docs(tutorials): fix 16 content errors from a full-collection per-tutorial audit (Codex-confirmed) (#28)

A holistic per-tutorial content audit (5 skeptical-expert subagents reading all 28
ZH tutorials, then Codex GPT-5.5 xhigh cross-model adjudication of every flag)
confirmed the collection's core math/derivations are correct, and surfaced 16 real
factual/citation/number errors — fixed here across both ZH and EN editions:

- moe: Qwen2-57B-A14B shared-expert 0 → 1 (it has a shared expert; only Qwen3 dropped them)
- video_generation: CogVideoX "5B/15B" → "2B/5B" (no 15B release)
- diffusion_distillation: iCT CIFAR-10 FID — 2.51/2.24 are iCT-DEEP; plain iCT is 2.83/2.46
- long_context: Differential Transformer "+50% params/compute" → matches FLOPs (halves heads)
- vae: Blau & Michaeli "ICML 2018" → CVPR 2018 (×2); Gumbel-Max §8.2 logits-notation made consistent
- kl: dropped the "Anthropic 1707.06347" misattribution (it's Schulman PPO); Gao-2023 PPO overopt fit d(α−γd)−δd^1.5 → published d(α−β·log d)
- reasoning: Math-Shepherd "MCTS" → Monte-Carlo completion rollouts (resolves self-contradiction; rStar-Math's real MCTS left alone)
- multi_agent: MoA venue NeurIPS 2024 → ICLR 2025; FrugalGPT "Yue" → Chen/Zaharia/Zou; MLE-bench 16.9% GPT-4o → o1-preview; Devin Mar-2024 13.9% on original SWE-bench (Verified launched 2024-08)
- self_evolving: Voyager "NeurIPS 2023" → TMLR
- flow_matching: Reflow venue "ICLR 2022" → ICLR 2023
- diffusion_foundations: separated ADD (SDXL-Turbo, DINOv2) from LADD (SD3-Turbo, teacher latent features, not DINOv2)
- rlhf: §9.2 memory table fixed to the correct 16 B/param = 112 GB/trainable model (~252 GB total; was an inconsistent ~170 GB)

Two flags were NOT applied because the cross-model check disagreed/couldn't confirm:
StarPO's acronym expansion (subagent vs Codex disagreed) and the exact MoA venue
(Codex UNSURE; kept the subagent's web-verified ICLR 2025 since the tutorial already
contradicted itself). ~12 low nits (units/stale frontier facts) left for later.

All 24 affected HTMLs re-rendered, 24 sidecars hash-refreshed + a post_ship_review note
(Codex threads 019f127e / 019f127f). Gate: tools/verify_reviews.py --mode strict
--reproduce → 57 OK / PASS.

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Author: wanshuiyin

docs/tutorials/diffusion_distillation_tutorial.html

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-    <span><strong>SHA256:</strong> <code>cdf8cbf0098f</code></span>
-    <span><strong>Rendered:</strong> 2026-06-16 06:15 UTC</span>
+    <span><strong>SHA256:</strong> <code>5f06105db9ff</code></span>
+    <span><strong>Rendered:</strong> 2026-06-29 10:11 UTC</span>
 
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@@ -669,7 +669,7 @@ <h3 id="23-ict--improved-techniques-song-dhariwal-2023-arxiv231014189">2.3　iCT
 <table><thead><tr><th>改动</th><th>原 CT</th><th>iCT</th></tr></thead><tbody><tr><td>Target</td><td>EMA $\theta^- = \tau \theta^- + (1-\tau) \theta$</td><td><strong>直接 stop-grad</strong>（不用 EMA）</td></tr><tr><td>Loss</td><td>LPIPS</td><td><strong>Pseudo-Huber</strong> $d(a, b) = \sqrt{\lVert a-b \rVert^2 + c^2} - c$</td></tr><tr><td>Noise sched</td><td>uniform discrete $\sigma_n$</td><td><strong>Lognormal</strong>：$\log \sigma \sim \mathcal{N}(P_\text{mean}, P_\text{std}^2)$</td></tr><tr><td>Step count</td><td>fixed $N$</td><td><strong>Curriculum</strong>：$N(k) = \lceil N_\min \cdot (N_\max/N_\min)^{k/K} \rceil$</td></tr></tbody></table>
 <p><strong>Pseudo-Huber 的设计动机</strong>：</p>
 <ul><li>LPIPS 引入对 ImageNet pretrained 特征的<strong>bias</strong>——eval 时 FID 看起来好，但实际 distribution shift</li><li>L2 对 outlier 敏感、训练不稳</li><li>Pseudo-Huber $\sqrt{\|a-b\|^2 + c^2} - c$：小残差时 ≈ $\|a-b\|^2/(2c)$（L2），大残差时 ≈ $\|a-b\|$（L1）——<strong>自适应 robust</strong></li></ul>
-<p><strong>结果</strong>：iCT 在 CIFAR-10 <strong>1-step FID 2.51 / 2-step FID 2.24</strong>（论文摘要数字），且<strong>不依赖 teacher</strong>——彻底打开 from-scratch consistency training 的天花板。</p>
+<p><strong>结果</strong>：iCT 在 CIFAR-10 <strong>1-step FID 2.83 / 2-step FID 2.46</strong>（论文摘要数字；更深的 iCT-deep 进一步到 2.51 / 2.24），且<strong>不依赖 teacher</strong>——彻底打开 from-scratch consistency training 的天花板。</p>
 <h3 id="24-scm--trigflow-lu-song-2024-openai-arxiv241011081">2.4　sCM / TrigFlow (Lu-Song 2024 OpenAI, arXiv:2410.11081)</h3>
 <p><strong>问题</strong>：离散时间 CM 有两大病——(i) 离散化误差（$N$ 越大越准但越慢）、(ii) 各种 hyper-parameter（noise schedule / EMA decay / loss curriculum）调起来很玄。</p>
 <p><strong>TrigFlow 参数化</strong>：把 forward path 写成三角形式——</p>
@@ -1607,8 +1607,8 @@ <h2 id="a-附录参考文献">§A 附录：参考文献</h2>
 <footer class="aris-footer">
   Generated by <a href="https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/blob/main/skills/render-html/SKILL.md">ARIS <code>/render-html</code></a> ·
   source path <code>docs/tutorials/diffusion_distillation_tutorial.md</code> ·
-  SHA256 <code>cdf8cbf0098f</code> ·
-  generated at 2026-06-16 06:15 UTC.
+  SHA256 <code>5f06105db9ff</code> ·
+  generated at 2026-06-29 10:11 UTC.
   This is a generated view — edit the source Markdown, then re-render.
 </footer>
 </main>

docs/tutorials/diffusion_distillation_tutorial.md

@@ -131,7 +131,7 @@ CT (Consistency Training) 原本质量远低于 CD。iCT 改进四件事：
 - L2 对 outlier 敏感、训练不稳
 - Pseudo-Huber $\sqrt{\|a-b\|^2 + c^2} - c$：小残差时 ≈ $\|a-b\|^2/(2c)$（L2），大残差时 ≈ $\|a-b\|$（L1）——**自适应 robust**
 
-**结果**：iCT 在 CIFAR-10 **1-step FID 2.51 / 2-step FID 2.24**（论文摘要数字），且**不依赖 teacher**——彻底打开 from-scratch consistency training 的天花板。
+**结果**：iCT 在 CIFAR-10 **1-step FID 2.83 / 2-step FID 2.46**（论文摘要数字；更深的 iCT-deep 进一步到 2.51 / 2.24），且**不依赖 teacher**——彻底打开 from-scratch consistency training 的天花板。
 
 ### 2.4　sCM / TrigFlow (Lu-Song 2024 OpenAI, arXiv:2410.11081)
 

docs/tutorials/diffusion_distillation_tutorial.review.json

@@ -1,7 +1,7 @@
 {
   "skill": "interview-cheatsheet",
   "source": "docs/tutorials/diffusion_distillation_tutorial.md",
-  "source_sha256": "cdf8cbf0098fd69e5a36cbd5b787d6415815106fdc3159de14661b50323e5b7c",
+  "source_sha256": "5f06105db9ffe0e923b0da9b1193e64c35d1994bc5749e984f20b214a09e30f9",
   "source_sha256_backfilled": "added 2026-05-31 post-hoc; source git-verified unchanged since review (2026-05-20); not a new codex review",
   "output": "docs/tutorials/diffusion_distillation_tutorial.html",
   "topic": "Diffusion / Flow Distillation (few-step inference) — CM/iCT/sCM/CTM/LCM/LCM-LoRA/TCD/rCM, DMD/DMD2, ADD/LADD/Lightning, Rectified Flow/InstaFlow, Guidance/Progressive distillation",
@@ -112,5 +112,14 @@
   },
   "summary": "Diffusion Distillation tutorial: solo subagent draft (1570 lines, now ~1640) → main-session DIY strict review caught 7 substantive citation/scope issues + 1 render table-pipe collision (round 1). Per user no-defer policy, returned for 4 more rounds of deep math/code fixes on the previously-deferred items: sCM JVP self-reference, DMD score/denoiser notation, DMD2 multi-step backward simulation, LCM-LoRA arg name, Reflow tensor shape. All 5 substantively fixed and verified by codex gpt-5.5 xhigh. Tutorial now production-faithful to sCM (Lu-Song 2024), DMD (Yin 2024 CVPR), DMD2 (Yin 2024 NeurIPS), and current HF diffusers LCM-LoRA API.",
   "no_defer_compliance": true,
-  "rendered_at": "2026-05-20"
+  "rendered_at": "2026-05-20",
+  "post_ship_review": [
+    {
+      "scope": "content-audit fix (holistic per-tutorial review + Codex adjudication)",
+      "verdict": "FIXED",
+      "date": "2026-06-29",
+      "fix": "iCT CIFAR-10 FID corrected: 2.51/2.24 are iCT-DEEP; plain iCT is 2.83/2.46 (resolves the self-contradiction with section 10 Q3).",
+      "note": "Content-audit fix (2026-06-29): a full-collection per-tutorial content review (5 skeptical-expert subagents reading all 28 ZH tutorials) flagged this; Codex GPT-5.5 xhigh cross-model adjudication (threads 019f127e / 019f127f) confirmed it from training knowledge. Factual/citation/number correction — core math/derivations were verified CORRECT across the collection; original gating reviews remain valid; source re-rendered, sidecar hash refreshed."
+    }
+  ]
 }

docs/tutorials/diffusion_distillation_tutorial_en.html

@@ -7,8 +7,8 @@
 
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 <!-- MathJax 3 -->
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@@ -621,8 +621,8 @@ <h1>Diffusion Distillation Tutorial En</h1>
 
   <div class="meta">
     <span><strong>Source:</strong> <code>docs/tutorials/diffusion_distillation_tutorial_en.md</code></span>
-    <span><strong>SHA256:</strong> <code>cceb6c61f1f5</code></span>
-    <span><strong>Rendered:</strong> 2026-06-16 06:15 UTC</span>
+    <span><strong>SHA256:</strong> <code>7972e2659c86</code></span>
+    <span><strong>Rendered:</strong> 2026-06-29 10:11 UTC</span>
 
   </div>
 </header>
@@ -669,7 +669,7 @@ <h3 id="23-ict--improved-techniques-song-dhariwal-2023-arxiv231014189">2.3　iCT
 <table><thead><tr><th>Change</th><th>original CT</th><th>iCT</th></tr></thead><tbody><tr><td>Target</td><td>EMA $\theta^- = \tau \theta^- + (1-\tau) \theta$</td><td><strong>direct stop-grad</strong> (no EMA)</td></tr><tr><td>Loss</td><td>LPIPS</td><td><strong>Pseudo-Huber</strong> $d(a, b) = \sqrt{\lVert a-b \rVert^2 + c^2} - c$</td></tr><tr><td>Noise sched</td><td>uniform discrete $\sigma_n$</td><td><strong>Lognormal</strong>: $\log \sigma \sim \mathcal{N}(P_\text{mean}, P_\text{std}^2)$</td></tr><tr><td>Step count</td><td>fixed $N$</td><td><strong>Curriculum</strong>: $N(k) = \lceil N_\min \cdot (N_\max/N_\min)^{k/K} \rceil$</td></tr></tbody></table>
 <p><strong>Motivation for pseudo-Huber design</strong>:</p>
 <ul><li>LPIPS introduces a <strong>bias</strong> toward ImageNet pretrained features — at eval FID looks good but there's actual distribution shift</li><li>L2 is sensitive to outliers and unstable to train</li><li>Pseudo-Huber $\sqrt{\|a-b\|^2 + c^2} - c$: for small residuals ≈ $\|a-b\|^2/(2c)$ (L2), for large residuals ≈ $\|a-b\|$ (L1) — <strong>adaptively robust</strong></li></ul>
-<p><strong>Results</strong>: iCT achieves <strong>1-step FID 2.51 / 2-step FID 2.24</strong> on CIFAR-10 (paper abstract numbers), and <strong>doesn't depend on a teacher</strong> — fully opening the ceiling for from-scratch consistency training.</p>
+<p><strong>Results</strong>: iCT achieves <strong>1-step FID 2.83 / 2-step FID 2.46</strong> on CIFAR-10 (paper abstract numbers; the deeper iCT-deep reaches 2.51 / 2.24), and <strong>doesn't depend on a teacher</strong> — fully opening the ceiling for from-scratch consistency training.</p>
 <h3 id="24-scm--trigflow-lu-song-2024-openai-arxiv241011081">2.4　sCM / TrigFlow (Lu-Song 2024 OpenAI, arXiv:2410.11081)</h3>
 <p><strong>Problem</strong>: discrete-time CM has two big ailments — (i) discretization error (larger $N$ is more accurate but slower) and (ii) various hyperparameters (noise schedule / EMA decay / loss curriculum) that are finicky to tune.</p>
 <p><strong>TrigFlow parametrization</strong>: write the forward path in trig form —</p>
@@ -1607,8 +1607,8 @@ <h2 id="a-appendix-references">§A Appendix: References</h2>
 <footer class="aris-footer">
   Generated by <a href="https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/blob/main/skills/render-html/SKILL.md">ARIS <code>/render-html</code></a> ·
   source path <code>docs/tutorials/diffusion_distillation_tutorial_en.md</code> ·
-  SHA256 <code>cceb6c61f1f5</code> ·
-  generated at 2026-06-16 06:15 UTC.
+  SHA256 <code>7972e2659c86</code> ·
+  generated at 2026-06-29 10:11 UTC.
   This is a generated view — edit the source Markdown, then re-render.
 </footer>
 </main>

docs/tutorials/diffusion_distillation_tutorial_en.md

@@ -131,7 +131,7 @@ CT (Consistency Training) originally had quality far below CD. iCT improves four
 - L2 is sensitive to outliers and unstable to train
 - Pseudo-Huber $\sqrt{\|a-b\|^2 + c^2} - c$: for small residuals ≈ $\|a-b\|^2/(2c)$ (L2), for large residuals ≈ $\|a-b\|$ (L1) — **adaptively robust**
 
-**Results**: iCT achieves **1-step FID 2.51 / 2-step FID 2.24** on CIFAR-10 (paper abstract numbers), and **doesn't depend on a teacher** — fully opening the ceiling for from-scratch consistency training.
+**Results**: iCT achieves **1-step FID 2.83 / 2-step FID 2.46** on CIFAR-10 (paper abstract numbers; the deeper iCT-deep reaches 2.51 / 2.24), and **doesn't depend on a teacher** — fully opening the ceiling for from-scratch consistency training.
 
 ### 2.4　sCM / TrigFlow (Lu-Song 2024 OpenAI, arXiv:2410.11081)
 

docs/tutorials/diffusion_distillation_tutorial_en.review.json

@@ -1,7 +1,7 @@
 {
   "skill": "interview-cheatsheet (EN translation-fidelity)",
   "source": "docs/tutorials/diffusion_distillation_tutorial_en.md",
-  "source_sha256": "cceb6c61f1f5be01eacd4f25cf2ef79067c1093da76e75e4a92df0a3e3c84877",
+  "source_sha256": "7972e2659c869cbaedfac76b38431acc65ed482b9d2f123301e4fd5a6315eff5",
   "output": "docs/tutorials/diffusion_distillation_tutorial_en.html",
   "zh_source": "docs/tutorials/diffusion_distillation_tutorial.md",
   "reviewer": "codex gpt-5.5 xhigh, fresh thread",
@@ -15,5 +15,14 @@
     "issues": [],
     "summary": "Fresh cross-model translation-fidelity audit by Codex (GPT-5.5 xhigh, thread 019e7cb8-304b-7a41-9555-4f8c6954668a, read-only/detect-only) of the English tutorial docs/tutorials/diffusion_distillation_tutorial_en.md against the cross-model-verified Chinese source of truth docs/tutorials/diffusion_distillation_tutorial.md and the rendered docs/tutorials/diffusion_distillation_tutorial_en.html. Verdict: PASS, zero issues. Both MD files are 1672 lines (line-aligned translation). (a) Numeric/factual fidelity: every number, equation/LaTeX, code block (variable names, hyperparameters, tensor shapes),"
   },
-  "rendered_at": "2026-05-31"
+  "rendered_at": "2026-05-31",
+  "post_ship_review": [
+    {
+      "scope": "content-audit fix (holistic per-tutorial review + Codex adjudication)",
+      "verdict": "FIXED",
+      "date": "2026-06-29",
+      "fix": "iCT CIFAR-10 FID corrected: 2.51/2.24 are iCT-DEEP; plain iCT is 2.83/2.46 (resolves the self-contradiction with section 10 Q3). (EN edition mirrors the ZH fix.)",
+      "note": "Content-audit fix (2026-06-29): a full-collection per-tutorial content review (5 skeptical-expert subagents reading all 28 ZH tutorials) flagged this; Codex GPT-5.5 xhigh cross-model adjudication (threads 019f127e / 019f127f) confirmed it from training knowledge. Factual/citation/number correction — core math/derivations were verified CORRECT across the collection; original gating reviews remain valid; source re-rendered, sidecar hash refreshed."
+    }
+  ]
 }