Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation
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Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation
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LanguagePython
LicenseMIT
📦 Ships with claude-code-templates
</> SKILL.md
ml-paper-writing.SKILL.md
---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, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
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.
## 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:
- Which specific results to show (make a choice, flag it)
- Citation completeness (draft with what you find, note gaps)
---
## The Narrative Principle
**The single most critical insight**: Your paper is not a collection of experiments—it's a story with one clear contribution supported by evidence.
Every successful ML paper centers on what Neel Nanda calls "the narrative": a short, rigorous, evidence-based technical story with a takeaway readers care about.
**Three Pillars (must be crystal clear by end of introduction):**
| Pillar | Description | Example |
|--------|-------------|---------|
| **The What** | 1-3 specific novel claims within cohesive theme | "We prove that X achieves Y under condition Z" |
NeurIPS, ICML, and ICLR all require paper checklists. See [references/checklists.md](references/checklists.md).
---
## Writing Philosophy for Top ML Conferences
**This section distills the most important writing principles from leading ML researchers.** These aren't optional style suggestions—they're what separates accepted papers from rejected ones.
> "A paper is a short, rigorous, evidence-based technical story with a takeaway readers care about." — Neel Nanda
### The Sources Behind This Guidance
This skill synthesizes writing philosophy from researchers who have published extensively at top venues:
| Source | Key Contribution | Link |
|--------|-----------------|------|
| **Neel Nanda** (Google DeepMind) | The Narrative Principle, What/Why/So What framework | [How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) |
| **Sebastian Farquhar** (DeepMind) | 5-sentence abstract formula | [How to Write ML Papers](https://sebastianfarquhar.com/on-research/2024/11/04/how_to_write_ml_papers/) |
| **Gopen & Swan** | 7 principles of reader expectations | [Science of Scientific Writing](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) |
| **Zachary Lipton** | Word choice, eliminating hedging | [Heuristics for Scientific Writing](https://www.approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/) |
**Why?** Most reviewers form judgments before reaching your methods. Readers encounter your paper as: **title → abstract → introduction → figures → maybe the rest.**
These principles are based on how readers actually process prose. Violating them forces readers to spend cognitive effort on structure rather than content.
| Principle | Rule | Example |
|-----------|------|---------|
| **Subject-verb proximity** | Keep subject and verb close | ❌ "The model, which was trained on..., achieves" → ✅ "The model achieves... after training on..." |
| **Stress position** | Place emphasis at sentence ends | ❌ "Accuracy improves by 15% when using attention" → ✅ "When using attention, accuracy improves by **15%**" |
| **Topic position** | Put context first, new info after | ✅ "Given these constraints, we propose..." |
| **Old before new** | Familiar info → unfamiliar info | Link backward, then introduce new |
| **One unit, one function** | Each paragraph makes one point | Split multi-point paragraphs |
| **Action in verb** | Use verbs, not nominalizations | ❌ "We performed an analysis" → ✅ "We analyzed" |
| **Context before new** | Set stage before presenting | Explain before showing equation |
**Full 7 principles with detailed examples:** See [references/writing-guide.md](references/writing-guide.md#the-7-principles-of-reader-expectations)
#### Micro-Level Tips (Ethan Perez)
These small changes accumulate into significantly clearer prose:
| COLM 2025 | `colm2025_conference.tex` | `colm2025_conference.sty` | Similar to ICLR |
---
## Conference Resubmission & Format Conversion
When a paper is rejected or withdrawn from one venue and resubmitted to another, format conversion is required. This is a common workflow in ML research.
### Workflow 3: Converting Between Conference Formats
- **Online**: Upload to [Overleaf](https://overleaf.com)
See [templates/README.md](templates/README.md) for detailed setup instructions.
### Key External Sources
**Writing Philosophy:**
- [Neel Nanda: How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) - Narrative, "What/Why/So What"
- [Farquhar: How to Write ML Papers](https://sebastianfarquhar.com/on-research/2024/11/04/how_to_write_ml_papers/) - 5-sentence abstract
- [Lipton: Heuristics for Scientific Writing](https://www.approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/) - Word choice
- [Perez: Easy Paper Writing Tips](https://ethanperez.net/easy-paper-writing-tips/) - Micro-level clarity
