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:

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

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):

What you achieved: "We introduce...", "We prove...", "We demonstrate..."
Why this is hard and important
How you do it (with specialist keywords for discoverability)
What evidence you have
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

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

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:

Title and authors (anonymize for submission)
Abstract
Introduction
Methods
Experiments
Related Work
Conclusion
References (your .bib file)
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:

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

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

sources.md Complete bibliography of all sources

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

ml-paper-writing

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