSections("# Title\n\n- Item\n  ## List detail\n\n> ## Quoted\n").length, 1);
+});
+
+test("outline reads existing Markdown without changing it and returns only selected bodies", () => isolated(({ headers }) => {
+  saveDocument({ ...input, content: chapterContent }, 0);
+  const db = openSiteDatabase();
+  db.exec("DELETE FROM document_sections; DELETE FROM document_section_revisions;"); db.close();
+  const result = call(sections, { headers, query: chapterQuery });
+  assert.equal(result.status, 200);
+  const outline = result.body as SectionResponse;
+  assert.equal(outline.revision, 1); assert.equal(outline.sections.length, 3);
+  assert.deepEqual(outline.sections.map((s) => s.title), ["导言", "First", "Second"]);
+  assert.ok(outline.sections.every((s) => s.content === undefined && s.summaryStatus === "missing"));
+  const selected = call(sections, { headers, query: { ...chapterQuery, section: outline.sections[1].id } }).body as SectionResponse;
+  assert.equal(selected.sections.length, 1);
+  assert.ok(selected.sections[0].content!.includes("### Detail"));
+  assert.equal(readDocument(input.slug)!.content, chapterContent);
+  assert.equal(call(sections, { headers, query: { ...chapterQuery, section: "missing" } }).status, 404);
+  assert.equal(call(sections, { headers, query: { ...chapterQuery, revision: "0" } }).status, 400);
+}));
+
+test("internal summaries persist separately and chapter updates leave other bytes untouched", () => isolated(({ headers }) => {
+  saveDocument({ ...input, content: chapterContent }, 0);
+  const before = readDocumentSections(input.slug)!;
+  const first = before.sections[1]; const second = before.sections[2];
+  const changed = first.content.replace("First body.", "Revised body.");
+  const edited = call(sections, { method: "POST", headers, query: chapterQuery, body: { revision: 1, operations: [
+    { op: "update", id: first.id, content: changed, summary: "INTERNAL_SUMMARY_FIRST" },
+    { op: "update", id: second.id, summary: "INTERNAL_SUMMARY_SECOND" },
+  ] } });
+  assert.equal(edited.status, 200);
+  const after = readDocumentSections(input.slug)!;
+  assert.equal(after.revision, 2); assert.equal(after.sections[1].id, first.id);
+  assert.equal(after.sections[0].content, before.sections[0].content);
+  assert.equal(after.sections[2].content, before.sections[2].content);
+  assert.equal(after.sections[1].summary, "INTERNAL_SUMMARY_FIRST");
+  assert.equal(readDocument(input.slug)!.content, chapterContent.replace("First body.", "Revised body."));
+  assert.ok(!JSON.stringify(allDocuments()).includes("INTERNAL_SUMMARY"));
+  assert.ok(!JSON.stringify(call(item, { headers, query: chapterQuery }).body).includes("INTERNAL_SUMMARY"));
+  assert.ok(!JSON.stringify(call(documents, { headers }).body).includes("INTERNAL_SUMMARY"));
+  const history = readDocumentSections(input.slug, 1)!;
+  assert.equal(history.sections[1].content, first.content); assert.equal(history.sections[1].summary, "");
+  assert.equal(readDocumentSections(input.slug, 2)!.sections[1].summary, "INTERNAL_SUMMARY_FIRST");
+}));
+
+test("insert, move and delete chapters commit together with replay and conflict protection", () => isolated(({ headers }) => {
+  saveDocument({ ...input, content: chapterContent }, 0);
+  const before = readDocumentSections(input.slug)!;
+  const first = before.sections[1]; const second = before.sections[2];
+  const body = { revision: 1, operations: [
+    { op: "move", id: second.id, after: "preamble" },
+    { op: "delete", id: first.id },
+    { op: "insert", after: second.id, content: "## New chapter\n\nNew body.\n", summary: "New material" },
+  ] };
+  const options = { method: "POST", headers: { ...headers, "idempotency-key": "chapters-1" }, query: chapterQuery, body };
+  assert.equal(call(sections, options).status, 200);
+  const repeated = call(sections, options);
+  assert.equal(repeated.status, 200); assert.equal((repeated.body as SectionResponse).replayed, true);
+  const after = readDocumentSections(input.slug)!;
+  assert.deepEqual(after.sections.map((s) => s.title), ["导言", "Second", "New chapter"]);
+  assert.equal(after.sections[1].content, second.content);
+  assert.equal(after.revision, 2); assert.equal(documentHistory(input.sourceSlug).length, 2);
+  assert.equal(call(sections, { ...options, body: { ...body, revision: 2 } }).status, 409);
+  assert.equal(call(sections, { ...options, headers }).status, 409);
+  assert.equal(saveDocument({ ...readDocument(input.slug)!, description: "Changed metadata" }, 2).revision, 3);
+  assert.equal(readDocumentSections(input.slug)!.sections[2].summary, "New material");
+}));
+
+test("invalid late operation rolls back content, summary and history; read-only keys cannot edit", () => isolated(({ headers }) => {
+  saveDocument({ ...input, content: chapterContent }, 0);
+  const before = readDocumentSections(input.slug)!;
+  const invalid = [
+    [{ op: "update", id: before.sections[1].id, summary: "Should roll back" }, { op: "delete", id: "missing" }],
+    [{ op: "delete", id: "preamble" }],
+    [{ op: "move", id: before.sections[1].id, after: before.sections[1].id }],
+    [{ op: "update", id: before.sections[1].id, content: "## First\n\n## Extra\n" }],
+    [{ op: "update", id: before.sections[1].id, content: "## First\n![raw](/figures/raw.png)\n" }],
+    [{ op: "insert", after: "preamble", content: "## Missing summary\n" }],
+    [{ op: "delete", id: before.sections[1].id, unexpected: true }],
+  ];
+  for (const operations of invalid) {
+    assert.equal(call(sections, { method: "POST", headers, query: chapterQuery, body: { revision: 1, operations } }).status, 400);
+    assert.deepEqual(readDocumentSections(input.slug), before);
+    assert.equal(documentHistory(input.sourceSlug).length, 1);
+  }
+  assert.equal(call(sections, { query: chapterQuery }).status, 401);
+  const reader = createDocumentKey("chapter reader", "read");
+  const readHeaders = { authorization: "Bearer " + reader.token };
+  assert.equal(call(sections, { headers: readHeaders, query: chapterQuery }).status, 200);
+  assert.equal(call(sections, { method: "POST", headers: readHeaders, query: chapterQuery, body: { revision: 1, operations: [] } }).status, 403);
+}));
+
+test("full-document saves invalidate changed chapter summaries and preserve unaffected summaries and ids", () => isolated(({ headers }) => {
+  saveDocument({ ...input, content: chapterContent }, 0);
+  const before = readDocumentSections(input.slug)!;
+  assert.equal(call(sections, { method: "POST", headers, query: chapterQuery, body: { revision: 1, operations:
+    before.sections.map((s) => ({ op: "update", id: s.id, summary: "Summary of " + s.title })) } }).status, 200);
+  saveDocument({ ...readDocument(input.slug)!, content: chapterContent.replace("First body.", "Changed externally.") }, 2);
+  const after = readDocumentSections(input.slug)!;
+  assert.equal(after.sections[1].id, before.sections[1].id);
+  assert.equal(after.sections[1].summary, "");
+  assert.equal(after.sections[2].summary, "Summary of Second");
+  assert.equal(readDocumentSections(input.slug, 2)!.sections[1].summary, "Summary of First");
+}));

content/research/ai-income-analysis.json

@@ -1,6 +1,6 @@
 {
-  "sourceVersion": "2026-10-30-ph-history-1",
-  "asOf": "2026-10-30",
+  "sourceVersion": "2026-11-01-sr-learning-health-1",
+  "asOf": "2026-11-01",
   "metric": "platform-mrr-usd",
   "sampleUnit": "product-or-business",
   "note": "独立Python复算参考值，仅验证样本计算；不是行业估计或财务审计。",
@@ -21,7 +21,9 @@
         "MR011",
         "MR012",
         "MR015",
-        "MR016"
+        "MR016",
+        "MR018",
+        "MR019"
       ],
       "groups": [
         {
@@ -35,7 +37,7 @@
             "MR016"
           ],
           "n": 4,
-          "mean": 34815.0,
+          "mean": 34815,
           "median": 36723.0,
           "sampleDeviation": 16334.54845820151,
           "cv": 0.46918134304758036,
@@ -50,7 +52,7 @@
             "MR001"
           ],
           "n": 1,
-          "mean": 53101.0,
+          "mean": 53101,
           "median": 53101,
           "sampleDeviation": null,
           "cv": null,
@@ -65,7 +67,7 @@
             "MR002"
           ],
           "n": 1,
-          "mean": 62423.0,
+          "mean": 62423,
           "median": 62423,
           "sampleDeviation": null,
           "cv": null,
@@ -79,15 +81,17 @@
           "products": [
             "MR003",
             "MR004",
-            "MR005"
+            "MR005",
+            "MR018",
+            "MR019"
           ],
-          "n": 3,
-          "mean": 41693.0,
-          "median": 49069,
-          "sampleDeviation": 28111.375064197768,
-          "cv": 0.6742468775141575,
-          "meanWithoutLargest": 29849.0,
-          "largestRemovalDrop": 0.2840764636749574
+          "n": 5,
+          "mean": 51674.8,
+          "median": 65381,
+          "sampleDeviation": 24138.82269705795,
+          "cv": 0.4671294847209461,
+          "meanWithoutLargest": 47627.5,
+          "largestRemovalDrop": 0.07832250923080501
         },
         {
           "category": "C09",
@@ -105,7 +109,7 @@
           "median": 20483,
           "sampleDeviation": 11942.947278624319,
           "cv": 0.4711370488466823,
-          "meanWithoutLargest": 20046.0,
+          "meanWithoutLargest": 20046,
           "largestRemovalDrop": 0.20920581320120557
         }
       ]
@@ -127,9 +131,27 @@
         "MR012",
         "MR015",
         "MR016",
-        "AI016"
+        "AI016",
+        "MR017",
+        "MR018",
+        "MR019"
       ],
       "groups": [
+        {
+          "category": "C02",
+          "provider": "Stripe",
+          "model": "模型网关订阅与用量",
+          "products": [
+            "MR017"
+          ],
+          "n": 1,
+          "mean": 78764,
+          "median": 78764,
+          "sampleDeviation": null,
+          "cv": null,
+          "meanWithoutLargest": null,
+          "largestRemovalDrop": null
+        },
         {
           "category": "C06",
           "provider": "Stripe",
@@ -146,7 +168,7 @@
           "median": 40112,
           "sampleDeviation": 174786.78744916618,
           "cv": 1.5505509613536426,
-          "meanWithoutLargest": 34815.0,
+          "meanWithoutLargest": 34815,
           "largestRemovalDrop": 0.6911526751687284
         },
         {
@@ -157,7 +179,7 @@
             "MR001"
           ],
           "n": 1,
-          "mean": 53101.0,
+          "mean": 53101,
           "median": 53101,
           "sampleDeviation": null,
           "cv": null,
@@ -172,7 +194,7 @@
             "MR002"
           ],
           "n": 1,
-          "mean": 62423.0,
+          "mean": 62423,
           "median": 62423,
           "sampleDeviation": null,
           "cv": null,
@@ -186,15 +208,17 @@
           "products": [
             "MR003",
             "MR004",
-            "MR005"
+            "MR005",
+            "MR018",
+            "MR019"
           ],
-          "n": 3,
-          "mean": 41693.0,
-          "median": 49069,
-          "sampleDeviation": 28111.375064197768,
-          "cv": 0.6742468775141575,
-          "meanWithoutLargest": 29849.0,
-          "largestRemovalDrop": 0.2840764636749574
+          "n": 5,
+          "mean": 51674.8,
+          "median": 65381,
+          "sampleDeviation": 24138.82269705795,
+          "cv": 0.4671294847209461,
+          "meanWithoutLargest": 47627.5,
+          "largestRemovalDrop": 0.07832250923080501
         },
         {
           "category": "C09",
@@ -212,7 +236,7 @@
           "median": 20483,
           "sampleDeviation": 11942.947278624319,
           "cv": 0.4711370488466823,
-          "meanWithoutLargest": 20046.0,
+          "meanWithoutLargest": 20046,
           "largestRemovalDrop": 0.20920581320120557
         }
       ]

scripts/audit-ai-research.py

@@ -4,7 +4,7 @@ from collections import Counter
 import json
 from pathlib import Path
 import sys
-from urllib.parse import urlsplit
+from urllib.parse import parse_qs, urlsplit
 
 ROOT = Path(__file__).resolve().parents[1]
 DATA = json.loads((ROOT / 'content/research/ai-companies.seed.json').read_text())
@@ -16,6 +16,20 @@ SOURCE_SLUG = 'ai-company-research/02-coverage-and-gaps'
 BASE = '/docs/other-research/ai-company-research/overview'
 
 
+def yc_coverage_summary():
+    companies = {row['id']: row for row in DATA['companies']}
+    imported = [companies[f"YC{row['ycId']}"] for row in AUDIT['records'] if row['status'] == 'imported']
+    profile_only = sum(bool(row.get('profileOnly')) for row in imported)
+    groups = {}
+    for label, prefix in [('unclearAI', 'YC-AI-UNCLEAR-'), ('missingYear', 'YC-MISSING-YEAR-'),
+                          ('dateConflict', 'YC-DATE-CONFLICT-')]:
+        rows = [row for batch in DATA.get('candidateBatches', []) if batch['id'].startswith(prefix)
+                for row in batch['records']]
+        groups[label] = {'records': len(rows), 'statuses': dict(sorted(Counter(row['status'] for row in rows).items()))}
+    return {'initialProfiles': len(AUDIT['records']), 'originalImported': len(imported),
+            'profileOnly': profile_only, 'deepenedOriginal': len(imported) - profile_only, **groups}
+
+
 
 def source_register():
     sources = SOURCES['sources']
@@ -26,6 +40,63 @@ def source_register():
     for source in sources:
         assert source['status'] in {'candidate-audit', 'targeted', 'pending', 'context', 'access-limited', 'per-company'}
         assert source['boundary'] and source['nextCheck']
+        if source['id'] == 'S01':
+            assert source.get('coverageSummary') == yc_coverage_summary(), 'YC source summary is stale'
+        if source['id'] == 'S14' and source.get('rosterAudit'):
+            roster = source['rosterAudit']
+            batch = next(batch for batch in DATA['candidateBatches'] if batch['id'] == roster['candidateBatchId'])
+            assert batch['sourceId'] == 'S14' and not batch['complete']
+            assert roster['defaultCohort'] == 'SR007' and roster['next'] is None
+            assert len(batch['records']) == roster['recordCount'] == roster['reportedCount']
+            assert len({row['url'] for row in batch['records']}) == roster['recordCount']
+        if source['id'] == 'S14' and source.get('publicRosterAudits'):
+            audits = source['publicRosterAudits']
+            assert {a['cohort'] for a in audits} == {f'SR{i:03d}' for i in range(1, 8)}
+            assert len(audits) == 7
+            profile_urls = []
+            for audit in audits:
+                batch = next(b for b in DATA['candidateBatches'] if b['id'] == audit['candidateBatchId'])
+                query = parse_qs(urlsplit(audit['url']).query)
+                assert urlsplit(audit['url']).netloc == 'speedrun.a16z.com'
+                assert query == {'limit': ['100'], 'offset': ['0'], 'cohort': [audit['cohort']]}
+                assert urlsplit(audit['url']).path == '/api/companies/companyparams'
+                assert audit['httpStatus'] == 200 and audit['accessedAt'] <= DATA['asOf']
+                assert audit['next'] is None and audit['previous'] is None
+                assert len(batch['records']) == audit['recordCount'] == audit['reportedCount']
+                assert audit['uniquePublicIds'] == audit['uniqueSlugs'] == audit['recordCount']
+                assert len(audit['responseSha256']) == 64 and all(c in '0123456789abcdef' for c in audit['responseSha256'])
+                assert batch['sourceId'] == 'S14' and not batch['complete']
+                proof = next(e for e in DATA['evidence'] if e['id'] == audit['evidenceId'])
+                assert proof['url'] == audit['url'] and proof['companyId'] is None
+                profile_urls.extend(row['url'] for row in batch['records'])
+            assert len(profile_urls) == len(set(profile_urls)), 'duplicate profiles across public cohorts'
+        if source['id'] == 'S14' and source.get('profileAudit'):
+            audit = source['profileAudit']
+            entries = audit['records']
+            candidates = {row['id']: row for b in DATA['candidateBatches'] for row in b['records']}
+            evidence_by_id = {row['id']: row for row in DATA['evidence']}
+            assert len(entries) == audit['recordCount'] == audit['readable']
+            assert audit['failed'] == 0 and len({e['candidateId'] for e in entries}) == len(entries)
+            years = Counter(str(e['foundedYear']) for e in entries)
+            assert dict(years) == audit['yearCounts']
+            for entry in entries:
+                row = candidates[entry['candidateId']]
+                proof = evidence_by_id[entry['evidenceId']]
+                assert row['profileStatus'] == 'readable' and proof['url'] == row['url']
+                assert entry['evidenceId'] in row['evidenceIds']
+                assert isinstance(entry['foundedYear'], int) and 1800 <= entry['foundedYear'] <= int(DATA['asOf'][:4])
+                assert isinstance(entry['teamSize'], int) and entry['teamSize'] >= 0
+                assert len(entry['htmlSha256']) == 64 and all(c in '0123456789abcdef' for c in entry['htmlSha256'])
+                if entry['foundedYear'] not in [2025, 2026]:
+                    assert row['status'] == 'outside-cohort' and not row.get('companyId')
+            assert sum(e['foundedYear'] not in [2025, 2026] for e in entries) == audit['outsideTargetYears']
+            assert sum(e['foundedYear'] in [2025, 2026] for e in entries) == audit['targetYearSelfReports']
+            assert sum(candidates[e['candidateId']]['status'] == 'linked' for e in entries) == audit['newLinkedCompanies']
+            assert sum(candidates[e['candidateId']]['status'] == 'pending-ai' for e in entries) == audit['pendingCurrentAIOrIdentity']
+            assert sum(candidates[e['candidateId']]['status'] == 'pending-year' for e in entries) == audit['pendingFoundingIdentity']
+            assert audit['targetYearSelfReports'] == (
+                audit['newLinkedCompanies'] + audit['pendingCurrentAIOrIdentity'] + audit['pendingFoundingIdentity']
+            ), 'self-reported years must retain unresolved brand/entity dates'
         if source['url']:
             assert urlsplit(source['url']).scheme in {'http', 'https'} and urlsplit(source['url']).netloc
         for host in source['hosts']:
@@ -49,7 +120,18 @@ def source_register():
         company_ids = {e['companyId'] for e in evidence if e['companyId']}
         observations = sum(o['evidence'] in evidence_ids for o in DATA['observations'])
         name = f"[{source['name']}]({source['url']})" if source['url'] else source['name']
-        lines.append(f"| {source['id']} {name} | {statuses[source['status']]} | {len(evidence)} / {len(company_ids)} / {observations} | {source['boundary']} | {source['nextCheck']} |")
+        boundary = source['boundary']
+        if source['id'] == 'S01':
+            summary = source['coverageSummary']
+            boundary += (f" 当前原候选池{summary['initialProfiles']}项，原新增{summary['originalImported']}项中"
+                         f"{summary['profileOnly']}项目录基础资料、{summary['deepenedOriginal']}项深化个案。")
+            for key, label in [('unclearAI', '原AI不明'), ('missingYear', '原缺年'), ('dateConflict', '原日期冲突')]:
+                group = summary[key]
+                labels = {'linked': '关联公司', 'pending-ai': 'AI主营待核',
+                          'pending-year': '成立/主体待核', 'outside-cohort': '范围外'}
+                counts = '、'.join(f'{labels.get(status, status)} {count}' for status, count in group['statuses'].items())
+                boundary += f" {label}{group['records']}项当前：{counts}。"
+        lines.append(f"| {source['id']} {name} | {statuses[source['status']]} | {len(evidence)} / {len(company_ids)} / {observations} | {boundary} | {source['nextCheck']} |")
     lines += ['', 'Dealroom、Product Hunt、机器之心与36氪入口于2026-10-30重新访问。官网首页可读不证明已取得完整筛选结果；安全检测页面不作为文章内容。候选池之外的来源边界尚需建立可回查条目，完成前不宣布来源清单已全部覆盖。', '']
     return lines
 
@@ -85,7 +167,7 @@ def candidate_register():
     all_ids = [r['id'] for b in batches for r in b['records']]
     assert len(set(all_ids)) == len(all_ids)
     lines = []
-    statuses = {'pending-year': '成立年待核', 'linked': '已关联公司',
+    statuses = {'pending-year': '成立年待核', 'pending-ai': 'AI主营待核', 'linked': '已关联公司',
                 'existing-outside-cohort': '已有关联，年份范围外',
                 'outside-cohort': '范围外或既有业务，不新增公司', 'promotion': '推广活动，非公司新增'}
     for batch in batches:
@@ -97,13 +179,13 @@ def candidate_register():
         assert sum(counts.values()) == len(rows)
         lines += ['', f"## {batch['sourceId']} 候选核对（{batch['id']}）", '',
             f"[发现页面]({batch['url']})，读取于{batch['accessedAt']}。{batch['scope']}", '',
-            f"本批{len(rows)}条：{counts['linked']}条关联公司、{counts['existing-outside-cohort']}条关联范围外对照、{counts['outside-cohort']}条范围外不新增公司、{counts['pending-year']}条成立年待核、{counts['promotion']}条推广活动。完整来源覆盖标记：{'已完成' if batch['complete'] else '未完成'}。主营或产品任务的初分类不计入主公司表数量，只有已关联的公司记录进入对应公司明细。候选与缺项随研究版本保存在文档站同一SQLite的研究元数据中。", '',
+            f"本批{len(rows)}条：{counts['linked']}条关联公司、{counts['existing-outside-cohort']}条关联范围外对照、{counts['outside-cohort']}条范围外不新增公司、{counts['pending-year']}条成立年待核、{counts['pending-ai']}条AI主营待核、{counts['promotion']}条推广活动。完整来源覆盖标记：{'已完成' if batch['complete'] else '未完成'}。主营或产品任务的初分类不计入主公司表数量，只有已关联的公司记录进入对应公司明细。候选与缺项随研究版本保存在文档站同一SQLite的研究元数据中。", '',
             '| 发现条目 | 主营或产品任务初分 | 核对状态 | 关联公司 | 核对证据 | 限制 |', '| --- | --- | --- | --- | --- | --- |']
         for row in rows:
             assert row['categoryId'] in categories
             assert row['status'] in statuses
             assert all(eid in evidence for eid in row.get('evidenceIds', []))
-            if row['status'] == 'outside-cohort':
+            if row['status'] in {'outside-cohort', 'pending-ai'}:
                 assert row.get('evidenceIds') and not row.get('companyId')
             name = f"[{row['name']}]({row['url']})" if row['url'] else row['name']
             company = '—'
@@ -121,6 +203,73 @@ def candidate_register():
     return lines
 
 
+def uncertain_ai_followups():
+    original = {row['source']: row for row in AUDIT['records'] if row['status'] == 'ai-unclear'}
+    rows = [row for batch in DATA.get('candidateBatches', [])
+            if batch['id'].startswith('YC-AI-UNCLEAR-') for row in batch['records']]
+    if not rows:
+        return []
+    urls = [row['url'] for row in rows]
+    assert len(set(urls)) == len(urls), 'Duplicate AI-relevance follow-up URL'
+    rechecks = {row['ycId']: row for row in DATA.get('directoryRechecks', [])}
+    counts = Counter(row['status'] for row in rows)
+    assert set(counts) <= {'linked', 'pending-ai'}
+    for row in rows:
+        assert row['url'] in original and row.get('evidenceIds')
+        baseline = original[row['url']]
+        if row['status'] == 'linked':
+            assert rechecks[baseline['ycId']]['companyId'] == row['companyId']
+        else:
+            assert not row.get('companyId') and baseline['ycId'] not in rechecks
+    unread = len(original) - len(rows)
+    assert unread >= 0
+    return ['', '## 原AI主营不明名单的后续覆盖', '',
+        f"原名单{len(original)}项，已有后续记录{len(rows)}项：{counts['linked']}项关联公司、{counts['pending-ai']}项仍待核AI主营，{unread}项尚无本批系列的后续记录。计数按原始YC页面去重；EveryApp此前的独立复核与末批记录不重复相加。", '',
+        '读完名单只证明每项有可回查的处理记录，不证明待核项均为AI公司，也不证明法律主体、收入或技术效果已独立验证。原始发现审计保持不变，成立年范围外/缺失、成立冲突及其他来源的缺项仍单列。', '']
+
+
+def founding_year_followups():
+    original = {row['source']: row for row in AUDIT['records'] if row['status'] == 'date-conflict'}
+    rows = [row for batch in DATA.get('candidateBatches', [])
+            if batch['id'].startswith('YC-DATE-CONFLICT-') for row in batch['records']]
+    urls = [row['url'] for row in rows]
+    assert len(set(urls)) == len(urls), 'Duplicate founding-conflict follow-up URL'
+    assert set(urls) <= set(original), 'Unknown founding-conflict follow-up URL'
+    evidence = {row['id']: row for row in DATA['evidence']}
+    for row in rows:
+        assert row['status'] == 'pending-year' and not row.get('companyId')
+        assert row.get('evidenceIds') and all(eid in evidence for eid in row['evidenceIds'])
+        assert all(evidence[eid]['companyId'] is None for eid in row['evidenceIds'])
+    unread = len(original) - len(rows)
+    return ['', '## 原成立年冲突名单的后续覆盖', '',
+        f"原名单{len(original)}项，已有后续记录{len(rows)}项，均保留成立年待核，{unread}项尚无本系列后续记录。当前业务初分类见候选表，不计入主公司表及收入统计。", '',
+        '2025旧品牌发布可证明当时已有产品材料；当前目录的2026字段、批次、条款日期和运营者名称不能单独证明法律主体成立或新旧主体连续。未据此强改成立年。Layers当前AI核心仍另待核。', '']
+
+
+def missing_year_followups():
+    original = {row['source'] for row in AUDIT['records']
+                if row['status'] == 'year-outside-or-missing' and row['foundedYear'] is None}
+    rows = [row for batch in DATA.get('candidateBatches', [])
+            if batch['id'].startswith('YC-MISSING-YEAR-') for row in batch['records']]
+    urls = [row['url'] for row in rows]
+    assert len(set(urls)) == len(urls) and set(urls) <= original
+    evidence = {e['id']: e for e in DATA['evidence']}
+    for row in rows:
+        assert row['status'] in {'outside-cohort', 'pending-year', 'pending-ai', 'linked'}
+        assert row.get('evidenceIds')
+        if row['status'] == 'linked':
+            matches = [r for r in DATA.get('directoryRechecks', []) if r['companyId'] == row.get('companyId')]
+            assert len(matches) == 1 and matches[0]['originalStatus'] == 'year-outside-or-missing'
+            assert all(eid in evidence and evidence[eid]['companyId'] == row['companyId'] for eid in row['evidenceIds'])
+        else:
+            assert not row.get('companyId')
+            assert all(eid in evidence and evidence[eid]['companyId'] is None for eid in row['evidenceIds'])
+    counts = Counter(row['status'] for row in rows)
+    return ['', '## 原缺少成立年名单的后续覆盖', '',
+        f"原快照缺年{len(original)}项，外部补核已有{len(rows)}项：{counts['linked']}项关联公司、{counts['outside-cohort']}项既有主体/业务范围外、{counts['pending-year']}项成立年待核、{counts['pending-ai']}项AI主营待核，{len(original)-len(rows)}项尚无本系列外部补核记录。", '',
+        '当前YC缺年字段复读与外部主体核查分别统计。登记创建日、公司自述年、产品发布、条款更新和批次不相互替代；已查英国或法国主体不代表完整集团沿革已核。原54项发现计数不回写，也不以Inactive补零收入。', '']
+
+
 def directory_rechecks():
     rows = DATA.get('directoryRechecks', [])
     if not rows:
@@ -167,6 +316,11 @@ def render():
         'year-outside-or-missing': cov['otherOrMissingYear'],
         'ai-unclear': cov['unclearAI'], 'date-conflict': cov['dateConflicts']}
     assert len({x['ycId'] for x in AUDIT['records']}) == len(AUDIT['records'])
+    excluded_years = Counter(x['foundedYear'] for x in AUDIT['records'] if x['status'] == 'year-outside-or-missing')
+    outside_years = {year: count for year, count in excluded_years.items() if year is not None}
+    assert all(year not in (2025, 2026) for year in outside_years)
+    assert sum(outside_years.values()) + excluded_years[None] == cov['otherOrMissingYear']
+    year_detail = '、'.join(f'{year}年{count}项' for year, count in sorted(outside_years.items()))
     lines = ['# AI 公司资料覆盖与缺项', '',
         f"截至 {DATA['asOf']}，研究表含{len(companies):,}条公司或产品记录。YC候选池有逐条读取审计；其他来源为定向补充。清单尚不能作为全球AI公司普查，也不能将收入未知解释为零。", '',
         '验收范围已于2026-10-30确认为：以确认的公开来源清单为边界，逐条核对并标明缺项。下方区分已逐条审计的候选池与定向补充来源；已使用某来源不代表该来源已被完整覆盖。', '',
@@ -178,7 +332,8 @@ def render():
         f"| 初筛匹配 | {cov['matchingProfiles']:,} | 成立年及AI相关描述通过初筛，尚非逐家商业验证 |",
         f"| 与原表重合 | {cov['existingProfiles']} | URL匹配，未再次新增 |",
         f"| 新增目录记录 | {cov['newProfiles']:,} | 原始入表来源，后续深化不改变发现计数 |",
-        f"| 年份不符或缺失 | {cov['otherOrMissingYear']} | 此轮不作为成立年匹配项 |",
+        f"| 原目录列为范围外年份 | {sum(outside_years.values())} | {year_detail}；原快照自述，不是独立登记核验 |",
+        f"| 原目录未给成立年 | {excluded_years[None]} | 与已列范围外年份分开，仍待补证 |",
         f"| AI业务待确认 | {cov['unclearAI']} | 不强行把背景关键词当主营 |",
         f"| 成立年与批次冲突 | {cov['dateConflicts']} | 另保留核查记录 |", '',
         f"全表由{depth}条目录基础项和{len(companies)-depth}条个案或平台记录组成。候选池已匹配项之外另有{len(companies)-cov['matchingProfiles']}条记录，包含其他来源及发布/转型对照；其中已有{promoted}条原目录记录深化为个案，仍保留目录来源。两种统计边界不同。候选池数字只是该镜像快照的覆盖，Bud等后续定向补充不回填为原候选池已覆盖。", '',
@@ -191,8 +346,8 @@ def render():
         f"| 其他目录基础项 | {depth-reviewed} | 规则初分或资料不足，继续待复核 |",
         f"| 有团队数字 | {sum(c['team'] is not None for c in companies)} | 多为自报Team Size或Employees，时点与口径不统一 |",
         f"| 专项技术证据记录 | {len(companies)-technical['unreviewed']} | 其中{technical['description']}条自述、{technical['public-artifact']}条公开材料、{technical['independent-test']}条第三方评测 |",
-        f"| 公司披露收入线索 | {income['self']} | 含历史、下界、币种不明及产品业务声明，不等于可比ARR |",
-        f"| 仅平台收入线索 | {income['platform']} | 同时有公司披露者计入上一行，不重复计数 |",
+        f"| 企业披露收入线索 | {income['self']} | 含公司或交易当事方披露、历史、下界及币种不明，不等于可比ARR |",
+        f"| 仅平台收入线索 | {income['platform']} | 同时有企业披露者计入上一行，不重复计数 |",
         f"| 仅第三方估算 | {income['estimate']} | 不与直接披露混合 |",
         f"| 暂无可用收入数字 | {income['unknown']} | 包含尚未逐家搜索的目录项，不代表没有收入 |", '',
         '## 业务分类与数据入口', '',
@@ -217,13 +372,16 @@ def render():
         lines.append(f"| {name} | {len(entries)} | {len({e['url'] for e in entries})} | {note} |")
     others = [e for e in DATA['evidence'] if urlsplit(e['url']).netloc not in included]
     lines.append(f"| 其他官网、评测及公开资料 | {len(others)} | {len({e['url'] for e in others})} | 按每条来源限制理解，不能统一当作独立证据 |")
+    lines += uncertain_ai_followups()
+    lines += founding_year_followups()
+    lines += missing_year_followups()
     lines += directory_rechecks()
     lines += source_register()
     lines += context_methods()
     lines += candidate_register()
     lines += ['', '## 尚未满足的研究条件', '',
         '行业ARR比较仍缺统一业务截止日、币种、经常性定义和业务边界。现有平台MRR可描述已采集产品，不足以代表全部新公司。技术评测只覆盖所列版本与任务，不宜跨领域汇总为统一强弱分数。', '',
-        '下一轮优先补收入线索的日期及合同定义、非YC和非英文来源、规则初分的人工复核，以及停止服务或被收购案例。这样能减少“只见赢家”和“只见融资公司”的偏差。公开来源池之外的未披露公司仍无法证明已被穷尽。', '',
+        '下一轮优先补收入线索的日期及合同定义、成立年冲突、AI主营仍待核项、非YC和非英文来源，以及停止服务或被收购案例。这样能减少“只见赢家”和“只见融资公司”的偏差。公开来源池之外的未披露公司仍无法证明已被穷尽。', '',
     ]
     return '\n'.join(lines)
 

scripts/documents-client.py

@@ -116,6 +116,17 @@ def main():
     mode.add_argument('--revision', type=int)
     reading.add_argument('--limit', type=int, default=50)
     reading.add_argument('--offset', type=int, default=0)
+    sections = sub.add_parser('outline', help='Read chapter titles and internal summaries without bodies')
+    sections.add_argument('slug')
+    sections.add_argument('--revision', type=int)
+    section = sub.add_parser('sections', help='Read selected chapter bodies')
+    section.add_argument('slug')
+    section.add_argument('ids', nargs='+')
+    section.add_argument('--revision', type=int)
+    editing = sub.add_parser('edit-sections', help='Atomically apply chapter operations from private JSON')
+    editing.add_argument('slug')
+    editing.add_argument('file')
+    editing.add_argument('--request-id')
     saving = sub.add_parser('save')
     saving.add_argument('file', help='Private JSON file or - for stdin; one document or {documents: [...]}')
     saving.add_argument('--request-id', help='Reuse this id only with the same input when retrying')
@@ -135,6 +146,24 @@ def main():
             elif args.revision is not None:
                 path += '?' + urllib.parse.urlencode({'revision': args.revision})
             result = request(base, token, 'GET', path)
+        elif args.command in ('outline', 'sections', 'edit-sections'):
+            path = '/api/documents/sections/' + urllib.parse.quote(args.slug, safe='/')
+            if args.command == 'edit-sections':
+                payload = json.load(sys.stdin) if args.file == '-' else json.loads(Path(args.file).read_text())
+                request_id = args.request_id or str(uuid.uuid4())
+                if not request_id or len(request_id) > 100 or any(c not in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789._:-' for c in request_id):
+                    raise ValueError('Invalid request id')
+                print('Request id: ' + request_id, file=sys.stderr)
+                result = request(base, token, 'POST', path, payload, request_id)
+            else:
+                query = []
+                if args.revision is not None:
+                    query.append(('revision', args.revision))
+                if args.command == 'sections':
+                    query.extend(('section', section_id) for section_id in args.ids)
+                if query:
+                    path += '?' + urllib.parse.urlencode(query)
+                result = request(base, token, 'GET', path)
         else:
             payload = json.load(sys.stdin) if args.file == '-' else json.loads(Path(args.file).read_text())
             request_id = args.request_id or str(uuid.uuid4())

scripts/summarize-ai-income.py

@@ -89,23 +89,42 @@ def link(cid):
 
 def render():
     points, excluded = sample(7)
+    grouped = groups(points)
+    described = [(key, stats(values)) for key, values in sorted(grouped.items())]
+    sizes = [value['n'] for _, value in described]
+    eligible = [(key, value) for key, value in described if value['n'] >= 3]
+    interpretation = '各组均不足3个样本，暂不比较分组离散度或收入高低。'
+    if eligible:
+        highest_key, highest = max(eligible, key=lambda item: item[1]['mean'])
+        sensitive_key, sensitive = max(eligible, key=lambda item: item[1]['largestRemovalDrop'] or 0)
+        cvs = [value['cv'] for _, value in eligible if value['cv'] is not None]
+        cv_text = f"CV范围为{min(cvs):.3f}—{max(cvs):.3f}。" if cvs else '各组CV均不可计算。'
+        interpretation = (
+            f"在至少3个样本的组内，当前均值最高的是{CATEGORIES[highest_key[0]]} · {highest_key[2]}"
+            f"（{highest_key[1]}，{money(highest['mean'])}美元每月）。{cv_text}"
+            f"剔除最大值后均值下降最多的是{CATEGORIES[sensitive_key[0]]} · {sensitive_key[2]}"
+            f"（{sensitive_key[1]}，下降{sensitive['largestRemovalDrop'] or 0:.1%}）。"
+            '这些只是当前采集点的描述，不据微小CV差异宣称某领域更稳定。'
+        )
+    size_text = f"各组目前只有{min(sizes)}至{max(sizes)}个产品" if sizes else '当前没有可比较的产品'
     lines = [
         '# AI 公司收入样本分布快照', '',
-        f"截至 {DATA['asOf']}，7日窗口内有 {len(points)} 个产品的美元平台MRR可纳入分组描述。创意软件订阅组的样本均值较高、CV也较大；营销软件组与消费者个人应用组的CV接近。各组只有1至5个产品，尚不足以估计行业总体或造假概率。", '',
+        f"截至 {DATA['asOf']}，7日窗口内有 {len(points)} 个产品的美元平台MRR可纳入分组描述。按业务、支付平台和收费模式分别计算均值、中位数与离散度。{size_text}，尚不足以估计行业总体或造假概率。", '',
         f"这是已采集资料的固定快照。[动态工作台]({BASE})提供筛选与原始证据。窗口按最后同步日计算，不保证所有点同日，也不是{len(DATA['companies']):,}条公司/产品记录的收入普查。", '',
         '## 7日窗口的分组结果', '',
         'MRR单位为美元每月。CV为样本标准差除以均值；最大值剔除降幅表示移除最高收入产品后均值下降的比例。单个样本不计算离散度。', '',
         '| 业务及收费模式 | 平台 | 样本数 | 均值 | 中位数 | CV | 最大值剔除降幅 |',
         '| --- | --- | --- | --- | --- | --- | --- |',
     ]
-    for key, values in sorted(groups(points).items()):
+    for key, values in sorted(grouped.items()):
         cat, provider, model = key
         v = stats(values)
         cv = f"{v['cv']:.3f}" if v['cv'] is not None else '不计算'
         drop = f"{v['largestRemovalDrop']:.1%}" if v['largestRemovalDrop'] is not None else '不计算'
         lines.append(f"| {CATEGORIES[cat]} · {model} | {provider} | {v['n']} | {money(v['mean'])} | {money(v['median'])} | {cv} | {drop} |")
     lines += ['',
-        '在至少3个样本的组内，内容创意软件订阅的均值较高，但移除最大样本后的下降也最大。营销软件与个人应用的CV仅有微小差别，不据此宣称哪个领域更稳定。两个内容类单样本组虽有更高数值，不能作为典型收入或领域排名。', '',
+        interpretation, '',
+        '单个样本不计算离散度，也不能作为典型收入或领域排名；不同收费模式和支付平台的分组保留各自含义。', '',
         '均值和中位数接近只描述当前采集点，并不证明样本具有代表性。平台公开者、成立年、规模、获客渠道与收费方式均可能影响结果。', '',
         '## 样本与证据入口', '',
         '点击产品名称可打开公司明细，再查看平台记录及其限制。连接已过期的近期快照仍按所标日期保留，并不表示连接当前有效。', '',

scripts/test_documents_client.py

@@ -4,6 +4,7 @@ import io
 import json
 from pathlib import Path
 import tempfile
+import sys
 import unittest
 from unittest.mock import patch
 import urllib.error
@@ -15,6 +16,30 @@ spec.loader.exec_module(client)
 
 
 class ClientTests(unittest.TestCase):
+    def test_section_reads_select_bodies_and_revision_without_full_document_fetch(self):
+        argv = ['client', '--url', 'https://docs.example', '--key-file', '/private/key',
+                'sections', 'endor/example', 'preamble', 's_one', '--revision', '3']
+        with patch.object(sys, 'argv', argv), patch.object(client, 'read_token', return_value='test-token'), \
+                patch.object(client, 'request', return_value={'revision': 3, 'sections': []}) as request, \
+                patch.object(sys, 'stdout', io.StringIO()):
+            client.main()
+        request.assert_called_once_with('https://docs.example', 'test-token', 'GET',
+                                        '/api/documents/sections/endor/example?revision=3&section=preamble&section=s_one')
+
+    def test_section_edit_keeps_payload_and_retry_id(self):
+        payload = {'revision': 3, 'operations': [{'op': 'update', 'id': 's_one', 'summary': 'Internal summary'}]}
+        with tempfile.TemporaryDirectory() as directory:
+            path = Path(directory) / 'edit.json'
+            path.write_text(json.dumps(payload))
+            argv = ['client', '--url', 'https://docs.example', '--key-file', '/private/key',
+                    'edit-sections', 'endor/example', str(path), '--request-id', 'edit-1']
+            with patch.object(sys, 'argv', argv), patch.object(client, 'read_token', return_value='test-token'), \
+                    patch.object(client, 'request', return_value={'revision': 4}) as request, \
+                    patch.object(sys, 'stdout', io.StringIO()), patch.object(sys, 'stderr', io.StringIO()):
+                client.main()
+            request.assert_called_once_with('https://docs.example', 'test-token', 'POST',
+                                            '/api/documents/sections/endor/example', payload, 'edit-1')
+
     def test_only_tls_or_loopback_origins_are_accepted(self):
         self.assertEqual(client.server_url('https://docs.example/'), 'https://docs.example')
         self.assertEqual(client.server_url('http://127.0.0.1:18135'), 'http://127.0.0.1:18135')