feat: add paired DeepSeek routing length control

2026-07-29 16:17:18 +08:00
parent 059458f8e3
commit 9ca08501a8
15 changed files with 318874 additions and 69 deletions
@@ -5,6 +5,11 @@ import rawAbsorb from "@/data/deepseek-v2-lite-absorb.json";
 import rawAbsorbRepro from "@/data/deepseek-v2-lite-absorb-repro.json";
 import rawCorpus from "@/data/deepseek-v2-lite-routing-corpus.json";
 import rawCorpusRepro from "@/data/deepseek-v2-lite-routing-corpus-repro.json";
+import rawMatched16 from "@/data/deepseek-v2-lite-routing-matched16.json";
+import rawMatched16Repro from "@/data/deepseek-v2-lite-routing-matched16-repro.json";
+import rawMatched24 from "@/data/deepseek-v2-lite-routing-matched24.json";
+import rawMatched24Repro from "@/data/deepseek-v2-lite-routing-matched24-repro.json";
+import rawLengthSensitivity from "@/data/deepseek-v2-lite-routing-length-sensitivity.json";

 const trace = rawTrace as any;
 const repro = rawRepro as any;
@@ -12,8 +17,15 @@ const absorb = rawAbsorb as any;
 const absorbRepro = rawAbsorbRepro as any;
 const corpus = rawCorpus as any;
 const corpusRepro = rawCorpusRepro as any;
+const matched16 = rawMatched16 as any;
+const matched16Repro = rawMatched16Repro as any;
+const matched24 = rawMatched24 as any;
+const matched24Repro = rawMatched24Repro as any;
+const lengthSensitivity = rawLengthSensitivity as any;
 const absorbExact = JSON.stringify(absorb) === JSON.stringify(absorbRepro);
 const corpusExact = JSON.stringify(corpus) === JSON.stringify(corpusRepro);
+const matched16Exact = JSON.stringify(matched16) === JSON.stringify(matched16Repro);
+const matched24Exact = JSON.stringify(matched24) === JSON.stringify(matched24Repro);
 const bytes = (value: number) => value >= 1024
  ? `${(value / 1024).toFixed(2)} KiB`
  : `${value.toLocaleString()} B`;
@@ -49,17 +61,25 @@ const compact = {
  },
 };
 const compactJson = JSON.stringify(compact).replaceAll("<", "\\u003c");
-const corpusCompact = {
-  domains: corpus.corpus_contract.domains,
-  labels: corpus.corpus_contract.domain_labels,
-  counts: corpus.corpus_contract.counts,
-  inference: corpus.inference_contract,
-  statistics: corpus.statistical_contract,
-  layers: corpus.layers.slice(1).map((layer: any) => ({
+const compactCorpus = (input: any) => ({
+  domains: input.corpus_contract.domains,
+  labels: input.corpus_contract.domain_labels,
+  counts: input.corpus_contract.counts,
+  inference: input.inference_contract,
+  statistics: input.statistical_contract,
+  layers: input.layers.slice(1).map((layer: any) => ({
    layer: layer.layer,
    routes: layer.routes,
    modes: layer.statistics,
  })),
+});
+const corpusCompact = {
+  cohorts: {
+    natural: compactCorpus(corpus),
+    matched16: compactCorpus(matched16),
+    matched24: compactCorpus(matched24),
+  },
+  lengthSensitivity,
 };
 const corpusCompactJson = JSON.stringify(corpusCompact).replaceAll("<", "\\u003c");
 const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoint_tensor_bytes;
@@ -73,7 +93,7 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
    </div>
    <p>
      固定官方 revision、tokenizer、模型代码和 BF16 第一分片；RTX 5090 连续执行 layer 0–6，
-      从 3,240 次 token 显微轨迹扩到 304,560 次公开语料路由，并让 layer-1 权重继续走入官方吸收式 cache。
+      从 3,240 次 token 显微轨迹扩到 488,880 次公开语料路由，并让 layer-1 权重继续走入官方吸收式 cache。
      所有结论都带证据身份与停止线。
    </p>
  </header>
@@ -390,22 +410,35 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi

  <section class="artifact-panel" data-artifact-panel="corpus" hidden>
    <div class="panel-lead">
-      <div><span>X + S / FIXED PUBLIC CORPUS</span><h4>从四条示例，走到 128 条可重建样本与区间</h4></div>
+      <div><span>X + S / FIXED PUBLIC CORPUS</span><h4>从自然长度，再走到同 prompt 的 16 / 24-token 对照</h4></div>
      <p>
-        WikiText-2、TNEWS、HumanEval、GSM8K 各取 32 条；固定哈希选样、最多 96 tokens。
-        每个区间都重采样 prompt，而不是把同一 prompt 里的 token 假装成独立样本。
+        三个 cohort 都来自 WikiText-2、TNEWS、HumanEval、GSM8K；等长两档使用完全相同的
+        128 条源 prompt 和嵌套前缀。每个区间都重采样 prompt，不把相关 token 假装成独立样本。
      </p>
    </div>

    <div class="corpus-ledger">
-      <article><span>PROMPTS</span><b>{corpus.inference_contract.total_prompts}</b><p>4 domains × 32</p></article>
-      <article><span>VALID TOKENS</span><b>{corpus.inference_contract.valid_tokens.toLocaleString()}</b><p>答案未使用，代码未执行</p></article>
-      <article><span>REAL ROUTES</span><b>{(corpus.inference_contract.routes_per_moe_layer * 6).toLocaleString()}</b><p>50,760 / layer × 6 MoE layers</p></article>
+      <article><span>COHORTS</span><b>3</b><p>natural ≤96 · matched 16 / 24</p></article>
+      <article><span>PROMPT RUNS</span><b>384</b><p>每档 4 domains × 32</p></article>
+      <article><span>VALID TOKENS</span><b>13,580</b><p>答案未使用，代码未执行</p></article>
+      <article><span>REAL ROUTES</span><b>488,880</b><p>三档 × 前六个 MoE 层</p></article>
      <article><span>BOOTSTRAP</span><b>{corpus.statistical_contract.replicates.toLocaleString()}</b><p>prompt-level / domain-stratified</p></article>
-      <article class="exact"><span>INDEPENDENT RERUN</span><b>{corpusExact ? "BYTE-EXACT" : "MISMATCH"}</b><p>SHA-256 4678a1d1…a09e4</p></article>
+      <article class="exact">
+        <span>INDEPENDENT RERUN</span>
+        <b>{corpusExact && matched16Exact && matched24Exact ? "3 / 3 EXACT" : "MISMATCH"}</b>
+        <p>自然长度与两个等长 cohort</p>
+      </article>
    </div>

    <div class="corpus-controls">
+      <div>
+        <span>COHORT</span>
+        <div class="corpus-cohort-switch" role="group" aria-label="选择公开语料长度 cohort">
+          <button type="button" data-corpus-cohort="natural" aria-pressed="true">自然 ≤96</button>
+          <button type="button" data-corpus-cohort="matched16" aria-pressed="false">同样本 16</button>
+          <button type="button" data-corpus-cohort="matched24" aria-pressed="false">同样本 24</button>
+        </div>
+      </div>
      <div>
        <span>MOE LAYER</span>
        <div class="layer-switch corpus-layer-switch" role="group" aria-label="选择公开语料路由层">
@@ -422,7 +455,7 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
        </div>
      </div>
      <p data-corpus-mode-note>
-        先把每条 prompt 的 64 维路由分布归一，再平均；短中文标题与长代码 prompt 各有一票。
+        自然长度 cohort：每条 prompt 先归一再等权；它保留来源长度差异，适合描述实际选中样本。
      </p>
    </div>

@@ -456,7 +489,38 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
    <div class="corpus-findings">
      <article><span>CURRENT HIGHEST CV</span><b data-corpus-highest-cv></b><p data-corpus-highest-cv-ci></p></article>
      <article><span>CURRENT LARGEST JSD</span><b data-corpus-largest-jsd></b><p data-corpus-largest-jsd-ci></p></article>
-      <article><span>WHAT CHANGED</span><b>区间替代单点印象</b><p>看到“不同”之后，继续问 prompt 换一批时波动多大。</p></article>
+      <article><span>CURRENT COHORT</span><b data-corpus-cohort-title>自然长度 ≤96</b><p data-corpus-cohort-boundary>四域 token 总量不同；不能把差异全归因于内容。</p></article>
+    </div>
+
+    <div class="length-sensitivity">
+      <div class="length-sensitivity-head">
+        <div>
+          <span>PAIRED LENGTH SENSITIVITY / SAME SOURCE PROMPTS</span>
+          <h5>同一条 prompt：16 → 24 tokens，CV 怎样变化？</h5>
+        </div>
+        <p>
+          short / long 每次 bootstrap 使用同一组 prompt indices；下方 Δ = CV24 − CV16。
+          负值表示读入后续 8 tokens 后，64-expert 分布更平。
+        </p>
+      </div>
+      <div class="length-delta-grid" data-length-delta-grid></div>
+      <div class="length-pair-summary">
+        <article>
+          <span>中文新闻 ↔ 代码 / JSD</span>
+          <b data-length-jsd></b>
+          <p data-length-jsd-ci></p>
+        </article>
+        <article>
+          <span>LARGEST |CV Δ|</span>
+          <b data-length-largest></b>
+          <p data-length-largest-ci></p>
+        </article>
+        <article>
+          <span>READING</span>
+          <b>长度影响存在，但没有抹掉域差异</b>
+          <p>六层中文↔代码 JSD 都下降，24-token 下仍保持非零经验距离。</p>
+        </article>
+      </div>
    </div>

    <div class="evidence-links">
@@ -469,7 +533,8 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
    <div class="artifact-boundary">
      <b>DESCRIPTIVE, NOT SEMANTICS</b>
      <p>
-        这是固定 128 条公开 prompt 上的前六个 MoE 层，不是训练分布或线上流量。
+        这是三个固定公开 cohort 上的前六个 MoE 层，不是训练分布或线上流量；matched cohort
+        只代表各域至少 24 tokens 的子群。
        bootstrap 区间描述本探针换 prompt 的稳定性，不是零差异假设检验；没有多重比较校正，
        也不能把 E29、E48 等参数索引命名成“中文专家”或“代码专家”。
      </p>
@@ -566,7 +631,8 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
    <code>experiments/deepseek/v2_lite_trace.py</code> ·
    <code>experiments/deepseek/v2_lite_absorb_probe.py</code> ·
    <code>experiments/deepseek/v2_lite_routing_corpus.py</code> ·
-    <code>research/DEEPSEEK_ROUTING_CORPUS_AUDIT.md</code>
+    <code>experiments/deepseek/compare_routing_length_control.py</code> ·
+    <code>research/DEEPSEEK_ROUTING_LENGTH_CONTROL_AUDIT.md</code>
  </figcaption>

  <script is:inline type="application/json" data-dsv2-trace set:html={compactJson}></script>
@@ -779,13 +845,34 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
      code: "Python 代码",
      math: "小学数学",
    };
-    const corpusLayer = (layer: number) => corpusData.layers.find((item: any) => item.layer === layer);
+    const cohortMeta: Record<string, { title: string; boundary: string }> = {
+      natural: {
+        title: "自然长度 ≤96",
+        boundary: "四域 token 总量不同；不能把差异全归因于内容。",
+      },
+      matched16: {
+        title: "同样本 · 16 tokens",
+        boundary: "只读取至少 24-token 固定 cohort 的前 16 tokens。",
+      },
+      matched24: {
+        title: "同样本 · 24 tokens",
+        boundary: "与 16-token 档源 prompt 完全相同，只增加后续 8 tokens。",
+      },
+    };
+    let corpusCohort = "natural";
    let corpusLayerNumber = 1;
    let corpusMode = "prompt_balanced";
    const formatCi = (ci: number[], digits = 3) => `[${ci[0].toFixed(digits)}, ${ci[1].toFixed(digits)}]`;
+    const signed = (value: number, digits = 3) => `${value >= 0 ? "+" : ""}${value.toFixed(digits)}`;
+    const formatSignedCi = (ci: number[], digits = 3) =>
+      `[${signed(ci[0], digits)}, ${signed(ci[1], digits)}]`;
    const renderCorpus = () => {
-      const layer = corpusLayer(corpusLayerNumber);
+      const cohort = corpusData.cohorts[corpusCohort];
+      const layer = cohort.layers.find((item: any) => item.layer === corpusLayerNumber);
      const mode = layer.modes[corpusMode];
+      all<HTMLButtonElement>("[data-corpus-cohort]").forEach((button) => {
+        button.setAttribute("aria-pressed", String(button.dataset.corpusCohort === corpusCohort));
+      });
      all<HTMLButtonElement>("[data-corpus-layer]").forEach((button) => {
        button.classList.toggle("active", Number(button.dataset.corpusLayer) === corpusLayerNumber);
      });
@@ -794,18 +881,24 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
      });
      set(
        "[data-corpus-mode-note]",
-        corpusMode === "prompt_balanced"
-          ? "先把每条 prompt 的 64 维路由分布归一，再平均；短中文标题与长代码 prompt 各有一票。"
-          : "直接汇总全部 token 的路由计数；长 prompt 权重更高，适合回答“本批 token 实际流向哪里”。",
+        corpusCohort === "natural"
+          ? corpusMode === "prompt_balanced"
+            ? "自然长度 cohort：每条 prompt 先归一再等权；它保留来源长度差异，适合描述实际选中样本。"
+            : "自然长度 cohort：直接汇总 token 路由；长 prompt 权重更高，适合回答本批 token 实际流向哪里。"
+          : corpusMode === "prompt_balanced"
+            ? "等长 cohort：每条 prompt 等权；16 / 24 两档使用同一批源样本，可以做 paired delta。"
+            : "等长 cohort 中每条 prompt 的 token 数相同，因此 token 加权与 prompt 等权理论上重合。",
      );
      set(
        "[data-corpus-heat-title]",
-        `layer ${corpusLayerNumber} · ${corpusMode === "prompt_balanced" ? "每条 prompt 等权" : "按 token 加权"}`,
+        `${cohortMeta[corpusCohort].title} · layer ${corpusLayerNumber} · ${corpusMode === "prompt_balanced" ? "prompt 等权" : "token 加权"}`,
      );
+      set("[data-corpus-cohort-title]", cohortMeta[corpusCohort].title);
+      set("[data-corpus-cohort-boundary]", cohortMeta[corpusCohort].boundary);

      const rows = one<HTMLElement>("[data-corpus-domain-rows]");
      if (rows) {
-        rows.replaceChildren(...corpusData.domains.map((domain: string) => {
+        rows.replaceChildren(...cohort.domains.map((domain: string) => {
          const result = mode.domains[domain];
          const metrics = result.metrics;
          const top = result.top_experts[0];
@@ -816,7 +909,7 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
          const identityLabel = document.createElement("b");
          const identityMeta = document.createElement("small");
          identityLabel.textContent = corpusLabels[domain];
-          identityMeta.textContent = `32 prompts · ${corpusData.counts[domain].valid_tokens.toLocaleString()} tokens`;
+          identityMeta.textContent = `32 prompts · ${cohort.counts[domain].valid_tokens.toLocaleString()} tokens`;
          identity.append(identityLabel, identityMeta);

          const cv = document.createElement("span");
@@ -846,7 +939,7 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi

      const heatmap = one<HTMLElement>("[data-corpus-heatmap]");
      if (heatmap) {
-        heatmap.replaceChildren(...corpusData.domains.map((domain: string) => {
+        heatmap.replaceChildren(...cohort.domains.map((domain: string) => {
          const result = mode.domains[domain];
          const max = Math.max(...result.distribution);
          const row = document.createElement("div");
@@ -874,16 +967,16 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
        const corner = document.createElement("b");
        corner.textContent = "DOMAIN";
        cells.push(corner);
-        corpusData.domains.forEach((domain: string) => {
+        cohort.domains.forEach((domain: string) => {
          const header = document.createElement("b");
          header.textContent = corpusLabels[domain];
          cells.push(header);
        });
-        corpusData.domains.forEach((left: string) => {
+        cohort.domains.forEach((left: string) => {
          const header = document.createElement("b");
          header.textContent = corpusLabels[left];
          cells.push(header);
-          corpusData.domains.forEach((right: string) => {
+          cohort.domains.forEach((right: string) => {
            const cell = document.createElement("span");
            if (left === right) {
              cell.className = "diagonal";
@@ -902,7 +995,7 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
        jsd.replaceChildren(...cells);
      }

-      const highest = corpusData.domains
+      const highest = cohort.domains
        .map((domain: string) => ({ domain, value: mode.domains[domain].metrics.cv }))
        .sort((left: any, right: any) => right.value.point - left.value.point)[0];
      const largest = [...mode.pairs]
@@ -925,7 +1018,63 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
        "[data-corpus-largest-jsd-ci]",
        `95% CI ${formatCi(largest.js_divergence.ci95)} · nats`,
      );
+
+      const comparisonLayer = corpusData.lengthSensitivity.layers
+        .find((item: any) => item.layer === corpusLayerNumber);
+      const comparison = comparisonLayer.modes[corpusMode];
+      const deltaGrid = one<HTMLElement>("[data-length-delta-grid]");
+      if (deltaGrid) {
+        deltaGrid.replaceChildren(...cohort.domains.map((domain: string) => {
+          const result = comparison.domains[domain];
+          const cv = result.metrics.cv;
+          const card = document.createElement("article");
+          const label = document.createElement("span");
+          const values = document.createElement("b");
+          const delta = document.createElement("strong");
+          const detail = document.createElement("p");
+          label.textContent = corpusLabels[domain];
+          values.textContent = `${cv.short.toFixed(3)} → ${cv.long.toFixed(3)}`;
+          delta.textContent = `Δ ${signed(cv.delta_long_minus_short)}`;
+          delta.className = cv.delta_long_minus_short <= 0 ? "down" : "up";
+          detail.textContent = `paired 95% ${formatSignedCi(cv.delta_ci95)} · TV ${result.total_variation.point.toFixed(3)}`;
+          card.append(label, values, delta, detail);
+          return card;
+        }));
+      }
+      const chineseCode = comparison.pairs.find((pair: any) =>
+        pair.left === "chinese" && pair.right === "code"
+      ).js_divergence;
+      set(
+        "[data-length-jsd]",
+        `${chineseCode.short.toFixed(3)} → ${chineseCode.long.toFixed(3)} · Δ ${signed(chineseCode.delta_long_minus_short)}`,
+      );
+      set(
+        "[data-length-jsd-ci]",
+        `paired 95% ${formatSignedCi(chineseCode.delta_ci95)} · L${corpusLayerNumber}`,
+      );
+      const largestDelta = cohort.domains
+        .map((domain: string) => ({
+          domain,
+          value: comparison.domains[domain].metrics.cv,
+        }))
+        .sort((left: any, right: any) =>
+          Math.abs(right.value.delta_long_minus_short) - Math.abs(left.value.delta_long_minus_short)
+        )[0];
+      set(
+        "[data-length-largest]",
+        `${corpusLabels[largestDelta.domain]} · Δ ${signed(largestDelta.value.delta_long_minus_short)}`,
+      );
+      set(
+        "[data-length-largest-ci]",
+        `paired 95% ${formatSignedCi(largestDelta.value.delta_ci95)} · CV24 − CV16`,
+      );
    };
+    all<HTMLButtonElement>("[data-corpus-cohort]").forEach((button) => {
+      button.addEventListener("click", () => {
+        corpusCohort = button.dataset.corpusCohort ?? "natural";
+        renderCorpus();
+      });
+    });
    all<HTMLButtonElement>("[data-corpus-layer]").forEach((button) => {
      button.addEventListener("click", () => {
        corpusLayerNumber = Number(button.dataset.corpusLayer);
@@ -987,7 +1136,8 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
  .corpus-controls > div > span,
  .corpus-heat-head span,
  .corpus-comparison span,
-  .corpus-findings span {
+  .corpus-findings span,
+  .length-sensitivity span {
    margin: 0;
    color: var(--blue);
    font: 700 .69rem/1.3 var(--font-mono);
@@ -1339,7 +1489,7 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
  .load-lessons b { display: block; margin-top: .35rem; font-size: .77rem; }
  .corpus-ledger {
    display: grid;
-    grid-template-columns: repeat(5, 1fr);
+    grid-template-columns: repeat(6, 1fr);
    border: 1px solid rgba(32,32,39,.14);
  }
  .corpus-ledger article {
@@ -1361,7 +1511,7 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
  }
  .corpus-controls {
    display: grid;
-    grid-template-columns: auto 1.1fr minmax(15rem, 1.3fr);
+    grid-template-columns: 1.2fr auto 1fr;
    gap: 1rem;
    align-items: end;
    margin-top: .8rem;
@@ -1371,8 +1521,10 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
  }
  .corpus-controls > div { display: grid; gap: .45rem; }
  .corpus-controls .layer-switch { margin: 0; }
-  .corpus-mode-switch { display: flex; }
-  .corpus-mode-switch button {
+  .corpus-mode-switch,
+  .corpus-cohort-switch { display: flex; }
+  .corpus-mode-switch button,
+  .corpus-cohort-switch button {
    padding: .58rem .75rem;
    border: 1px solid rgba(32,32,39,.22);
    background: #fffdf8;
@@ -1380,14 +1532,19 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
    font: 650 .67rem/1 var(--font-mono);
    cursor: pointer;
  }
-  .corpus-mode-switch button + button { border-left: 0; }
-  .corpus-mode-switch button[aria-pressed="true"] {
+  .corpus-mode-switch button + button,
+  .corpus-cohort-switch button + button { border-left: 0; }
+  .corpus-mode-switch button[aria-pressed="true"],
+  .corpus-cohort-switch button[aria-pressed="true"] {
    border-color: var(--blue);
    background: var(--blue);
    color: white;
  }
  .corpus-controls > p {
+    grid-column: 1 / -1;
    margin: 0;
+    padding-top: .75rem;
+    border-top: 1px solid rgba(32,32,39,.12);
    color: rgba(32,32,39,.62);
    font-size: .69rem;
    line-height: 1.5;
@@ -1543,6 +1700,93 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
    font-size: .65rem;
    line-height: 1.45;
  }
+  .length-sensitivity {
+    margin-top: .8rem;
+    padding: 1rem;
+    border: 1px solid rgba(32,32,39,.15);
+    background:
+      linear-gradient(120deg, rgba(57,120,110,.11), transparent 42%),
+      #e8e2d7;
+  }
+  .length-sensitivity-head {
+    display: grid;
+    grid-template-columns: 1fr 1.25fr;
+    gap: 1.2rem;
+    align-items: end;
+  }
+  .length-sensitivity h5 {
+    margin: .4rem 0 0;
+    font: 720 1.08rem/1.15 var(--font-display);
+  }
+  .length-sensitivity-head > p {
+    margin: 0;
+    color: rgba(32,32,39,.62);
+    font-size: .69rem;
+    line-height: 1.55;
+  }
+  .length-delta-grid {
+    display: grid;
+    grid-template-columns: repeat(4, 1fr);
+    margin-top: .8rem;
+    border: 1px solid rgba(32,32,39,.13);
+    background: #fffdf8;
+  }
+  .length-delta-grid > :global(article) {
+    padding: .8rem;
+    border-right: 1px solid rgba(32,32,39,.11);
+  }
+  .length-delta-grid > :global(article:last-child) { border-right: 0; }
+  .length-delta-grid > :global(article > span) {
+    display: block;
+    color: var(--blue);
+    font: 700 .62rem/1.2 var(--font-mono);
+  }
+  .length-delta-grid > :global(article > b) {
+    display: block;
+    margin-top: .45rem;
+    font: 730 .88rem/1.1 var(--font-mono);
+  }
+  .length-delta-grid > :global(article > strong) {
+    display: inline-block;
+    margin-top: .35rem;
+    padding: .22rem .35rem;
+    font: 750 .7rem/1 var(--font-mono);
+  }
+  .length-delta-grid > :global(article > strong.down) {
+    background: rgba(57,120,110,.12);
+    color: var(--teal);
+  }
+  .length-delta-grid > :global(article > strong.up) {
+    background: rgba(186,118,44,.12);
+    color: var(--amber);
+  }
+  .length-delta-grid > :global(article > p) {
+    margin: .4rem 0 0;
+    color: rgba(32,32,39,.54);
+    font: .59rem/1.4 var(--font-mono);
+  }
+  .length-pair-summary {
+    display: grid;
+    grid-template-columns: repeat(3, 1fr);
+    margin-top: .65rem;
+    border: 1px solid rgba(32,32,39,.13);
+  }
+  .length-pair-summary article {
+    padding: .8rem;
+    border-right: 1px solid rgba(32,32,39,.11);
+  }
+  .length-pair-summary article:last-child { border-right: 0; }
+  .length-pair-summary b {
+    display: block;
+    margin-top: .4rem;
+    font-size: .77rem;
+  }
+  .length-pair-summary p {
+    margin: .3rem 0 0;
+    color: rgba(32,32,39,.55);
+    font-size: .63rem;
+    line-height: 1.45;
+  }
  .observed-cache {
    display: grid;
    grid-template-columns: 1fr auto 1.25fr;
@@ -1777,7 +2021,8 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
    .kernel-contract,
    .corpus-controls,
    .corpus-heat-head,
-    .corpus-comparison { grid-template-columns: 1fr; }
+    .corpus-comparison,
+    .length-sensitivity-head { grid-template-columns: 1fr; }
    .artifact-status { grid-template-columns: 1fr 1fr; }
    .artifact-tabs { grid-template-columns: 1fr 1fr; }
    .route-controls { grid-template-columns: 1fr 1fr; }
@@ -1789,6 +2034,7 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
    .checksum-grid,
    .absorb-metrics { grid-template-columns: 1fr 1fr; }
    .corpus-ledger { grid-template-columns: repeat(3, 1fr); }
+    .length-delta-grid { grid-template-columns: 1fr 1fr; }
    .corpus-heat-head p { text-align: left; }
    .layer-evidence { grid-template-columns: repeat(9, 1fr); }
    .repro-gate { grid-template-columns: 1fr 1fr; }
@@ -1817,7 +2063,9 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
    .checksum-grid,
    .cache-ledger,
    .corpus-ledger,
-    .corpus-findings { grid-template-columns: 1fr; }
+    .corpus-findings,
+    .length-delta-grid,
+    .length-pair-summary { grid-template-columns: 1fr; }
    .route-metrics article,
    .cache-ratio article,
    .load-lessons article,
@@ -1826,8 +2074,12 @@ const shardFraction = trace.provenance.shard_1_bytes / trace.provenance.checkpoi
    .checksum-grid article { border-right: 0; border-bottom: 1px solid rgba(32,32,39,.12); }
    .corpus-ledger article,
    .corpus-findings article { border-right: 0; border-bottom: 1px solid rgba(32,32,39,.12); }
-    .corpus-mode-switch { display: grid; grid-template-columns: 1fr; }
-    .corpus-mode-switch button + button { border-left: 1px solid rgba(32,32,39,.22); border-top: 0; }
+    .corpus-mode-switch,
+    .corpus-cohort-switch { display: grid; grid-template-columns: 1fr; }
+    .corpus-mode-switch button + button,
+    .corpus-cohort-switch button + button { border-left: 1px solid rgba(32,32,39,.22); border-top: 0; }
+    .length-delta-grid > :global(article),
+    .length-pair-summary article { border-right: 0; border-bottom: 1px solid rgba(32,32,39,.11); }
    .artifact-boundary { grid-template-columns: 1fr; }
    .load-dials { grid-template-columns: 1fr; }
    .observed-cache,