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

📚 Jump to a topic — 33 first-party cheat sheets across 7 categories + 1 community-contributed category:

🧠 General / Foundations · 🎯 Post-Training & Reasoning · 🏛️ LLM Architecture & Systems · 🌊 Generative Models — Theory & Tokenizers · 🎨 Generation Systems (Image / Video / 3D / Diffusion Post-Training) · 👁️ Multimodal · 🤖 Agents · 🦾 Embodied AI / 具身智能

Or browse the full 📚 Tutorial Index ↓ · jump to 🌐 ARIS-Homepage ↓.

· · · · ·

🏆 Built on a battle-tested foundation — the ARIS main repo 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 /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).

📖 Preview (above): one snapshot per pillar, taken from the Diffusion Foundations cheat sheet — 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

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📢 What’s New

• 2026-07-31 — ✂️ Interview-scannability overhaul for #30-33 + collection-wide table-scroll fix — responding to reader feedback (“comprehensive but unscannable before an interview”): Transformer Block restructured — the concrete block assembly + dataflow + runnable code moved from a buried §7 up to §1 (answer first, rationale after); all four de-hedged — lead sentence states the conclusion, qualifiers demoted to notes, one canonical home per caveat (inference visible prose -33.5%, transformer_block -17.2%, eval -17.4%), zero factual changes; a quantitative screen confirmed the older 29 tutorials healthy. Renderer wide-table overflow fixed, all 67 HTMLs re-rendered; all four EN editions retranslated + fidelity-reviewed. Gate PASS. (#37 · 28e4fb9)
• 2026-07-22 — 🧱 4 new cheat sheets (#30-33): Transformer Block · LLM Evaluation & Benchmarking · LLM Pretraining Pipeline · LLM Inference & Serving Stack — four foundational/systems topics in one batch: residual topologies & the MQA/GQA/MLA design axes, unbiased pass@k estimation & LLM-as-judge, Kaplan vs Chinchilla & the corpus-factory data pipeline, the request state machine & PagedAttention/KV lifecycle — 25-30 interview questions each. First batch to move the cross-model design review before drafting (90-105 numbered guardrails each), then 3-5 independent GPT-5.6-sol review batches; 3 real bugs were each caught independently by multiple batches (Bradley-Terry separation criterion, the best-of-n KV formula). All bilingual with runnable scripts, gate PASS. (#36 · 74644cf)
• 2026-07-13 — 🔍 Diffusion-cluster resweep completes the sweep — 8 tutorials, 66 fixes, all 28 tutorials now under GPT-5.6-sol — covers the diffusion/generative-media cluster deferred from the prior resweep (07-12). Same two-stage pipeline (GPT-5.6-sol finds → independent Claude adversarially verifies): 67 candidates → 66 fixed, 2 REFUTED left untouched; includes 5 real code bugs (iCT/FSQ/LFQ/DDPO) and a recurring “conditional path is straight” ≠ “marginal ODE trajectory is straight” confusion. Gate PASS. (#32 · bfae8f1)
• 2026-07-12 — 🔍 Full-collection resweep — 20 tutorials, 236 fixes, cross-model review upgraded to GPT-5.6-sol — after the reviewer moved from GPT-5.5 to GPT-5.6-sol, re-audited training fundamentals / attention / RLHF / inference systems / PEFT / agents / RAG·VLM (20 files). Same two-stage pipeline: 240 candidates → 236 fixed, 4 REFUTED left untouched; includes 5 real code bugs and StarPO’s acronym settled from the paper’s own abstract. Gate PASS. (#31 · 59636aa)

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📝 Blog Index

Long-form technical blogs — hand-authored, cross-model reviewed; outside the audited /render-html pipeline (figures © their original authors, used with attribution).

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📚 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

🎯 Post-Training & Reasoning

🏛️ LLM Architecture & Systems

🌊 Generative Models — Theory & Tokenizers

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

👁️ Multimodal

🤖 Agents

🎉 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).

🦾 Embodied AI / 具身智能

🌟 Community contribution by @WinstonJQ — hosted externally on a separate repo, generously shared with the community. If it helps your interview prep, please ⭐ the source repo to thank the author 🙏

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🤖 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.

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🌐 ARIS-Homepage — fact-checked academic homepage from CV

The only personal-site generator that fact-checks your CV before publishing.

A new skill in this repo: /homepage-generator turns your CV (.docx / .pdf / .txt) into a polished single-file academic homepage. Cross-model factual audit runs against DBLP / arXiv — wrong venue / year / author / fabricated awards block ship until corrected or explicitly overridden.

Live demo: wanshuiyin.github.io — generated by this skill from a CV + the maintainer’s previous manual page as editorial reference. Preview strip is near the top of this README.

Quick start

aris-homepage init --from-cv ./cv.pdf --out ./site
cd ./site
# Calling agent fills .aris-homepage/extraction.json per EXTRACTION_HANDOFF.md
aris-homepage finalize
$EDITOR profile.yml             # tweak editorial choices
aris-homepage render --persona theory-minimal

Output: index.html + audit-report.md. Drop the HTML on GitHub Pages, S3, university ~user/public_html/, or attach to email — no build server. Minimum runtime is just Python + a calling LLM agent; Codex MCP optional for adversarial cross-model review; Gemini multimodal optional for visual critique.

How it works

                       ARIS-Homepage Pipeline

   📄 CV (.docx/.pdf/.txt)    🌐 Manual Homepage URL    🖼 Assets Dir
   factual source             editorial (optional)      visual (opt.)
         │                            │                      │
         ▼                            │                      │
   ┌──────────┐                       │                      │
   │ init     │                       │                      │
   │ extract  │                       │                      │
   │ CV→text  │                       │                      │
   └─────┬────┘                       │                      │
         ▼                            ▼                      ▼
   ┌─────────────────────────────────────────────────────────────────┐
   │ 🤖 Calling LLM agent reads EXTRACTION_HANDOFF.md +              │
   │    optional manual-homepage URL + assets dir as context         │
   │ → writes .aris-homepage/extraction.json                         │
   └─────────────────────────┬───────────────────────────────────────┘
                             ▼
                       ┌──────────┐
                       │ finalize │
                       └─────┬────┘
                             ▼
   ┌─────────────────────────────────────────────────────────────────┐
   │ ✋ Editable source files (truth lives here, edit in IDE):       │
   │   profile.yml · publications.bib · bio.md · news.md             │
   │   EXTRACTION_REVIEW.md  (review LLM uncertain extractions)      │
   └─────────────────────────┬───────────────────────────────────────┘
                             ▼
                  ┌────────────────────────┐
                  │ render                 │
                  │   --persona            │
                  │     theory-minimal     │
                  └───────────┬────────────┘
                              │
              ┌───────────────┼───────────────┐
              ▼               ▼               ▼
        ┌──────────┐    ┌──────────┐    ┌──────────────┐
        │ Layer-1  │    │ Layer-2  │    │ Layer-2      │
        │ DBLP /   │    │ Codex MCP│    │ Gemini       │
        │ arXiv    │    │ adv-rev  │    │ visual       │
        │ fact-chk │    │ (opt.)   │    │ critique     │
        │ (always) │    │          │    │ (opt.)       │
        └─────┬────┘    └──────────┘    └──────────────┘
              │
              ▼
        ┌──────────────┐
        │ index.html + │
        │ audit-report │ ──▶ 🚀 Deploy: GitHub Pages · S3 · email · anywhere
        │   .md        │
        └──────────────┘

   Typical flow (7 steps, ~5 minutes):
     1. aris-homepage init --from-cv ./cv.pdf --out ./site
     2. (calling agent) read .aris-homepage/EXTRACTION_HANDOFF.md
        → fill .aris-homepage/extraction.json
     3. aris-homepage finalize
     4. $EDITOR profile.yml publications.bib bio.md news.md
     5. aris-homepage check --strict        # fact-check only
     6. aris-homepage render --persona theory-minimal
     7. inspect audit-report.md; fix → re-render OR --override-all

   Minimum runtime: Python + a calling LLM agent.
   Codex MCP optional (cross-model adversarial review).
   Gemini optional (multimodal visual critique).

• Skill contract: skills/homepage-generator/SKILL.md
• Complete schema: skills/homepage-generator/PROFILE_SCHEMA.md
• Implementation: tools/aris_homepage.py (pure-stdlib Python; pip install pyyaml away from working)
• Template: tools/templates/homepage-theory-minimal.html

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🤝 Contributing

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

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

TL;DR: use the /interview-cheatsheet + /render-html 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.

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💬 Community

Shared community with the main ARIS repo — 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:

🔭 Community Showcase

Community-built projects derived from this collection (MIT license — attribution-preserving reuse welcome):

• 大模型秋招教程 (ARIS-in-AI-Offer & Hello-Agents) by @QiZishi — an online reading index that merges all 23 Chinese tutorials here with Datawhale Hello-Agents’ LLM interview Q&A, organized as clickable tutorial cards (repo · from #3).

Built something on top of these tutorials? Open an issue and we’ll list it here.

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🌟 What is ARIS — A Quick Pitch

ARIS — Auto 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.

· · · · ·

• ⭐ ~10k GitHub stars — top-trending AI agent repo
• 🥇 HuggingFace Daily Papers #1 — top of the day, paper arXiv: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

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📖 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:

@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.

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License

MIT — use, modify, share, fork freely. Hope this helps your job search. 💪