| 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, structuring arguments, verifying citations, or preparing camera-ready submissions. For systems venues (OSDI, NSDI, ASPLOS, SOSP), use systems-paper-writing instead. |
| version | 1.2.0 |
| 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, COLM. This skill combines writing philosophy from top researchers (Nanda, Farquhar, Karpathy, Lipton, Steinhardt) with practical tools: LaTeX templates, citation verification APIs, and conference checklists.
For systems venues (OSDI, NSDI, ASPLOS, SOSP), use the systems-paper-writing skill, which provides paragraph-level structural blueprints, writing patterns, venue-specific checklists, and LaTeX templates for systems conferences.
Core Philosophy: Collaborative Writing
Paper writing is collaborative, but Claude should be proactive in delivering drafts.
The typical workflow starts with a research repository containing code, results, and experimental artifacts. Claude's role is to:
- 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:
- Write the full first draft end-to-end
- Present the complete draft for feedback
- Iterate based on scientist's response
If genuinely uncertain about framing or major claims:
- Draft what you can confidently
- Flag specific uncertainties: "I framed X as the main contribution—let me know if you'd prefer to emphasize Y instead"
- Continue with the draft rather than blocking
Questions to include with the draft (not before):
- "I emphasized X as the main contribution—adjust if needed"
- "I highlighted results A, B, C—let me know if others are more important"
- "Related work section includes [papers]—add any I missed"
When to Use This Skill
Use this skill when:
- Starting from a research repo to write a paper
- Drafting or revising specific sections
- Finding and verifying citations for related work
- Formatting for conference submission
- Resubmitting to a different venue (format conversion)
- Iterating on drafts with scientist feedback
Always remember: First drafts are starting points for discussion, not final outputs.
Balancing Proactivity and Collaboration
Default: Be proactive. Deliver drafts, then iterate.
| Confidence Level | Action |
|---|
| High (clear repo, obvious contribution) | Write full draft, deliver, iterate on feedback |
| Medium (some ambiguity) | Write draft with flagged uncertainties, continue |
| Low (major unknowns) | Ask 1-2 targeted questions, then draft |
Draft first, ask with the draft (not before):
| Section | Draft Autonomously | Flag With Draft |
|---|
| Abstract | Yes | "Framed contribution as X—adjust if needed" |
| Introduction | Yes | "Emphasized problem Y—correct if wrong" |
| Methods | Yes | "Included details A, B, C—add missing pieces" |
| Experiments | Yes | "Highlighted results 1, 2, 3—reorder if needed" |
| Related Work | Yes | "Cited papers X, Y, Z—add any I missed" |
Only block for input when:
- Target venue is unclear (affects page limits, framing)
- Multiple contradictory framings seem equally valid
- Results seem incomplete or inconsistent
- Explicit request to review before continuing
Don't block for:
- Word choice decisions
- Section ordering
- Which specific results to show (make a choice, flag it)
- Citation completeness (draft with what you find, note gaps)
The Narrative Principle
The single most critical insight: Your paper is not a collection of experiments—it's a story with one clear contribution supported by evidence.
Every successful ML paper centers on what Neel Nanda calls "the narrative": a short, rigorous, evidence-based technical story with a takeaway readers care about.
Three Pillars (must be crystal clear by end of introduction):
| Pillar | Description | Example |
|---|
| The What | 1-3 specific novel claims within cohesive theme | "We prove that X achieves Y under condition Z" |
| The Why | Rigorous empirical evidence supporting claims | Strong baselines, experiments distinguishing hypotheses |
| The So What | Why readers should care | Connection to recognized community problems |
If you cannot state your contribution in one sentence, you don't yet have a paper.
Paper Structure Workflow
Workflow 1: Writing a Complete Paper (Iterative)
Copy this checklist and track progress. Each step involves drafting → feedback → revision:
Paper Writing Progress:
- [ ] Step 1: Define the one-sentence contribution (with scientist)
- [ ] Step 2: Draft Figure 1 → get feedback → revise
- [ ] Step 3: Draft abstract → get feedback → revise
- [ ] Step 4: Draft introduction → get feedback → revise
- [ ] Step 5: Draft methods → get feedback → revise
- [ ] Step 6: Draft experiments → get feedback → revise
- [ ] Step 7: Draft related work → get feedback → revise
- [ ] Step 8: Draft limitations → get feedback → revise
- [ ] Step 9: Complete paper checklist (required)
- [ ] Step 10: Final review cycle and submission
Step 1: Define the One-Sentence Contribution
This step requires explicit confirmation from the scientist.
Before writing anything, articulate and verify:
- What is the single thing your paper contributes?
- What was not obvious or present before your work?
"I propose framing the contribution as: '[one sentence]'. Does this capture
what you see as the main takeaway? Should we adjust the emphasis?"
Step 2: Draft Figure 1
Figure 1 deserves special attention—many readers skip directly to it.
- Convey core idea, approach, or most compelling result
- Use vector graphics (PDF/EPS for plots)
- Write captions that stand alone without main text
- Ensure readability in black-and-white (8% of men have color vision deficiency)
Step 3: Write Abstract (5-Sentence Formula)
From Sebastian Farquhar (DeepMind):
1. What you achieved: "We introduce...", "We prove...", "We demonstrate..."
2. Why this is hard and important
3. How you do it (with specialist keywords for discoverability)
4. What evidence you have
5. Your most remarkable number/result
Delete generic openings like "Large language models have achieved remarkable success..."
Step 4: Write Introduction (1-1.5 pages max)
Must include:
- 2-4 bullet contribution list (max 1-2 lines each in two-column format)
- Clear problem statement
- Brief approach overview
- Methods should start by page 2-3 maximum
Step 5: Methods Section
Enable reimplementation:
- Conceptual outline or pseudocode
- All hyperparameters listed
- Architectural details sufficient for reproduction
- Present final design decisions; ablations go in experiments
Step 6: Experiments Section
For each experiment, explicitly state:
- What claim it supports
- How it connects to main contribution
- Experimental setting (details in appendix)
- What to observe: "the blue line shows X, which demonstrates Y"
Requirements:
- Error bars with methodology (standard deviation vs standard error)
- Hyperparameter search ranges
- Compute infrastructure (GPU type, total hours)
- Seed-setting methods
Step 7: Related Work
Organize methodologically, not paper-by-paper:
Good: "One line of work uses Floogledoodle's assumption [refs] whereas we use Doobersnoddle's assumption because..."
Bad: "Snap et al. introduced X while Crackle et al. introduced Y."
Cite generously—reviewers likely authored relevant papers.
Step 8: Limitations Section (REQUIRED)
All major conferences require this. Counter-intuitively, honesty helps:
- Reviewers are instructed not to penalize honest limitation acknowledgment
- Pre-empt criticisms by identifying weaknesses first
- Explain why limitations don't undermine core claims
Step 9: Paper Checklist
NeurIPS, ICML, and ICLR all require paper checklists. See references/checklists.md.
Writing Philosophy for Top ML Conferences
This section distills the most important writing principles from leading ML researchers. These aren't optional style suggestions—they're what separates accepted papers from rejected ones.
"A paper is a short, rigorous, evidence-based technical story with a takeaway readers care about." — Neel Nanda
The Sources Behind This Guidance
This skill synthesizes writing philosophy from researchers who have published extensively at top venues:
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
ML/AI Conferences
| Conference | Page Limit | Extra for Camera-Ready | Key Requirement |
|---|
| NeurIPS 2025 | 9 pages | +0 | Mandatory checklist, lay summary for accepted |
| ICML 2026 | 8 pages | +1 | Broader Impact Statement required |
| ICLR 2026 | 9 pages | +1 | LLM disclosure required, reciprocal reviewing |
| ACL 2025 | 8 pages (long) | varies | Limitations section mandatory |
| AAAI 2026 | 7 pages | +1 | Strict style file adherence |
| COLM 2025 | 9 pages | +1 | Focus on language models |
Systems Conferences (OSDI, NSDI, ASPLOS, SOSP): See the systems-paper-writing skill for page limits, templates, deadlines, and submission rules.
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
ML/AI Conferences
| Conference | Main File | Key Style File | Notes |
|---|
| NeurIPS 2025 | main.tex | neurips.sty | Has Makefile |
| ICML 2026 | example_paper.tex | icml2026.sty | Includes algorithm packages |
| ICLR 2026 | iclr2026_conference.tex | iclr2026_conference.sty | Has math_commands.tex |
| ACL | acl_latex.tex | acl.sty | Strict formatting |
| AAAI 2026 | aaai2026-unified-template.tex | aaai2026.sty | Very strict compliance |
| COLM 2025 | colm2025_conference.tex | colm2025_conference.sty | Similar to ICLR |
Systems Conference Templates (OSDI, NSDI, ASPLOS, SOSP): See the systems-paper-writing skill.
Conference Resubmission & Format Conversion
When a paper is rejected or withdrawn from one venue and resubmitted to another, format conversion is required. This is a common workflow in ML research.
Workflow 3: Converting Between Conference Formats
Format Conversion Checklist:
- [ ] Step 1: Identify source and target template differences
- [ ] Step 2: Create new project with target template
- [ ] Step 3: Copy content sections (not preamble)
- [ ] Step 4: Adjust page limits and content
- [ ] Step 5: Update conference-specific requirements
- [ ] Step 6: Verify compilation and formatting
Step 1: Key Template Differences
ML/AI Conversions
| From → To | Page Change | Key Adjustments |
|---|
| NeurIPS → ICML | 9 → 8 pages | Cut 1 page, add Broader Impact if missing |
| ICML → ICLR | 8 → 9 pages | Can expand experiments, add LLM disclosure |
| NeurIPS → ACL | 9 → 8 pages | Restructure for NLP conventions, add Limitations |
| ICLR → AAAI | 9 → 7 pages | Significant cuts needed, strict style adherence |
| Any → COLM | varies → 9 | Reframe for language model focus |
ML → Systems Conversion: When converting to OSDI, NSDI, ASPLOS, or SOSP, see the systems-paper-writing skill for format conversion guidance, templates, and structural differences.
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
ML/AI Venues
| Target Venue | Required Additions |
|---|
| ICML | Broader Impact Statement (after conclusion) |
| ICLR | LLM usage disclosure, reciprocal reviewing agreement |
| ACL/EMNLP | Limitations section (mandatory), Ethics Statement |
| AAAI | Strict adherence to style file (no modifications) |
| NeurIPS | Paper checklist (appendix), lay summary if accepted |
Systems Venues (OSDI, NSDI, ASPLOS, SOSP): See the systems-paper-writing skill for venue-specific requirements, checklists, and reviewer guidelines.
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
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
Citing AI Research Skills
If this library helped with your research — whether for training pipelines, evaluation, paper writing, or any other skill — please consider citing it in your acknowledgments or references:
@software{ai_research_skills,
title = {AI Research Skills Library},
author = {{Orchestra Research}},
year = {2025},
url = {https://github.com/orchestra-research/AI-research-SKILLs},
note = {Open-source skills library enabling AI agents to autonomously conduct AI research}
}
A brief mention in your Acknowledgments section is also welcome:
\section*{Acknowledgments}
We used the AI Research Skills Library~\cite{ai_research_skills} for [experiment orchestration / evaluation / ...].
References & Resources
Reference Documents (Deep Dives)
LaTeX Templates
Templates in templates/ directory:
- ML/AI: ICML 2026, ICLR 2026, NeurIPS 2025, ACL/EMNLP, AAAI 2026, COLM 2025
- Systems (OSDI, NSDI, ASPLOS, SOSP): See systems-paper-writing skill
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
ML/AI Venues: NeurIPS | ICML | ICLR | ACL
Systems Venues: See the systems-paper-writing skill for OSDI, NSDI, ASPLOS, SOSP links and guides
