ml-paper-writing

Files (62)

• SKILL.md
• checklists.md · references/
• citation-workflow.md · references/
• reviewer-guidelines.md · references/
• sources.md · references/
• systems-conferences.md · references/
• writing-guide.md · references/
• README.md · templates/
• README.md · templates/aaai2026/
• aaai2026-unified-supp.tex · templates/aaai2026/
• aaai2026-unified-template.tex · templates/aaai2026/
• aaai2026.bib · templates/aaai2026/
• aaai2026.bst · templates/aaai2026/
• aaai2026.sty · templates/aaai2026/
• README.md · templates/acl/
• acl.sty · templates/acl/
• acl_latex.tex · templates/acl/
• acl_lualatex.tex · templates/acl/
• acl_natbib.bst · templates/acl/
• anthology.bib.txt · templates/acl/
• custom.bib · templates/acl/
• formatting.md · templates/acl/
• main.tex · templates/asplos2027/
• references.bib · templates/asplos2027/
• README.md · templates/colm2025/
• colm2025_conference.bib · templates/colm2025/
• colm2025_conference.bst · templates/colm2025/
• colm2025_conference.pdf · templates/colm2025/
• colm2025_conference.sty · templates/colm2025/
• colm2025_conference.tex · templates/colm2025/
• fancyhdr.sty · templates/colm2025/
• math_commands.tex · templates/colm2025/
• natbib.sty · templates/colm2025/
• fancyhdr.sty · templates/iclr2026/
• iclr2026_conference.bib · templates/iclr2026/
• iclr2026_conference.bst · templates/iclr2026/
• iclr2026_conference.pdf · templates/iclr2026/
• iclr2026_conference.sty · templates/iclr2026/
• iclr2026_conference.tex · templates/iclr2026/
• math_commands.tex · templates/iclr2026/
• natbib.sty · templates/iclr2026/
• algorithm.sty · templates/icml2026/
• algorithmic.sty · templates/icml2026/
• example_paper.bib · templates/icml2026/
• example_paper.pdf · templates/icml2026/
• example_paper.tex · templates/icml2026/
• fancyhdr.sty · templates/icml2026/
• icml2026.bst · templates/icml2026/
• icml2026.sty · templates/icml2026/
• icml_numpapers.pdf · templates/icml2026/
• Makefile · templates/neurips2025/
• extra_pkgs.tex · templates/neurips2025/
• main.tex · templates/neurips2025/
• neurips.sty · templates/neurips2025/
• main.tex · templates/nsdi2027/
• references.bib · templates/nsdi2027/
• usenix-2020-09.sty · templates/nsdi2027/
• main.tex · templates/osdi2026/
• references.bib · templates/osdi2026/
• usenix-2020-09.sty · templates/osdi2026/
• …and 2 more

SKILL.md (first lines)

# ML Paper Writing for Top AI & Systems Conferences

Expert-level guidance for writing publication-ready papers targeting **NeurIPS, ICML, ICLR, ACL, AAAI, COLM** (ML/AI venues) and **OSDI, NSDI, ASPLOS, SOSP** (Systems venues). This skill combines writing philosophy from top researchers (Nanda, Farquhar, Karpathy, Lipton, Steinhardt) with practical tools: LaTeX templates, citation verification APIs, and conference checklists.

## Core Philosophy: Collaborative Writing

**Paper writing is collaborative, but Claude should be proactive in delivering drafts.**

The typical workflow starts with a research repository containing code, results, and experimental artifacts. Claude's role is to:

1. **Understand the project** by exploring the repo, results, and existing documentation
2. **Deliver a complete first draft** when confident about the contribution
3. **Search literature** using web search and APIs to find relevant citations
4. **Refine through feedback cycles** when the scientist provides input
5. **Ask for clarification** only when genuinely uncertain about key decisions

**Key Principle**: Be proactive. If the repo and results are clear, deliver a full draft. Don't block waiting for feedback on every section—scientists are busy. Produce something concrete they can react to, then iterate based on their response.

---

## ⚠️ CRITICAL: Never Hallucinate Citations

**This is the most important rule in academic writing with AI assistance.**

### The Problem
AI-generated citations have a **~40% error rate**. Hallucinated references—papers that don't exist, wrong authors, incorrect years, fabricated DOIs—are a serious form of academic misconduct that can result in desk rejection or retraction.

### The Rule
**NEVER generate BibTeX entries from memory. ALWAYS fetch programmatically.**

| Action | ✅ Correct | ❌ Wrong |
|--------|-----------|----------|
| Adding a citation | Search API → verify → fetch BibTeX | Write BibTeX from memory |
| Uncertain about a paper | Mark as `[CITATION NEEDED]` | Guess the reference |
| Can't find exact paper | Note: "placeholder - verify" | Invent similar-sounding paper |

### When You Can't Verify a Citation

If you cannot programmatically verify a citation, you MUST:

```latex
% EXPLICIT PLACEHOLDER - requires human verification
\cite{PLACEHOLDER_author2024_verify_this}  % TODO: Verify this citation exists
```

**Always tell the scientist**: "I've marked [X] citations as placeholders that need verification. I could not confirm these papers exist."

### Recommended: Install Exa MCP for Paper Search

For the best paper search experience, install **Exa MCP** which provides real-time academic search:

**Claude Code:**
```bash
claude mcp add exa -- npx -y mcp-remote "https://mcp.exa.ai/mcp"
```

**Cursor / VS Code** (add to MCP settings):
```json
{
  "mcpServers": {
    "exa": {
      "type": "http",
      "url": "https://mcp.exa.ai/mcp"
    }
  }
}
```

Exa MCP enables searches like:
- "Find papers on RLHF for language models published after 2023"
- "Search for transformer architecture papers by Vaswani"
- "Get recent work on sparse autoencoders for interpretability"

Then verify results with Semantic Scholar API and fetch BibTeX via DOI.

---

## Workflow 0: Starting from a Research Repository

When beginning paper writing, start by understanding the project:

```
Project Understanding:
- [ ] Step 1: Explore the repository structure
- [ ] Step 2: Read README, existing docs, and key results
- [ ] Step 3: Identify the main contribution with the scientist
- [ ] Step 4: Find papers already cited in the codebase
- [ ] Step 5: Search for additional relevant literature
- [ ] Step 6: Outline the paper structure together
- [ ] Step 7: Draft sections iteratively with feedback
```

**Step 1: Explore the Repository**

```bash
# Understand project structure
ls -la
find . -name "*.py" | head -20
find . -name "*.md" -o -name "*.txt" | xargs grep -l -i "result\|conclusion\|finding"
```

Look for:
- `README.md` - Project overview and claims
- `results/`, `outputs/`, `experiments/` - Key findings
- `configs/` - Experimental settings
- Existing `.bib` files or citation references
- Any draft documents or notes

**Step 2: Identify Existing Citations**

Check for papers already referenced in the codebase:

```bash
# Find existing citations
grep -r "arxiv\|doi\|cite" --include="*.md" --include="*.bib" --include="*.py"
find . -name "*.bib"
```

These are high-signal starting points for Related Work—the scientist has already deemed them relevant.

From OpenRaiser/NanoResearch, MIT.

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