<p align="center">
  <img src="assets/aris_logo.svg" alt="ARIS — Auto Research in Sleep" width="640">
</p>

# ARIS-in-AI-Offer (ARIS in 秋招)

> Hoping to make your **秋招 (qiūzhāo, Chinese AI campus recruiting season)** a little easier 🌱
>
> 📖 **中文版 (Chinese version)**: [README_CN.md](README_CN.md)

[![Stars](https://img.shields.io/github/stars/wanshuiyin/Auto-claude-code-research-in-sleep?style=flat&logo=github&logoColor=white&color=gold&label=ARIS%20Stars)](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/stargazers) · [![arXiv](https://img.shields.io/badge/arXiv-2605.03042-b31b1b?style=flat&logo=arxiv)](https://huggingface.co/papers/2605.03042) · [![HF Daily #1](https://img.shields.io/badge/HF%20Daily%20Papers-%231-yellow?style=flat)](https://huggingface.co/papers/2605.03042) · [![PaperWeekly](https://img.shields.io/badge/Featured%20on-PaperWeekly-red?style=flat)](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep) · [![awesome-agent-skills](https://img.shields.io/badge/Featured%20in-awesome--agent--skills-blue?style=flat&logo=github)](https://github.com/VoltAgent/awesome-agent-skills) · [![Project of the Day](https://img.shields.io/badge/AI%20Digital%20Crew-Project%20of%20the%20Day-orange?style=flat)](https://aidigitalcrew.com)

> 🏆 **Built on a battle-tested foundation** — the [**ARIS main repo**](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep) has ~10k GitHub stars, was HuggingFace Daily Papers #1, won AI Digital Crew Project of the Day, and ships 74+ research skills across 7+ platforms. This isn't a vaporware preview — **every cheat sheet here is the production output** of the same `/interview-cheatsheet` + `/render-html` workflow used in academic-research production.

A curated, **bilingual** (中文 + English) collection of ML / LLM / multimodal / diffusion / agent / generative-model interview cheat sheets, auto-generated by the **[ARIS — Auto Research in Sleep](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep)** `/render-html` workflow.

Each cheat sheet is a long-form Chinese tutorial with: formula derivations · from-scratch PyTorch code · 25 high-frequency interview questions (L1 essentials · L2 advanced · L3 top-tier lab).

<p align="center">
  <img src="assets/preview_strip.jpg" alt="ARIS-in-AI-Offer preview — Foundations + Interview Q&A + From-Scratch Code, three columns from a representative cheat sheet" width="100%">
</p>

> 📖 **Preview** (above): one snapshot per pillar, taken from the [Diffusion Foundations cheat sheet](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/diffusion_foundations_tutorial.html) — ① **Foundations** (formula derivations + intuition + TL;DR), ② **Interview Q&A** (25 high-frequency questions stratified L1/L2/L3), ③ **From-Scratch Code** (runnable PyTorch, including CFG training + DDIM sampling). Every cheat sheet in this collection follows the same three-pillar structure.

### 📱 HTML reads cleanly everywhere

Phone on the subway, iPad at a café, laptop in the library — same HTML link opens equally well:

- 🧮 **MathJax** renders all LaTeX formulas (**not screenshots** — scalable, copyable, selectable)
- 💻 **highlight.js** colors all PyTorch code blocks
- 📐 **Responsive layout** adapts to any window width — no overflow, no blur
- 📑 **Sticky TOC** for jumping around long documents
- 💾 **Single-file HTML** — download once, read offline, no backend required

---

## 🌟 What is ARIS — A Quick Pitch

[**ARIS — Auto Research in Sleep**](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep) is one of the most-watched AI research agent skill platforms of 2025-2026. The `/interview-cheatsheet` + `/render-html` skills that produced this repo are 2 out of ARIS's 74+ skills.

[![Stars](https://img.shields.io/github/stars/wanshuiyin/Auto-claude-code-research-in-sleep?style=flat&logo=github&logoColor=white&color=gold&label=Stars)](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/stargazers) · [![arXiv](https://img.shields.io/badge/arXiv-2605.03042-b31b1b?style=flat&logo=arxiv)](https://huggingface.co/papers/2605.03042) · [![HF Daily #1](https://img.shields.io/badge/HF%20Daily%20Papers-%231-yellow?style=flat)](https://huggingface.co/papers/2605.03042) · [![PaperWeekly](https://img.shields.io/badge/Featured%20on-PaperWeekly-red?style=flat)](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep) · [![awesome-agent-skills](https://img.shields.io/badge/Featured%20in-awesome--agent--skills-blue?style=flat&logo=github)](https://github.com/VoltAgent/awesome-agent-skills) · [![Project of the Day](https://img.shields.io/badge/AI%20Digital%20Crew-Project%20of%20the%20Day-orange?style=flat)](https://aidigitalcrew.com)

- ⭐ **~10k GitHub stars** — top-trending AI agent repo
- 🥇 **HuggingFace Daily Papers #1** — top of the day, paper [arXiv:2605.03042](https://huggingface.co/papers/2605.03042)
- 🏆 **AI Digital Crew · Project of the Day** (2026.03.14)
- 📰 **Featured on PaperWeekly** + **VoltAgent/awesome-agent-skills**
- 🛠️ **74+ research skills** — full lifecycle from idea exploration → experiments → papers → rebuttals → talk slides
- 🌐 **7+ platforms supported** — Claude Code · Codex CLI · Cursor · Trae · Antigravity · GitHub Copilot CLI · OpenClaw
- 🔧 **ARIS-Code standalone CLI** — multi-provider runtime, no Claude Code dependency required

Core methodology: **cross-model adversarial review** — executor and reviewer must come from different model families (Claude × GPT-5.5 xhigh × Gemini), so no LLM ever judges its own output. This protocol carries directly into interview cheat sheet generation: every formula, code block, and citation in every tutorial passes an independent audit (see each `.review.json` audit trail).

👉 **ARIS main repo**: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep

---

## 📚 Tutorial Index

> 🌐 **Bilingual editions**: every cheat sheet ships with both a Chinese (default) and an English HTML — filenames are `*_tutorial.html` (CN) and `*_tutorial_en.html` (EN). HTML columns below link to both.

### 🧠 General / Foundations

| Topic | HTML 中文 | HTML EN | MD |
|---|---|---|---|
| **Attention Interview Cheat Sheet** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/attention_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/attention_tutorial_en.html) | [MD](docs/tutorials/attention_tutorial.md) |
| **KL Divergence in RLHF (k1/k2/k3 · placement gradient bias)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/kl_divergence_rlhf_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/kl_divergence_rlhf_tutorial_en.html) | [MD](docs/tutorials/kl_divergence_rlhf_tutorial.md) |

### 🎯 Post-Training & Reasoning

| Topic | HTML 中文 | HTML EN | MD |
|---|---|---|---|
| **RLHF / DPO / GRPO / PPO** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/rlhf_dpo_grpo_ppo_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/rlhf_dpo_grpo_ppo_tutorial_en.html) | [MD](docs/tutorials/rlhf_dpo_grpo_ppo_tutorial.md) |
| **Reasoning Models (o1 / R1 / Test-Time Compute / PRM)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/reasoning_models_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/reasoning_models_tutorial_en.html) | [MD](docs/tutorials/reasoning_models_tutorial.md) |
| **LLM On-Policy Distillation (MiniLLM / GKD / Qwen3 / Tinker)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/llm_opd_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/llm_opd_tutorial_en.html) | [MD](docs/tutorials/llm_opd_tutorial.md) |

### 🏛️ LLM Architecture & Systems

| Topic | HTML 中文 | HTML EN | MD |
|---|---|---|---|
| **MoE (DeepSeek-V3 / Mixtral / Llama 4)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/moe_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/moe_tutorial_en.html) | [MD](docs/tutorials/moe_tutorial.md) |
| **Long Context (RoPE / YaRN / NTK / MLA / StreamingLLM)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/long_context_rope_yarn_mla_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/long_context_rope_yarn_mla_tutorial_en.html) | [MD](docs/tutorials/long_context_rope_yarn_mla_tutorial.md) |
| **KV Cache + Speculative Decoding (Medusa / EAGLE / MLA)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/kv_cache_speculative_decoding_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/kv_cache_speculative_decoding_tutorial_en.html) | [MD](docs/tutorials/kv_cache_speculative_decoding_tutorial.md) |
| **Quantization (GPTQ / AWQ / FP8 / NVFP4 / SmoothQuant)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/quantization_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/quantization_tutorial_en.html) | [MD](docs/tutorials/quantization_tutorial.md) |
| **Distributed Training (DDP / FSDP2 / ZeRO / TP / PP / EP / SP)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/distributed_training_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/distributed_training_tutorial_en.html) | [MD](docs/tutorials/distributed_training_tutorial.md) |

### 🌊 Generative Models — Theory & Tokenizers

| Topic | HTML 中文 | HTML EN | MD |
|---|---|---|---|
| **Flow Matching Quick Reference** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/flow_matching_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/flow_matching_tutorial_en.html) | [MD](docs/tutorials/flow_matching_tutorial.md) |
| **Diffusion Foundations (DDPM / Score / DDIM / EDM / CFG)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/diffusion_foundations_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/diffusion_foundations_tutorial_en.html) | [MD](docs/tutorials/diffusion_foundations_tutorial.md) |
| **VAE / VQ-VAE / VQ-GAN / FSQ** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/vae_vqvae_vqgan_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/vae_vqvae_vqgan_tutorial_en.html) | [MD](docs/tutorials/vae_vqvae_vqgan_tutorial.md) |

### 🎨 Generation Systems — Image / Video / 3D / Diffusion Post-Training

| Topic | HTML 中文 | HTML EN | MD |
|---|---|---|---|
| **Image Gen Systems (LDM / SD / SDXL / SD3 / FLUX / ControlNet)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/image_generation_systems_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/image_generation_systems_tutorial_en.html) | [MD](docs/tutorials/image_generation_systems_tutorial.md) |
| **Video Gen (Sora / Hunyuan-Video / Kling / Wan / Movie Gen)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/video_generation_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/video_generation_tutorial_en.html) | [MD](docs/tutorials/video_generation_tutorial.md) |
| **3D Gen (NeRF / Instant-NGP / 3DGS / SDS / Trellis)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/3d_generation_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/3d_generation_tutorial_en.html) | [MD](docs/tutorials/3d_generation_tutorial.md) |
| **Diffusion Post-Training (DDPO / DPOK / DRaFT / AlignProp / Diffusion-DPO / Flow-GRPO)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/diffusion_post_training_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/diffusion_post_training_tutorial_en.html) | [MD](docs/tutorials/diffusion_post_training_tutorial.md) |
| **Diffusion / Flow Distillation (CM / iCT / sCM / CTM / LCM / DMD/DMD2 / ADD/LADD)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/diffusion_distillation_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/diffusion_distillation_tutorial_en.html) | [MD](docs/tutorials/diffusion_distillation_tutorial.md) |

### 👁️ Multimodal

| Topic | HTML 中文 | HTML EN | MD |
|---|---|---|---|
| **VLM (CLIP / LLaVA / Qwen-VL / DeepSeek-VL)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/vlm_multimodal_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/vlm_multimodal_tutorial_en.html) | [MD](docs/tutorials/vlm_multimodal_tutorial.md) |

### 🤖 Agents

| Topic | HTML 中文 | HTML EN | MD |
|---|---|---|---|
| **Agent Foundations (ReAct / MCP / A2A / SWE-bench / GAIA / OSWorld)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/agent_foundations_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/agent_foundations_tutorial_en.html) | [MD](docs/tutorials/agent_foundations_tutorial.md) |
| **Agentic RL (AgentTuning / ToolRL / RAGEN / WebRL / SWE-RL / GRPO for tool use)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/agentic_rl_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/agentic_rl_tutorial_en.html) | [MD](docs/tutorials/agentic_rl_tutorial.md) |
| **Multi-Agent & Long-Horizon (CAMEL / AutoGen / MetaGPT / MoA / Debate / MemGPT / LATS)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/multi_agent_long_horizon_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/multi_agent_long_horizon_tutorial_en.html) | [MD](docs/tutorials/multi_agent_long_horizon_tutorial.md) |
| **Self-Evolving Agents (Ctx2Skill / Native Evolution / A²RD / Voyager / Reflexion / STaR)** | [📄 CN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/self_evolving_agents_tutorial.html) | [📄 EN](https://wanshuiyin.github.io/ARIS-in-AI-Offer/tutorials/self_evolving_agents_tutorial_en.html) | [MD](docs/tutorials/self_evolving_agents_tutorial.md) |

> 🎉 **23 tutorials live (bilingual)** (2026-05) — each ships with both Chinese and English HTML. Seven buckets: General · Post-Training · Architecture · Generative · Multimodal · Agents · Diffusion Post-Training. This round adds 4 new sheets: KL Divergence in RLHF, LLM On-Policy Distillation, Diffusion Post-Training, Diffusion Distillation. More (Flow-OPD / Audio Gen / further SOTA updates) coming — **PRs welcome** (see [CONTRIBUTING](CONTRIBUTING.md)).

---

## 🤖 How These Are Generated

Every tutorial uses ARIS's `/interview-cheatsheet` skill:

1. **Plan** — 12-14 sections (TL;DR · Intuition · Formulas · Code · Variants · Complexity · 25 Q&A)
2. **Draft** — 600-1000 lines of Chinese tutorial + runnable from-scratch PyTorch
3. **Cross-model review** — fresh-thread codex GPT-5.5 xhigh audit on 10 properties (formula correctness · code runnability · citation accuracy · table-pipe escapes · callout style · personal-info leak · ...)
4. **Fix loop** — trajectory-based; keep going if FAIL set is shrinking, stop if same issue recurs or ~6 rounds without convergence
5. **`/render-html`** — single-file HTML render + 13-property render audit (information fidelity · TOC · math · code highlight · safety · privacy · ...)
6. **`.review.json`** — full audit trail saved next to each tutorial

Cross-model adversarial review (executor ≠ reviewer family) is ARIS's core invariant: an LLM auditing its own output is no audit.

---

## 🚧 Coming features

- 🌐 **ARIS-Homepage** *(TODO)* — auto-generate a personal portfolio / academic homepage for 秋招 from your CV + project list + paper bibliography, powered by the same `/render-html` workflow that produces these cheat sheets. Single-file HTML, deploy-anywhere, MathJax + responsive layout, cross-model review on factual claims (paper venues, dates, model names) before publish. Designed for: PhD candidates putting together an academic homepage, intern candidates building a portfolio, anyone who wants to ship a polished personal site without fighting Hugo / Jekyll / Webflow. **Open an [issue](https://github.com/wanshuiyin/ARIS-in-AI-Offer/issues/new) if you'd like to shape the API.**

---

## 🤝 Contributing

One person can only cover so much. The hope is that many hands make this collection more complete.

Full contribution guide: [**CONTRIBUTING.md**](CONTRIBUTING.md) ([English](CONTRIBUTING.md) · [中文](CONTRIBUTING_CN.md)) — covers ARIS workflow invocation, strict style guide (headings / math / tables / callouts / personal-info banlist), and PR checklist.

**TL;DR**: use the [`/interview-cheatsheet`](skills/interview-cheatsheet/SKILL.md) + [`/render-html`](skills/render-html/SKILL.md) workflow to generate, then open a PR. Both skills enforce a cross-model codex GPT-5.5 xhigh review gate (math / code / citation / render fidelity), so anything merged via PR has a baseline quality floor. Skill source and `tools/render_html.py` are bundled in this repo so you can fork & extend.

**Honest disclaimer**: across the existing tutorials, the HTML structural foundations (math, code, tables, callouts, TOC, responsive layout) are solid. But the very latest frontier work in any given topic (e.g., methods released in late 2025, niche subfield updates) likely is not fully covered. If you spot something outdated or wrong, PRs and issues are equally welcome — let's keep this resource alive together.

---

## 💬 Community

**Shared community with the main [ARIS repo](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep)** — the same WeChat group covers ARIS skill workflows + this tutorial collection. Join to discuss interview prep, request new cheat-sheet topics, or share corrections / contributions:

<p align="center">
  <img src="assets/wechat_group.jpg" alt="WeChat Group QR Code (shared with ARIS main repo)" width="300">
</p>

---

## 📖 Citing ARIS

If this collection — or any cheat sheet here — helped you in your interview prep / research / paper, please consider citing the underlying ARIS methodology paper:

```bibtex
@article{yang2026aris,
  title={ARIS: Autonomous Research via Adversarial Multi-Agent Collaboration},
  author={Yang, Ruofeng and Li, Yongcan and Li, Shuai},
  journal={arXiv preprint arXiv:2605.03042},
  year={2026}
}
```

Every tutorial in this repo was generated end-to-end by the ARIS `/interview-cheatsheet` + `/render-html` workflow with cross-model adversarial review (Claude × GPT-5.5 xhigh × Gemini). The citation supports the methodology behind the workflow, not just this collection.

---

## License

[MIT](LICENSE) — use, modify, share, fork freely. Hope this helps your job search. 💪
