/ml-paper-writing

name: ml-paper-writing
description: Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, conducting literature reviews, finding related work, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, citation verification workflows, and paper discovery/evaluation criteria.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Academic Writing, NeurIPS, ICML, ICLR, ACL, AAAI, COLM, LaTeX, Paper Writing, Citations, Research]
dependencies: [semanticscholar, arxiv, habanero, requests]

ML Paper Writing for Top AI Conferences

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

Default operating order

Use this skill in the following order unless the task is unusually narrow: 1. lock the operating mode from `references/OPERATING-MODES.md`, 2. understand the repo or draft context, 3. use `references/citation-workflow.md` as the **canonical citation authority**, 4. load venue- or template-specific references only after the main writing path is clear.

Google Scholar may still help with manual discovery, but it is **not** the canonical verification authority in this skill. Default verification should use programmatic sources such as Semantic Scholar, CrossRef, and arXiv.

Claim ledger gate

Before a project plan, experiment note, or literature summary becomes manuscript prose:

• identify the Claim Candidate or Evidence Record that supports the sentence,
• preserve allowed wording and forbidden stronger wording,
• keep project plans as hypotheses unless experiment artifacts or verified papers support them,
• do not turn related-work motivation into evidence for the paper's own result,
• mark unsupported claims as `[CLAIM NEEDS EVIDENCE]` instead of polishing them.

If the repo context is clear enough for a first draft, still apply this gate before stating contributions, results, related-work contrasts, or rebuttal-facing claims.

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:

% 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:**

claude mcp add exa -- npx -y mcp-remote "https://mcp.exa.ai/mcp"

**Cursor / VS Code** (add to MCP settings):

{
  "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**

# 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 n