ml-paper-writing

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: Understand the project by exploring the repo, results, and existing documentation Deliver a complete first draft when confident about the contribution Search literature using web search and APIs to find relevant citations Refine through feedback cycles when the scientist provides input 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 notes Step 2: Identify Existing Citations Check for papers already referenced in the codebase: # 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. 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: Search queries to try: - "[main technique] + [application domain]" - "[baseline method] comparison" - "[problem name] state-of-the-art" - Author names from existing citations 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: Write the full first draft end-to-end Present the complete draft for feedback Iterate based on scientist's response If genuinely uncertain about framing or major claims: Draft what you can confidently Flag specific uncertainties: "I framed X as the main contribution—let me know if you'd prefer to emphasize Y instead" 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: 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. 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 Sebastian Farquhar (DeepMind) 5-sentence abstract formula How to Write ML Papers Gopen & Swan 7 principles of reader expectations Science of Scientific Writing Zachary Lipton Word choice, eliminating hedging Heuristics for Scientific Writing Jacob Steinhardt (UC Berkeley) Precision, consistent terminology Writing Tips Ethan Perez (Anthropic) Micro-level clarity tips Easy Paper Writing Tips Andrej Karpathy Single contribution focus Various lectures For deeper dives into any of these, see: references/writing-guide.md - Full explanations with examples references/sources.md - Complete bibliography Time Allocation (From Neel Nanda) Spend approximately equal time on each of: The abstract The introduction The figures Everything else combined Why? Most reviewers form judgments before reaching your methods. Readers encounter your paper as: title → abstract → introduction → figures → maybe the rest. Writing Style Guidelines Sentence-Level Clarity (Gopen & Swan's 7 Principles) 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 Micro-Level Tips (Ethan Perez) These small changes accumulate into significantly clearer prose: Minimize pronouns: ❌ "This shows..." → ✅ "This result shows..." Verbs early: Position verbs near sentence start Unfold apostrophes: ❌ "X's Y" → ✅ "The Y of X" (when awkward) Delete filler words: "actually," "a bit," "very," "really," "basically," "quite," "essentially" Full micro-tips with examples: See references/writing-guide.md Word Choice (Zachary Lipton) Be specific: ❌ "performance" → ✅ "accuracy" or "latency" (say what you mean) Eliminate hedging: Drop "may" and "can" unless genuinely uncertain Avoid incremental vocabulary: ❌ "combine," "modify," "expand" → ✅ "develop," "propose," "introduce" Delete intensifiers: ❌ "provides very tight approximation" → ✅ "provides tight approximation" Precision Over Brevity (Jacob Steinhardt) Consistent terminology: Different terms for same concept creates confusion. Pick one and stick with it. State assumptions formally: Before theorems, list all assumptions explicitly Intuition + rigor: Provide intuitive explanations alongside formal proofs What Reviewers Actually Read Understanding reviewer behavior helps prioritize your effort: Paper Section % Reviewers Who Read Implication Abstract 100% Must be perfect Introduction 90%+ (skimmed) Front-load contribution Figures Examined before methods Figure 1 is critical Methods Only if interested Don't bury the lede Appendix Rarely Put only supplementary details Bottom line: If your abstract and intro don't hook reviewers, they may never read your brilliant methods section. 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 Conference Page Limit Extra for Camera-Ready Key Requirement Template OSDI 2026 12 pages +2 (14 pages) Research + Operational Systems tracks USENIX NSDI 2027 12 pages varies Prescreening via Introduction; 3 tracks USENIX ASPLOS 2027 12 pages (ACM) varies Rapid review on first 2 pages; dual cycles ACM SIGPLAN SOSP 2026 12 pages varies Optional artifact evaluation; author response ACM SIGPLAN Detailed Systems conference info: See references/systems-conferences.md for deadlines, track descriptions, submission rules, and format conversion guides. 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 Systems venues: USENIX uses custom .sty; ACM uses acmart.cls LaTeX Templates: See templates/ directory for all conference templates. Using LaTeX Templates Properly Workflow 4: Starting a New Paper from Template Always copy the entire template directory first, then write within it. Template Setup Checklist: - [ ] Step 1: Copy entire template directory to new project - [ ] Step 2: Verify template compiles as-is (before any changes) - [ ] Step 3: Read the template's example content to understand structure - [ ] Step 4: Replace example content section by section - [ ] Step 5: Keep template comments/examples as reference until done - [ ] Step 6: Clean up template artifacts only at the end Step 1: Copy the Full Template # Create your paper directory with the complete template cp -r templates/neurips2025/ ~/papers/my-new-paper/ cd ~/papers/my-new-paper/ # Verify structure is complete ls -la # Should see: main.tex, neurips.sty, Makefile, etc. ⚠️ IMPORTANT: Copy the ENTIRE directory, not just main.tex. Templates include: Style files (.sty) - required for compilation Bibliography styles (.bst) - required for references Example content - useful as reference Makefiles - for easy compilation Step 2: Verify Template Compiles First Before making ANY changes, compile the template as-is: # Using latexmk (recommended) latexmk -pdf main.tex # Or manual compilation pdflatex main.tex bibtex main pdflatex main.tex pdflatex main.tex If the unmodified template doesn't compile, fix that first. Common issues: Missing TeX packages → install via tlmgr install Wrong TeX distribution → use TeX Live (recommended) Step 3: Keep Template Content as Reference Don't immediately delete all example content. Instead: % KEEP template examples commented out as you write % This shows you the expected format % Template example (keep for reference): % \begin{figure}[t] % \centering % \includegraphics[width=0.8\linewidth]{example-image} % \caption{Template shows caption style} % \end{figure} % Your actual figure: \begin{figure}[t] \centering \includegraphics[width=0.8\linewidth]{your-figure.pdf} \caption{Your caption following the same style.} \end{figure} Step 4: Replace Content Section by Section Work through the paper systematically: Replacement Order: 1. Title and authors (anonymize for submission) 2. Abstract 3. Introduction 4. Methods 5. Experiments 6. Related Work 7. Conclusion 8. References (your .bib file) 9. Appendix For each section: Read the template's example content Note any special formatting or macros used Replace with your content following the same patterns Compile frequently to catch errors early Step 5: Use Template Macros Templates often define useful macros. Check the preamble for: % Common template macros to use: \newcommand{\method}{YourMethodName} % Consistent method naming \newcommand{\eg}{e.g.,\xspace} % Proper abbreviations \newcommand{\ie}{i.e.,\xspace} \newcommand{\etal}{\textit{et al.}\xspace} Step 6: Clean Up Only at the End Only remove template artifacts when paper is nearly complete: % BEFORE SUBMISSION - remove these: % - Commented-out template examples % - Unused packages % - Template's example figures/tables % - Lorem ipsum or placeholder text % KEEP these: % - All style files (.sty) % - Bibliography style (.bst) % - Required packages from template % - Any custom macros you're using Template Pitfalls to Avoid Pitfall Problem Solution Copying only main.tex Missing .sty, won't compile Copy entire directory Modifying .sty files Breaks conference formatting Never edit style files Adding random packages Conflicts, breaks template Only add if necessary Deleting template content too early Lose formatting reference Keep as comments until done Not compiling frequently Errors accumulate Compile after each section Quick Template Reference ML/AI Conferences Conference Main File Key Style File Notes NeurIPS 2025 main.tex neurips.sty Has Makefile ICML 2026 example_paper.tex icml2026.sty Includes algorithm packages ICLR 2026 iclr2026_conference.tex iclr2026_conference.sty Has math_commands.tex ACL acl_latex.tex acl.sty Strict formatting AAAI 2026 aaai2026-unified-template.tex aaai2026.sty Very strict compliance COLM 2025 colm2025_conference.tex colm2025_conference.sty Similar to ICLR Systems Conferences Conference Main File Key Style File Notes OSDI 2026 main.tex usenix-2020-09.sty USENIX format, 12pp, two-column, 10pt NSDI 2027 main.tex usenix-2020-09.sty Same USENIX format as OSDI ASPLOS 2027 main.tex acmart.cls (sigplan) ACM SIGPLAN format, 12pp SOSP 2026 main.tex acmart.cls (sigplan) ACM SIGPLAN, same as ASPLOS 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 Format Conversion Checklist: - [ ] Step 1: Identify source and target template differences - [ ] Step 2: Create new project with target template - [ ] Step 3: Copy content sections (not preamble) - [ ] Step 4: Adjust page limits and content - [ ] Step 5: Update conference-specific requirements - [ ] Step 6: Verify compilation and formatting Step 1: Key Template Differences ML/AI Conversions From → To Page Change Key Adjustments NeurIPS → ICML 9 → 8 pages Cut 1 page, add Broader Impact if missing ICML → ICLR 8 → 9 pages Can expand experiments, add LLM disclosure NeurIPS → ACL 9 → 8 pages Restructure for NLP conventions, add Limitations ICLR → AAAI 9 → 7 pages Significant cuts needed, strict style adherence Any → COLM varies → 9 Reframe for language model focus Systems Conference Conversions From → To Key Adjustments ML → OSDI/NSDI USENIX template; add system design + implementation sections ML → ASPLOS/SOSP ACM SIGPLAN template; reframe for systems contribution OSDI ↔ SOSP USENIX ↔ ACM SIGPLAN; similar page limits, different style files Full conversion guide: See references/systems-conferences.md for detailed guidance. Step 2: Content Migration (NOT Template Merge) Never copy LaTeX preambles between templates. Instead: # 1. Start fresh with target template cp -r templates/icml2026/ new_submission/ # 2. Copy ONLY content sections from old paper # - Abstract text # - Section content (between \section{} commands) # - Figures and tables # - Bibliography entries # 3. Paste into target template structure Step 3: Adjusting for Page Limits When cutting pages (e.g., NeurIPS 9 → AAAI 7): Move detailed proofs to appendix Condense related work (cite surveys instead of individual papers) Combine similar experiments into unified tables Use smaller figure sizes with subfigures Tighten writing: eliminate redundancy, use active voice When expanding (e.g., ICML 8 → ICLR 9): Add ablation studies reviewers requested Expand limitations discussion Include additional baselines Add qualitative examples Step 4: Conference-Specific Adjustments ML/AI Venues Target Venue Required Additions ICML Broader Impact Statement (after conclusion) ICLR LLM usage disclosure, reciprocal reviewing agreement ACL/EMNLP Limitations section (mandatory), Ethics Statement AAAI Strict adherence to style file (no modifications) NeurIPS Paper checklist (appendix), lay summary if accepted Systems Venues Target Venue Key Required Additions OSDI 2026 Choose Research or Operational Systems track; anonymize system name NSDI 2027 Strong Introduction (prescreening); choose track ASPLOS 2027 Self-contained first 2 pages (rapid review); resubmission note SOSP 2026 ACM SIGPLAN format; optional Artifact Evaluation Full requirements: See references/systems-conferences.md for details. Step 5: Update References % Remove self-citations that reveal identity (for blind review) % Update any "under review" citations to published versions % Add new relevant work published since last submission Step 6: Addressing Previous Reviews When resubmitting after rejection: Do address reviewer concerns in the new version Do add experiments/clarifications reviewers requested Don't include a "changes from previous submission" section (blind review) Don't reference the previous submission or reviews Common Conversion Pitfalls: ❌ Copying \usepackage commands (causes conflicts) ❌ Keeping old conference header/footer commands ❌ Forgetting to update \bibliography{} path ❌ Missing conference-specific required sections ❌ Exceeding page limit after format change 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 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 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: % 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 for complete API documentation. Common Issues and Solutions Issue: Abstract too generic Delete first sentence if it could be prepended to any ML paper. Start with your specific contribution. Issue: Introduction exceeds 1.5 pages Split background into Related Work. Front-load contribution bullets. Methods should start by page 2-3. Issue: Experiments lack explicit claims Add sentence before each experiment: "This experiment tests whether [specific claim]..." Issue: Reviewers find paper hard to follow Add explicit signposting: "In this section, we show X" Use consistent terminology throughout Include figure captions that stand alone Issue: Missing statistical significance Always include: Error bars (specify: std dev or std error) Number of runs Statistical tests if comparing methods Reviewer Evaluation Criteria Reviewers assess papers on four dimensions: Criterion What Reviewers Look For Quality Technical soundness, well-supported claims Clarity Clear writing, reproducible by experts Significance Community impact, advances understanding Originality New insights (doesn't require new method) Scoring (NeurIPS 6-point scale): 6: Strong Accept - Groundbreaking, flawless 5: Accept - Technically solid, high impact 4: Borderline Accept - Solid, limited evaluation 3: Borderline Reject - Solid but weaknesses outweigh 2: Reject - Technical flaws 1: Strong Reject - Known results or ethics issues See references/reviewer-guidelines.md for detailed reviewer instructions. Tables and Figures Tables Use booktabs LaTeX package for professional tables: \usepackage{booktabs} \begin{tabular}{lcc} \toprule Method & Accuracy ↑ & Latency ↓ \ \midrule Baseline & 85.2 & 45ms \ \textbf{Ours} & \textbf{92.1} & 38ms \ \bottomrule \end{tabular} Rules: Bold best value per metric Include direction symbols (↑ higher is better, ↓ lower is better) Right-align numerical columns Consistent decimal precision Figures Vector graphics (PDF, EPS) for all plots and diagrams Raster (PNG 600 DPI) only for photographs Use colorblind-safe palettes (Okabe-Ito or Paul Tol) Verify grayscale readability (8% of men have color vision deficiency) No title inside figure—the caption serves this function Self-contained captions—reader should understand without main text References & Resources Reference Documents (Deep Dives) Document Contents writing-guide.md Gopen & Swan 7 principles, Ethan Perez micro-tips, word choice citation-workflow.md Citation APIs, Python code, BibTeX management checklists.md NeurIPS 16-item, ICML, ICLR, ACL requirements reviewer-guidelines.md Evaluation criteria, scoring, rebuttals systems-conferences.md OSDI/NSDI/ASPLOS/SOSP deadlines, tracks, rules sources.md Complete bibliography of all sources LaTeX Templates Templates in templates/ directory: ML/AI: ICML 2026, ICLR 2026, NeurIPS 2025, ACL/EMNLP, AAAI 2026, COLM 2025 Systems: OSDI 2026, NSDI 2027, ASPLOS 2027, SOSP 2026 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 See templates/README.md for detailed setup instructions. Key External Sources Writing Philosophy: Neel Nanda: How to Write ML Papers - Narrative, "What/Why/So What" Farquhar: How to Write ML Papers - 5-sentence abstract Gopen & Swan: Science of Scientific Writing - 7 reader expectation principles Lipton: Heuristics for Scientific Writing - Word choice Perez: Easy Paper Writing Tips - Micro-level clarity APIs: Semantic Scholar | CrossRef | arXiv ML/AI Venues: NeurIPS | ICML | ICLR | ACL Systems Venues: OSDI 2026 | NSDI 2027 | ASPLOS 2027 | SOSP 2026Weekly Installs381Repositoryzechenzhangagi/…h-skillsGitHub Stars5.1KFirst SeenJan 23, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykWarnInstalled onopencode343gemini-cli319codex316cursor299github-copilot286claude-code253