ml-paper-writing

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.

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.

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⚠️ 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:

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
• 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 patterns
• references/literature-research/paper-quality-criteria.md - Detailed 5-dimension evaluation rubrics

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Knowledge Base: Writing Patterns from ML Papers

This skill maintains a curated knowledge base of writing patterns, techniques, and requirements extracted from successful ML conference papers. The knowledge base grows as you analyze more papers.

Knowledge Organization

The knowledge base is organized into 4 categories at references/knowledge/:

Category	File	Contents
Structure	structure.md	Paper organization, IMRaD patterns, transitions, section flow
Writing Techniques	writing-techniques.md	Sentence patterns, transition phrases, clarity techniques
Submission Guides	submission-guides.md	Venue requirements (NeurIPS, ICML, ICLR, ACL, AAAI, COLM)
Review Response	review-response.md	Rebuttal strategies, addressing reviewer comments

How the Knowledge Base is Maintained

The paper-miner agent automatically extracts and categorizes writing knowledge from papers you provide:

You: "Learn writing techniques from this NeurIPS paper: path/to/paper.pdf"
↓
paper-miner analyzes the paper
↓
Extracts patterns → Categorizes into 4 types → Updates knowledge files
↓
Knowledge grows with each paper analyzed

What gets extracted:

• Structure patterns: How successful papers organize sections, transition between topics
• Writing techniques: Sentence templates, transition phrases, clarity methods
• Venue requirements: Page limits, required sections, formatting rules
• Rebuttal strategies: How to respond to specific reviewer concerns

When to Use the Knowledge Base

For writing patterns:

• Stuck on how to phrase a transition? Check writing-techniques.md
• Need structure inspiration? Browse structure.md
• Writing rebuttal? Consult review-response.md

For venue requirements:

• Submitting to NeurIPS? See submission-guides.md for checklist
• Converting between venues? Compare page limits and requirements
• Unsure about required sections? Each venue has specific requirements

Contributing to the Knowledge Base

Every paper you analyze makes the knowledge base richer for future use:

# Trigger paper-miner from any context
"Extract writing patterns from this paper: path/to/paper.pdf"
"Analyze structure of https://arxiv.org/abs/2301.xxxxx"
"What writing techniques does this ICLR paper use?"

The paper-miner agent:

1. Extracts paper content (PDF, DOCX, or arXiv link)
2. Analyzes IMRaD structure and writing patterns
3. Identifies venue-specific requirements
4. Updates appropriate knowledge files with new patterns
5. Reports what was added with source attribution

Knowledge Base Principles

Actionable patterns only: Each entry provides reusable techniques with examples.

Source attribution: Every pattern cites the paper it came from for traceability.

No duplicates: Checks existing content before adding new patterns.

Quality over quantity: Focus on techniques that work, not comprehensive lists.

See references/knowledge/README.md for complete knowledge base documentation.

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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)

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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.

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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:

1. The abstract
2. The introduction
3. The figures
4. 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.

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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.

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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 <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:

1. Read the template's example content
2. Note any special formatting or macros used
3. Replace with your content following the same patterns
4. 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

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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:

# 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

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

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Citation Workflow (Hallucination Prevention)

⚠️ CRITICAL: AI-generated citations have ~40% error rate. Never write BibTeX from memory.

The Golden Rule

IF you cannot verify a citation through web search:
    → Mark it as [CITATION NEEDED] or [PLACEHOLDER - VERIFY]
    → Tell the scientist explicitly
    → NEVER invent a plausible-sounding reference

MANDATORY: Use WebSearch tool to verify EVERY citation before adding to bibliography.

Workflow 2: Adding Citations

Citation Verification (MANDATORY for every citation):
- [ ] Step 1: Use WebSearch to find the paper
- [ ] Step 2: Verify paper exists on Google Scholar
- [ ] Step 3: Confirm paper details (title, authors, year, venue)
- [ ] Step 4: Retrieve BibTeX from Google Scholar or DOI
- [ ] Step 5: Verify the claim you're citing actually appears in the paper
- [ ] Step 6: Add verified BibTeX to bibliography
- [ ] Step 7: If ANY step fails → mark as placeholder, inform scientist

Step 1: Use WebSearch to Find the Paper

When you need to cite a paper, ALWAYS start with web search:

WebSearch query examples:
- "Attention is All You Need Vaswani 2017"
- "RLHF language model alignment 2023"
- "sparse autoencoders interpretability Anthropic"
- "transformer architecture NeurIPS"

What to look for in search results:

• Paper title matches your intended citation
• Authors are correct
• Publication year is correct
• Venue (conference/journal) is identified

Step 2: Verify on Google Scholar

After finding the paper, verify it exists on Google Scholar:

WebSearch query: "site:scholar.google.com [paper title] [first author]"

Example: "site:scholar.google.com Attention is All You Need Vaswani"

Verification checklist:

• ✅ Paper appears in Google Scholar results
• ✅ Title matches exactly (or very close)
• ✅ Authors match
• ✅ Year matches
• ✅ Venue is listed (conference/journal)
• ✅ Citation count is reasonable (not 0 for old papers)

If paper NOT found on Google Scholar:

• ❌ STOP - Do not cite
• Mark as [CITATION NEEDED - not found on Google Scholar]
• Inform scientist explicitly

Step 3: Confirm Paper Details

Before retrieving BibTeX, double-check all details:

Verification checklist:
- Title: [exact title from Google Scholar]
- Authors: [all authors, in order]
- Year: [publication year]
- Venue: [conference/journal name]
- DOI: [if available]

Step 4: Retrieve BibTeX

Option 1: From Google Scholar (Recommended)

1. Find the paper on Google Scholar
2. Click "Cite" button below the paper
3. Select "BibTeX" format
4. Copy the BibTeX entry

Option 2: From DOI (if available)

1. Use WebSearch to find: "doi.org/[DOI]"
2. Look for BibTeX export option on the publisher's page
3. Copy the BibTeX entry

Option 3: From arXiv (for preprints)

1. Find paper on arXiv
2. Click "Export BibTeX Citation" on the right sidebar
3. Copy the BibTeX entry

CRITICAL: Never write BibTeX from memory. Always copy from verified source.

Step 5: Verify the Claim

Before citing for a specific claim, verify the claim actually appears in the paper:

Verification process:
1. Use WebSearch to access the paper (PDF or HTML)
2. Search for keywords related to your claim
3. Confirm the claim is explicitly stated or clearly implied
4. Note the section/page where claim appears

If you cannot access the paper:

• ❌ Do not cite for specific claims
• Only cite for general contributions (if verified on Google Scholar)
• Mark as [CLAIM NOT VERIFIED - no access to paper]

Step 6: Add Verified BibTeX to Bibliography

Only after completing all verification steps:

% Add to your .bib file
@inproceedings{vaswani2017attention,
  title={Attention is All You Need},
  author={Vaswani, Ashish and Shazeer, Noam and ...},
  booktitle={Advances in Neural Information Processing Systems},
  year={2017}
}

% Use in your paper
\cite{vaswani2017attention}

Step 7: 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 - not found on Google Scholar
• Jones 2022 on scaling laws - found similar paper but different authors Please verify these before submission."

Summary: Citation Rules

Situation	Action
Found on Google Scholar, verified details, got BibTeX	✅ Use the citation
Found paper, verified on Google Scholar, no BibTeX	✅ Create BibTeX from Google Scholar info
Paper exists but details don't match	⚠️ Mark placeholder, inform scientist
Not found on Google Scholar	❌ 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 verify through WebSearch and Google Scholar. 🚨

Complete Citation Workflow Example

Scenario: You need to cite the Transformer paper.

Step 1: WebSearch
Query: "Attention is All You Need Vaswani 2017"
Result: Found paper on multiple sources

Step 2: Google Scholar Verification
Query: "site:scholar.google.com Attention is All You Need Vaswani"
Result: ✅ Paper found, 50,000+ citations, NeurIPS 2017

Step 3: Confirm Details
- Title: "Attention is All You Need"
- Authors: Vaswani, Ashish; Shazeer, Noam; Parmar, Niki; ...
- Year: 2017
- Venue: NeurIPS (NIPS)
- DOI: Available

Step 4: Retrieve BibTeX
- Click "Cite" on Google Scholar
- Select BibTeX format
- Copy entry

Step 5: Verify Claim
- Access paper via WebSearch
- Confirm claim appears in paper
- Note section/page

Step 6: Add to Bibliography
- Paste BibTeX to .bib file
- Use \cite{vaswani2017attention} in paper

Step 7: Success
- Citation verified and added
- No placeholder needed

---

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
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:

• 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

Venues: NeurIPS | ICML | ICLR | ACL