docs(readme): make English the default README, keep Chinese as README_CN

- README.md → README_CN.md (Chinese preserved)
- README_EN.md → README.md (English now default)
- Cross-language pointers updated in both files (CN ↔ EN)
- CONTRIBUTING.md + CONTRIBUTING_CN.md: README_EN.md references → README_CN.md

The collection itself remains Chinese-first (each cheat sheet ships with both
中文 and EN HTML); English README is now the landing page so international
readers can navigate the project at a glance.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Author: wanshuiyin

CONTRIBUTING.md

@@ -19,7 +19,7 @@ Every tutorial in this repo was generated by ARIS's `/interview-cheatsheet` + `/
 1. **Install ARIS** (skill source is bundled in `skills/`; canonical home is the [ARIS main repo](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep)). With Claude Code: `bash <(curl -s https://raw.githubusercontent.com/wanshuiyin/Auto-claude-code-research-in-sleep/main/tools/install_aris.sh)`
 2. **Invoke** `/interview-cheatsheet "<TOPIC>"` from inside this repo. The skill auto-runs the cross-model codex review gate + render-html.
 3. **Review** the generated `.md` / `.html` / `.review.json` files yourself before pushing.
-4. **Open a PR** with all three files (MD source, HTML render, review JSON) plus a row added to README.md / README_EN.md in the right category.
+4. **Open a PR** with all three files (MD source, HTML render, review JSON) plus a row added to README.md / README_CN.md in the right category.
 
 If you want to do it by hand without ARIS, that's fine too — just match the structure of an existing tutorial (e.g. `docs/tutorials/attention_tutorial.md`) and run `tools/render_html.py` to render.
 
@@ -41,7 +41,7 @@ See `skills/render-html/SKILL.md` and `skills/interview-cheatsheet/SKILL.md` for
 ## Pull request process
 
 1. Make sure your changes are well-documented (include the `.review.json` so reviewers can see your audit trail).
-2. Update **both** `README.md` and `README_EN.md` if you add a new tutorial — pick the right category bucket (General / Post-Training & Reasoning / LLM Architecture & Systems / Generative Models — Theory & Tokenizers / Generation Systems / Multimodal / Agents).
+2. Update **both** `README.md` and `README_CN.md` if you add a new tutorial — pick the right category bucket (General / Post-Training & Reasoning / LLM Architecture & Systems / Generative Models — Theory & Tokenizers / Generation Systems / Multimodal / Agents).
 3. Keep PRs focused on a single topic — one tutorial per PR.
 4. Clear commit messages — `docs(tutorials): add <Topic> cheat sheet (rendered via /render-html)` is the convention.
 
@@ -51,7 +51,7 @@ See `skills/render-html/SKILL.md` and `skills/interview-cheatsheet/SKILL.md` for
 - [ ] `.html` is freshly rendered via `tools/render_html.py`
 - [ ] `.review.json` includes cross-model codex audit trail (math/code review verdict + render review verdict + thread IDs)
 - [ ] No personal info beyond the byline
-- [ ] Both `README.md` + `README_EN.md` updated
+- [ ] Both `README.md` + `README_CN.md` updated
 - [ ] Citations are real and verifiable (no hallucinated authors / years / venues)
 
 ## Honest disclaimer

CONTRIBUTING_CN.md

@@ -19,7 +19,7 @@
 1. **安装 ARIS**（skill 源码在 `skills/`；canonical 来源是 [ARIS 主仓库](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep)）。Claude Code 用户：`bash <(curl -s https://raw.githubusercontent.com/wanshuiyin/Auto-claude-code-research-in-sleep/main/tools/install_aris.sh)`
 2. **在本仓库目录里调** `/interview-cheatsheet "<TOPIC>"`。skill 自动跑跨模型 codex review gate + render-html。
 3. **自己 review** 生成的 `.md` / `.html` / `.review.json` 再 push。
-4. **开 PR**：三个文件都带上（MD 源 / HTML 渲染 / review JSON），README.md / README_EN.md 对应分类下加一行。
+4. **开 PR**：三个文件都带上（MD 源 / HTML 渲染 / review JSON），README.md / README_CN.md 对应分类下加一行。
 
 不想用 ARIS 手写也行 —— 照着现有教程的结构写（例如 `docs/tutorials/attention_tutorial.md`），跑 `tools/render_html.py` 渲染就行。
 
@@ -41,7 +41,7 @@
 ## PR 流程
 
 1. 改动要文档化（带上 `.review.json` 让 reviewer 看你的 audit trail）
-2. 加新教程要 **同时** 更新 `README.md` 和 `README_EN.md`，按分类放（General / Post-Training & Reasoning / LLM Architecture & Systems / Generative Models — Theory & Tokenizers / Generation Systems / Multimodal / Agents）
+2. 加新教程要 **同时** 更新 `README.md` 和 `README_CN.md`，按分类放（General / Post-Training & Reasoning / LLM Architecture & Systems / Generative Models — Theory & Tokenizers / Generation Systems / Multimodal / Agents）
 3. PR 聚焦单一主题 —— 一篇教程一个 PR
 4. Commit 信息清晰 —— 规范是 `docs(tutorials): add <Topic> cheat sheet (rendered via /render-html)`
 
@@ -51,7 +51,7 @@
 - [ ] `.html` 是用 `tools/render_html.py` 重新渲染的
 - [ ] `.review.json` 包含跨模型 codex audit trail（math/code review verdict + render review verdict + thread IDs）
 - [ ] byline 之外没个人信息
-- [ ] `README.md` + `README_EN.md` 都更新了
+- [ ] `README.md` + `README_CN.md` 都更新了
 - [ ] 引用真实可验证（不能 hallucinate 作者 / 年份 / venue）
 
 ## 坦白说