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. For systems venues (OSDI, NSDI, ASPLOS, SOSP), use systems-paper-writing instead.
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---
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. For systems venues (OSDI, NSDI, ASPLOS, SOSP), use systems-paper-writing instead.
license: MIT
metadata:
version: 1.2.0
category: research-writing
maintainer: Kalaris Labs
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, 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.
**For systems venues (OSDI, NSDI, ASPLOS, SOSP)**, use the [systems-paper-writing](../systems-paper-writing/) skill, which provides paragraph-level structural blueprints, writing patterns, venue-specific checklists, and LaTeX templates for systems conferences.
## 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 the user points you at a code repository (results, configs, logs) instead of a draft, follow the repository-exploration workflow in [references/from-research-repo.md](references/from-research-repo.md) to extract the contribution, results and existing citations before writing.
## When to Use This Skill
Use this skill when:
- **Starting from a research repo** to write a paper
- **Drafting or revising** specific sections
- **Finding and verifying citations** for related work
- **Formatting** for conference submission
- **Resubmitting** to a different venue (format conversion)
- **Iterating** on drafts with scientist feedback
**Always remember**: First drafts are starting points for discussion, not final outputs.
---
## Balancing Proactivity and Collaboration
**Default: Be proactive. Deliver drafts, then iterate.**
| Confidence Level | Action |
|-----------------|--------|
| **High** (clear repo, obvious contribution) | Write full draft, deliver, iterate on feedback |
| **Medium** (some ambiguity) | Write draft with flagged uncertainties, continue |
| **Low** (major unknowns) | Ask 1-2 targeted questions, then draft |
**Draft first, ask with the draft** (not before):
| Section | Draft Autonomously | Flag With Draft |
|---------|-------------------|-----------------|
| Abstract | Yes | "Framed contribution as X—adjust if needed" |
| Introduction | Yes | "Emphasized problem Y—correct if wrong" |
| Methods | Yes | "Included details A, B, C—add missing pieces" |
| Experiments | Yes | "Highlighted results 1, 2, 3—reorder if needed" |
| Related Work | Yes | "Cited papers X, Y, Z—add any I missed" |
**Only block for input when:**
- Target venue is unclear (affects page limits, framing)
- Multiple contradictory framings seem equally valid
- Results seem incomplete or inconsistent
- Explicit request to review before continuing
**Don't block for:**
- Word choice decisions
- Section ordering
- 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" |
| **The Why** | Rigorous empirical evidence supporting claims | Strong baselines, experiments distinguishing hypotheses |
| **The So What** | Why readers should care | Connection to recognized community problems |
**If you cannot state your contribution in one sentence, you don't yet have a paper.**
---
## Paper Structure Workflow
### Workflow 1: Writing a Complete Paper (Iterative)
Copy this checklist and track progress. **Each step involves drafting → feedback → revision:**
```
Paper Writing Progress:
- [ ] Step 1: Define the one-sentence contribution (with scientist)
- [ ] Step 2: Draft Figure 1 → get feedback → revise
- [ ] Step 3: Draft abstract → get feedback → revise
- [ ] Step 4: Draft introduction → get feedback → revise
- [ ] Step 5: Draft methods → get feedback → revise
- [ ] Step 6: Draft experiments → get feedback → revise
- [ ] Step 7: Draft related work → get feedback → revise
- [ ] Step 8: Draft limitations → get feedback → revise
- [ ] Step 9: Complete paper checklist (required)
- [ ] Step 10: Final review cycle and submission
```
**Step 1: Define the One-Sentence Contribution**
**This step requires explicit confirmation from the scientist.**
Before writing anything, articulate and verify:
- What is the single thing your paper contributes?
- What was not obvious or present before your work?
> "I propose framing the contribution as: '[one sentence]'. Does this capture
> what you see as the main takeaway? Should we adjust the emphasis?"
**Step 2: Draft Figure 1**
Figure 1 deserves special attention—many readers skip directly to it.
- Convey core idea, approach, or most compelling result
- Use vector graphics (PDF/EPS for plots)
- Write captions that stand alone without main text
- Ensure readability in black-and-white (8% of men have color vision deficiency)
**Step 3: Write Abstract (5-Sentence Formula)**
From Sebastian Farquhar (DeepMind):
```
1. What you achieved: "We introduce...", "We prove...", "We demonstrate..."
2. Why this is hard and important
3. How you do it (with specialist keywords for discoverability)
4. What evidence you have
5. Your most remarkable number/result
```
**Delete** generic openings like "Large language models have achieved remarkable success..."
**Step 4: Write Introduction (1-1.5 pages max)**
Must include:
- 2-4 bullet contribution list (max 1-2 lines each in two-column format)
- Clear problem statement
- Brief approach overview
- Methods should start by page 2-3 maximum
**Step 5: Methods Section**
Enable reimplementation:
- Conceptual outline or pseudocode
- All hyperparameters listed
- Architectural details sufficient for reproduction
- Present final design decisions; ablations go in experiments
**Step 6: Experiments Section**
For each experiment, explicitly state:
- What claim it supports
- How it connects to main contribution
- Experimental setting (details in appendix)
- What to observe: "the blue line shows X, which demonstrates Y"
Requirements:
- Error bars with methodology (standard deviation vs standard error)
- Hyperparameter search ranges
- Compute infrastructure (GPU type, total hours)
- Seed-setting methods
**Step 7: Related Work**
Organize methodologically, not paper-by-paper:
**Good:** "One line of work uses Floogledoodle's assumption [refs] whereas we use Doobersnoddle's assumption because..."
**Bad:** "Snap et al. introduced X while Crackle et al. introduced Y."
Cite generously—reviewers likely authored relevant papers.
**Step 8: Limitations Section (REQUIRED)**
All major conferences require this. Counter-intuitively, honesty helps:
- Reviewers are instructed not to penalize honest limitation acknowledgment
- Pre-empt criticisms by identifying weaknesses first
- Explain why limitations don't undermine core claims
**Step 9: Paper Checklist**
NeurIPS, ICML, and ICLR all require paper checklists. See [references/checklists.md](references/checklists.md).
---
## Writing Philosophy for Top ML Conferences
Sentence-level clarity, word choice, time allocation and what reviewers actually read are covered in [references/writing-philosophy.md](references/writing-philosophy.md). Read it before polishing prose.
## Conference Requirements Quick Reference
### ML/AI Conferences
| Conference | Page Limit | Extra for Camera-Ready | Key Requirement |
|------------|------------|------------------------|------------------|
| **NeurIPS 2025** | 9 pages | +0 | Mandatory checklist, lay summary for accepted |
| **ICML 2026** | 8 pages | +1 | Broader Impact Statement required |
| **ICLR 2026** | 9 pages | +1 | LLM disclosure required, reciprocal reviewing |
| **ACL 2025** | 8 pages (long) | varies | Limitations section mandatory |
| **AAAI 2026** | 7 pages | +1 | Strict style file adherence |
| **COLM 2025** | 9 pages | +1 | Focus on language models |
**Systems Conferences (OSDI, NSDI, ASPLOS, SOSP)**: See the [systems-paper-writing](../systems-paper-writing/) skill for page limits, templates, deadlines, and submission rules.
**Universal Requirements:**
- Double-blind review (anonymize submissions)
- References don't count toward page limit
- Appendices unlimited but reviewers not required to read
- LaTeX required for all venues
**LaTeX Templates:** See [templates/](templates/) directory for all conference templates.
---
## Using LaTeX Templates and Converting Between Venues
Starting a paper from a bundled template (Workflow 4), compiling, template pitfalls, and converting between venues after a rejection (Workflow 3) are covered in [references/latex-templates-and-conversion.md](references/latex-templates-and-conversion.md). Always copy the complete template directory from `templates/` rather than writing LaTeX preambles from memory.
## Citation Workflow (Hallucination Prevention)
**⚠️ CRITICAL**: AI-generated citations have ~40% error rate. **Never write BibTeX from memory.**
### The Golden Rule
```
IF you cannot programmatically fetch a citation:
→ Mark it as [CITATION NEEDED] or [PLACEHOLDER - VERIFY]
→ Tell the scientist explicitly
→ NEVER invent a plausible-sounding reference
```
### Workflow 2: Adding Citations
```
Citation Verification (MANDATORY for every citation):
- [ ] Step 1: Search using Exa MCP or Semantic Scholar API
- [ ] Step 2: Verify paper exists in 2+ sources (Semantic Scholar + arXiv/CrossRef)
- [ ] Step 3: Retrieve BibTeX via DOI (programmatically, not from memory)
- [ ] Step 4: Verify the claim you're citing actually appears in the paper
- [ ] Step 5: Add verified BibTeX to bibliography
- [ ] Step 6: If ANY step fails → mark as placeholder, inform scientist
```
**Step 0: Use Exa MCP for Initial Search (Recommended)**
If Exa MCP is installed, use it to find relevant papers:
```
Search: "RLHF language model alignment 2023"
Search: "sparse autoencoders interpretability"
Search: "attention mechanism transformers Vaswani"
```
Then verify each result with Semantic Scholar and fetch BibTeX via DOI.
**Step 1: Search Semantic Scholar**
```python
from semanticscholar import SemanticScholar
sch = SemanticScholar()
results = sch.search_paper("attention mechanism transformers", limit=5)
for paper in results:
print(f"{paper.title} - {paper.paperId}")
print(f" DOI: {paper.externalIds.get('DOI', 'N/A')}")
```
**Step 2: Verify Existence**
Confirm paper appears in at least two sources (Semantic Scholar + CrossRef/arXiv).
**Step 3: Retrieve BibTeX via DOI**
```python
import requests
def doi_to_bibtex(doi: str) -> str:
"""Get verified BibTeX from DOI via CrossRef."""
response = requests.get(
f"https://doi.org/{doi}",
headers={"Accept": "application/x-bibtex"}
)
response.raise_for_status()
return response.text
# Example
bibtex = doi_to_bibtex("10.48550/arXiv.1706.03762")
print(bibtex)
```
**Step 4: Verify Claims**
Before citing for a specific claim, access the paper and confirm the attributed claim actually appears.
**Step 5: Handle Failures Explicitly**
If you cannot verify a citation at ANY step:
```latex
% Option 1: Explicit placeholder
\cite{PLACEHOLDER_smith2023_verify} % TODO: Could not verify - scientist must confirm
% Option 2: Note in text
... as shown in prior work [CITATION NEEDED - could not verify Smith et al. 2023].
```
**Always inform the scientist:**
> "I could not verify the following citations and have marked them as placeholders:
> - Smith et al. 2023 on reward hacking - could not find in Semantic Scholar
> - Jones 2022 on scaling laws - found similar paper but different authors
> Please verify these before submission."
### Summary: Citation Rules
| Situation | Action |
|-----------|--------|
| Found paper, got DOI, fetched BibTeX | ✅ Use the citation |
| Found paper, no DOI | ✅ Use arXiv BibTeX or manual entry from paper |
| Paper exists but can't fetch BibTeX | ⚠️ Mark placeholder, inform scientist |
| Uncertain if paper exists | ❌ Mark `[CITATION NEEDED]`, inform scientist |
| "I think there's a paper about X" | ❌ **NEVER cite** - search first or mark placeholder |
**🚨 NEVER generate BibTeX from memory—always fetch programmatically. 🚨**
See [references/citation-workflow.md](references/citation-workflow.md) for complete API documentation.
---
## Troubleshooting, Reviewer Criteria and Figures
Troubleshooting common writing problems, the criteria reviewers score on, and table/figure conventions are in [references/issues-reviewers-figures.md](references/issues-reviewers-figures.md).
## References & Resources
### Reference Documents (Deep Dives)
| Document | Contents |
|----------|----------|
| [writing-guide.md](references/writing-guide.md) | Gopen & Swan 7 principles, Ethan Perez micro-tips, word choice |
| [citation-workflow.md](references/citation-workflow.md) | Citation APIs, Python code, BibTeX management |
| [checklists.md](references/checklists.md) | NeurIPS 16-item, ICML, ICLR, ACL requirements |
| [reviewer-guidelines.md](references/reviewer-guidelines.md) | Evaluation criteria, scoring, rebuttals |
| [sources.md](references/sources.md) | Complete bibliography of all sources |
| [from-research-repo.md](references/from-research-repo.md) | Workflow 0: turning a code repository into a paper |
| [writing-philosophy.md](references/writing-philosophy.md) | Time allocation, clarity, word choice, what reviewers read |
| [latex-templates-and-conversion.md](references/latex-templates-and-conversion.md) | Template setup, compilation, pitfalls, venue conversion |
| [issues-reviewers-figures.md](references/issues-reviewers-figures.md) | Common issues, reviewer criteria, tables and figures |
### LaTeX Templates
Templates in `templates/` directory:
- **ML/AI**: ICML 2026, ICLR 2026, NeurIPS 2025, ACL/EMNLP, AAAI 2026, COLM 2025
- **Systems** (OSDI, NSDI, ASPLOS, SOSP): See [systems-paper-writing](../systems-paper-writing/) skill
**Compiling to PDF:**
- **VS Code/Cursor**: Install LaTeX Workshop extension + TeX Live → Save to auto-compile
- **Command line**: `latexmk -pdf main.tex` or `pdflatex` + `bibtex` workflow
- **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
- [Gopen & Swan: Science of Scientific Writing](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) - 7 reader expectation principles
- [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
**APIs:** [Semantic Scholar](https://api.semanticscholar.org/api-docs/) | [CrossRef](https://www.crossref.org/documentation/retrieve-metadata/rest-api/) | [arXiv](https://info.arxiv.org/help/api/basics.html)
**ML/AI Venues:** [NeurIPS](https://neurips.cc/Conferences/2025/PaperInformation/StyleFiles) | [ICML](https://icml.cc/Conferences/2025/AuthorInstructions) | [ICLR](https://iclr.cc/Conferences/2026/AuthorGuide) | [ACL](https://github.com/acl-org/acl-style-files)
**Systems Venues:** See the [systems-paper-writing](../systems-paper-writing/) skill for OSDI, NSDI, ASPLOS, SOSP links and guides
## Related skills
- `systems-paper-writing`: Provides paragraph-level structural blueprints for 10-12 page systems papers targeting OSDI, SOSP, ASPLOS, NSDI, and EuroSys.
- `rebuttal-and-response-to-reviewers`: Plan and write responses to peer review, including journal "response to reviewers" letters for revise-and-resubmit, conference rebuttals un…
- `reproducibility-statement`: Prepare the reproducibility, transparency and open-science parts of a paper, including data and code availability statements, reproducibili…
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