| 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:
- lock the operating mode from
references/OPERATING-MODES.md,
- understand the repo or draft context,
- use
references/citation-workflow.md as the canonical citation authority,
- 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:
- 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
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 claimsresults/, outputs/, experiments/ - Key findingsconfigs/ - 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:
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:
- Check the claim ledger gate for contribution and result claims
- Write the full first draft end-to-end only for supported claims
- Mark unsupported or speculative claims explicitly
- 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
- Conducting literature reviews and finding related work
- Discovering recent papers in your research area
- 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.
Workflow: Literature Research & Paper Discovery
When conducting literature reviews, finding related work, or discovering recent papers, use this workflow to systematically search, evaluate, and select ML papers.
Workflow 5: Finding and Evaluating Papers
Literature Research Process:
- [ ] Step 1: Define search scope and keywords
- [ ] Step 2: Search arXiv and academic databases
- [ ] Step 3: Screen papers by title/abstract
- [ ] Step 4: Evaluate paper quality (5 dimensions)
- [ ] Step 5: Select top papers and extract citations
- [ ] Step 6: Verify citations programmatically
Step 1: Define Search Scope
Identify specific research areas, methods, or applications:
- Technique-focused:
transformer architecture, graph neural networks, self-supervised learning
- Application-focused:
medical image analysis, reinforcement learning for robotics, language model alignment
- Problem-focused:
out-of-distribution generalization, continual learning, fairness in ML
Step 2: Search arXiv
Use arXiv search with targeted keywords:
URL Pattern:
https://arxiv.org/search/?searchtype=all&query=KEYWORDS&abstracts=show&order=-announced_date_first
Example Searches:
- https://arxiv.org/search/?searchtype=all&query=graph+neural+networks&abstracts=show&order=-announced_date_first
- https://arxiv.org/search/?cat:cs.LG+AND+all:transformer&abstracts=show&order=-announced_date_first
Tips:
- Combine keywords with
+ for AND
- Filter by categories:
cs.LG, cs.AI, cs.CV, cs.CL
- Sort by
announced_date_first for recent papers
- Use Chrome MCP tools when available for automation
Step 3: Screen Papers
Quick screening by title and abstract:
- Relevance to research topic
- Novelty of contribution
- Venue/reputation of authors
- Code availability (check for GitHub links)
Step 4: Evaluate Quality
Use the 5-dimension quality criteria:
| Dimension | Weight | Evaluation Focus |
|---|
| Innovation | 30% | Novelty and originality |
| Method Completeness | 25% | Clarity and reproducibility |
| Experimental Thoroughness | 25% | Validation depth |
| Writing Quality | 10% | Presentation clarity |
| Relevance & Impact | 10% | Domain importance |
Scoring: Rate each dimension 1-5, calculate weighted total
Step 5: Select and Extract
- Rank papers by total score
- Select top papers for detailed review
- Extract metadata: title, authors, arXiv ID, abstract
- Note code repository links
Step 6: Verify Citations
For selected papers, verify citations using Semantic Scholar API:
- Fetch BibTeX programmatically via DOI
- Mark unverified citations as
[CITATION NEEDED]
- Store in bibliography with verification status
When to Use Literature Research
Use this workflow when:
- Starting a new project: Find related work and baselines
- Writing Related Work section: Discover recent papers in your area
- Staying updated: Track recent publications in your field
- Finding baselines: Identify state-of-the-art methods for comparison
- Literature review: Comprehensive survey of research area
Quality Thresholds
- Excellent: 4.0+ (include definitely)
- Good: 3.5-3.9 (include if relevant)
- Fair: 3.0-3.4 (include if highly relevant)
- Poor: <3.0 (exclude unless essential)
Reference Files
For detailed literature research guidance:
references/literature-research/arxiv-search-guide.md - arXiv search strategies and URL patternsreferences/literature-research/paper-quality-criteria.md - Detailed 5-dimension evaluation rubrics
Knowledge Base: Paper-Miner Installed Writing Memory
This skill consumes the active installed writing memory maintained by paper-miner:
references/knowledge/paper-miner-writing-memory.md
This memory belongs to the active installed skill home, not to the source checkout copy.
Even when paper-miner is invoked while working inside a specific repository, it still writes mined writing knowledge only into the active installed skill memory. It does not maintain project-local writing memory unless the user explicitly requests that.
Canonical memory structure
The maintained memory contains these sections:
| Section | Purpose |
|---|
Writing patterns mined | Reusable rhetorical and claim-evidence patterns |
Structure signals | Section flow, paragraph progression, and paper organization signals |
Reusable phrasing | Transition phrases, framing templates, and concise wording |
Venue-specific signals | Visible venue-facing style and convention cues |
How this helps our writing | Practical guidance for future drafts, reports, and rebuttals |
Source index | Source attribution for mined papers |
How the memory is maintained
The paper-miner agent reads papers and merges reusable writing knowledge into this one file:
You: "Learn writing patterns from this paper: path/to/paper.pdf"
↓
paper-miner analyzes the paper
↓
Extracts reusable writing signals
↓
Updates paper-miner-writing-memory.md
↓
ml-paper-writing reuses that memory later
When to use this memory
Use the active installed paper-miner memory when you need:
- structure inspiration for intros, methods, results, or discussion,
- reusable transition phrases or framing templates,
- venue-facing writing signals,
- rebuttal phrasing and response structure ideas,
- examples of how strong papers support and sequence claims.
Default read order
When drafting or revising with ml-paper-writing, read this memory before writing if the task involves:
- introduction framing,
- related work organization,
- method exposition style,
- results narration,
- discussion framing,
- venue-facing polishing.
Use this read order:
references/knowledge/paper-miner-writing-memory.md- repo-local evidence and experiment artifacts
- cited papers or notes if needed
- venue template and formatting constraints
Read narrowly, not exhaustively:
- first scan
How this helps our writing,
- then check
Writing patterns mined and Structure signals,
- then inspect
Reusable phrasing only for concrete wording help,
- use
Venue-specific signals when targeting a known venue.
Contribution rule
Every paper mined by paper-miner should improve the same active installed memory.
Do not scatter newly mined knowledge across multiple maintained files.
Do not create project-specific paper-miner memory.
Do not duplicate near-identical patterns from the same source.
See references/knowledge/README.md for the detailed knowledge-base contract.
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:
For deeper dives into any of these, see:
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
| 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 |
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/ 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
cp -r templates/neurips2025/ ~/papers/my-new-paper/
cd ~/papers/my-new-paper/
ls -la
⚠️ 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:
latexmk -pdf main.tex
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 <package>
- 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
| 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 |
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
| 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 |
Step 2: Content Migration (NOT Template Merge)
Never copy LaTeX preambles between templates. Instead:
cp -r templates/icml2026/ new_submission/
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
| 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 |
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 are a high-risk failure mode. Never write BibTeX from memory.
Canonical authority
Use references/citation-workflow.md as the default authority for citation verification.
The default verification path is:
- Search programmatically with Semantic Scholar / CrossRef / arXiv / OpenAlex when appropriate.
- Verify existence in two sources when the claim is important.
- Retrieve BibTeX programmatically from DOI or a trusted source.
- Validate the claim against the actual paper content when the citation supports a specific statement.
- Add the citation only after the metadata and claim are verified.
The golden rule
IF you cannot verify a citation programmatically:
-> mark it as [CITATION NEEDED] or [PLACEHOLDER - VERIFY]
-> tell the scientist explicitly
-> NEVER invent a plausible-sounding reference
Workflow 2: Adding citations
Citation verification:
- [ ] Step 1: Search with Semantic Scholar / CrossRef / arXiv / OpenAlex as appropriate
- [ ] Step 2: Confirm title, authors, year, and venue
- [ ] Step 3: Retrieve BibTeX from DOI, arXiv, or another trusted export path
- [ ] Step 4: Verify that the claim being cited actually appears in the source
- [ ] Step 5: Add verified BibTeX to the bibliography
- [ ] Step 6: If any step fails -> mark as placeholder and report it explicitly
Discovery vs authority
- Programmatic APIs are the canonical verification path.
- Google Scholar may still be used as a manual discovery surface when coverage is weak, but not as the primary authority.
- If Google Scholar finds something that the canonical APIs do not, treat it as a lead that still requires explicit verification.
Summary: citation rules
| Situation | Action |
|---|
| Verified metadata + verified BibTeX + verified claim | ✅ Use the citation |
| Verified paper exists but the claim was not checked | ⚠️ Use only for general attribution, not for precise technical claims |
| Discovery surface suggests a paper but metadata is still weak | ⚠️ Keep as lead, not as final citation |
| Cannot verify programmatically | ❌ Mark [CITATION NEEDED], inform the scientist |
🚨 NEVER generate BibTeX from memory. Use the programmatic workflow in references/citation-workflow.md. 🚨
Complete Citation Workflow Example
Scenario: You need to cite the Transformer paper.
Step 1: Search programmatically
- Semantic Scholar query: "Attention is All You Need Vaswani 2017"
- Result: title, authors, year, and DOI align
Step 2: Verify existence
- CrossRef confirms DOI metadata
- Semantic Scholar record matches the same paper
Step 3: Retrieve BibTeX
- Fetch BibTeX from the DOI / trusted export path
Step 4: Verify the claim
- Read the abstract or paper section that supports the cited statement
- Confirm that the claim being cited is actually present
Step 5: Add to bibliography
- Paste verified BibTeX into the .bib file
- Cite with the verified key
Step 6: If any step fails
- mark the citation as [PLACEHOLDER - VERIFY]
- tell the scientist explicitly what remains unverified
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
If the task is to generate or redesign paper-ready figures/tables themselves, use publication-chart-skill; ml-paper-writing stays responsible for caption quality, placement, storyline, and paper integration.
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 |
| sources.md | Complete bibliography of all sources |
| Literature Research: | |
| arxiv-search-guide.md | arXiv search strategies, URL patterns, Chrome MCP automation |
| paper-quality-criteria.md | 5-dimension paper evaluation rubrics (innovation, method, experiments, writing, impact) |
LaTeX Templates
Templates in templates/ directory: ICML 2026, ICLR 2026, NeurIPS 2025, ACL/EMNLP, AAAI 2026, COLM 2025.
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:
APIs: Semantic Scholar | CrossRef | arXiv
Venues: NeurIPS | ICML | ICLR | ACL
