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.

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: 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: Cursor / VS Code (add to MCP settings): 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: Step 1: Explore the Repository 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: These are high signal starting points for Related Work—the scientist has already deemed them relevant. Step 3: Clarify the Contribution Before writing, explicitly confirm with the scientist: "Based on my understanding of the repo, the main contribution appears to be [X]. The key results show [Y]. Is this the framing you want for the paper, or should we emphasize different aspects?" Never assume the narrative—always verify with the human. Step 4: Search for Additional Literature Use web search to find relevant papers: Then verify and retrieve BibTeX using the citation workflow below. Step 5: Deliver a First Draft Be proactive—deliver a complete draft rather than asking permission for each section. If the repo provides clear results and the contribution is apparent: 1. Write the full first draft end to end 2. Present the complete draft for feedback 3. Iterate based on scientist's response If genuinely uncertain about framing or major claims: 1. Draft what you can confidently 2. Flag specific uncertainties: "I framed X as the main contribution—let me know if you'd prefer to emphasize Y instead" 3. Continue with the draft rather than blocking Questions to include with the draft (not before): "I emphasized X as the main contribution—adjust if needed" "I highlighted results A, B, C—let me know if others are more important" "Related work section includes [papers]—add any I missed" 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: 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): 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 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/) Jacob Steinhardt (UC Berkeley) Precision, consistent terminology [Writing Tips](https://bounded regret.ghost.io/) Ethan Perez (Anthropic) Micro level clarity tips [Easy Paper Writing Tips](https://ethanperez.net/easy paper writing tips/) Andrej Karpathy Single contribution focus Various lectures For deeper dives into any of these, see: [references/writing guide.md](references/writing guide.md) Full explanations with examples [references/sources.md](references/sources.md)

ml-paper-writing