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 — 29 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 ↓.

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🏆 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.

Same /render-html workflow turning a CV into a fact-checked academic homepage. Live demo at wanshuiyin.github.io. Details + pipeline diagram in the ARIS-Homepage section ↓.

A standalone hand-authored long-form technical survey. A Survey on Continuous DLM (2026 H1, 6 papers) — Chinese-language survey by Ruofeng Yang (SJTU), written end-to-end via cross-model discussion (Claude Opus 4.7 + Codex GPT-5.5 xhigh + Gemini auto-gemini-3). 📖 Read full blog ↗.

📱 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's New

• 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)
• 2026-07-11 — 🔤 #29 Tokenization cheat sheet — the foundation the whole collection had been taking for granted: the pipeline mental model (chat template / special-token recognition upstream · normalization → pre-tokenization → segmentation model → IDs → post-processing) · BPE / WordPiece / Unigram (training ≠ encoding — the #1 hand-coding trap) · byte-level BPE vs byte fallback (Ġ vs ▁) · the five vocab quantities (mergeable · special · len(tokenizer) · max-ID+1 · embedding rows — cl100k has ID holes, Qwen pads by checkpoint size) · fertility / BPB metrics · vocab extension (lexical vs control paths) · production traps (prefix instability / token healing · special-token injection · glitch tokens · offset mapping) · tokenizer-free (ByT5 / MegaByte / BLT). Bilingual (中文 + EN), with a runnable script (code/tokenization.py — exact GPT-2 bytes_to_unicode, frozen-rank BPE encoding, both Unigram DPs (forward 0.22350625 hand-checked), vocab-extension regression; pure stdlib, verified on a real box) and 25 高频题. First tutorial with cross-model review moved UP to the design stage (81 GPT-5.6-sol guardrails before drafting + 5 review batches + 1 dispute round + an ultra final SHIP — corrections flowed both ways: the reviewer caught 6 hard errors, the orchestrator overturned the reviewer's Qwen vocab claim). tokenization_tutorial.html.
• 2026-06-29 — ⚙️ #28 Optimizers & LR Schedules cheat sheet — the optimizer/schedule training-mechanics gap: SGD·Momentum·Nesterov · AdaGrad→RMSProp→Adam (the adaptive lineage) · Adam bias correction (the uncorrected first step is ~3.16× too large, not small — the direction trap) · AdamW decoupled weight decay (Adam's L2 ≠ weight decay) · frontier (Muon / Lion / Shampoo / SOAP / Adafactor / LAMB / Sophia) · LR schedules (warmup / cosine / Noam / WSD / one-cycle) · weight decay + LR-batch scaling + no-decay groups · grad clipping + LLM hyperparams (β₂=0.95). Bilingual (中文 + EN), with a runnable script (code/optimizer_lr_schedule.py — SGD/Adam/AdamW from-scratch vs torch.optim, the executable AdamW≠Adam+L2 proof, the bias-correction direction, cosine-warmup, verified on a real box) and 25 高频题. optimizer_lr_schedule_tutorial.html.

• 2026-06-19 — 🧱 #27 Normalization / Residual / Init cheat sheet — the foundational training-mechanics hole: **BatchNorm / LayerN