paper-writing

name	paper-writing
description	Write publication-ready ML/AI/Systems conference papers (NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP). Use when the user requests a conference manuscript, needs conference-specific templates/checklists, or format conversion between venues. For journal articles or technical reports, use scientific-writing instead.
category	Writing & Review
depends	["rewrite-humanize"]
tags	["Academic Writing","NeurIPS","ICML","ICLR","ACL","AAAI","COLM","OSDI","NSDI","ASPLOS","SOSP","LaTeX","Paper Writing","Citations","Research","Systems"]
triggers	["write paper","conference paper","NeurIPS","ICML","ICLR","ACL","AAAI","COLM","OSDI","NSDI","ASPLOS","SOSP","submit paper","draft paper","LaTeX paper","写论文","投稿","会议论文"]

ML Paper Writing for Top AI & Systems Conferences

Expert-level guidance for writing publication-ready papers targeting NeurIPS, ICML, ICLR, ACL, AAAI, COLM (ML/AI venues) and OSDI, NSDI, ASPLOS, SOSP (Systems venues). This skill combines writing philosophy from top researchers (Nanda, Farquhar, Karpathy, Lipton, Steinhardt) with practical tools: LaTeX templates, citation verification, and conference checklists.

Overview

This skill covers the full lifecycle of conference paper writing: from assembling research findings into a narrative, through drafting each section, to citation verification and conference-specific formatting. It is designed for CS/AI/Systems conferences with double-blind review, strict page limits, and LaTeX requirements.

When to Use This Skill

• The user explicitly requests a conference paper (e.g., "write a NeurIPS submission", "prepare an OSDI paper").
• You are working on a writing task targeting a specific CS/AI/Systems conference.
• You need conference-specific LaTeX templates, formatting checklists, or reviewer criteria.
• You are converting a manuscript between conference formats (e.g., NeurIPS → ICML resubmission).

Do NOT use this skill when:

• Research is still in progress — finish experiments and analysis first.
• You need a literature survey — use the literature-search tool.
• You need to evaluate, sharpen, or brainstorm research directions — use research-strategy.
• You already have a draft and want to strategically revise it (reframe the contribution, strengthen evidence, prepare reviewer defense, unify the narrative) — use paper-revision instead. That skill covers framing diagnosis, claim crystallization, and reviewer-adversarial revision, which are fundamentally different from first-draft writing.
• You are writing a journal article (Nature, Science, NEJM, etc.), technical report, or research summary — use scientific-writing instead, which covers IMRAD structure, journal-specific citation styles (APA/AMA/Vancouver), and reporting guidelines (CONSORT/STROBE/PRISMA).

paper-writing vs scientific-writing: Which to Use

Target Output	Use This Skill	Why
CS/AI/Systems conference paper (NeurIPS, ICML, OSDI, etc.)	paper-writing	Conference-specific templates, ML writing philosophy, reviewer guidelines, page limits
Journal article (Nature, Science, PNAS, NEJM, etc.)	scientific-writing	IMRAD structure, journal citation styles, reporting guidelines, discipline-specific terminology
Technical report, white paper, grant report	scientific-writing	Professional report formatting

If the venue is ambiguous, ask the user before proceeding.

Do Not Reuse This Structure for Talks

This skill's narrative serves reviewers: contribution claims, evidence, novelty, and venue expectations. Do not directly reuse its paper structure for slides or public/mixed-audience talks. Talks need an audience-facing story spine before contribution claims, frameworks, or result lists. For Marp decks, use academic-marp-slides.

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CRITICAL: Never Hallucinate Citations

This is the most important rule in academic writing.

The Problem

AI-generated citations have a ~40% error rate. Hallucinated references — papers that don't exist, wrong authors, incorrect years, fabricated DOIs — are serious academic misconduct that can result in desk rejection or retraction.

The Rule

NEVER generate BibTeX entries from memory. ALWAYS verify programmatically.

Action	Correct	Wrong
Adding a citation	Search via literature-search or web-search → 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 Cannot Verify a Citation

% EXPLICIT PLACEHOLDER - requires user verification
\cite{PLACEHOLDER_author2024_verify_this}  % TODO: Verify this citation exists

Flag all placeholder citations when presenting the draft to the user.

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Workflow: From Research to Paper

Phase 0: Assemble the Narrative

Before writing, gather your materials:

1. Review existing artifacts: Search note and data artifacts in the workspace for key findings, experimental results, and conclusions from prior research sessions.
2. Identify contribution claims: What changed in understanding as a result of this research? These become the paper's contribution claims.
3. Collect supporting evidence: Identify key experimental results, figures, and data that support each claim.
4. Review collected literature: Use literature-search and check existing paper artifacts. These become Related Work citations.
5. Identify the target venue: If the user specified a venue, use that. If not, propose a venue with rationale and ask for confirmation.

Save a synthesis document as a note artifact before starting to write.

Phase 1: Define the One-Sentence Contribution

Distill the accumulated findings into a single contribution statement:

• What is the single thing this research contributes?
• What was not obvious or present before this work?

Write this as the first line of the paper's working document. If the contribution is unclear from the evidence, flag this to the user — the research may not be ready for a paper yet.

Phase 2: Draft the Paper

Write sections as .tex files in the workspace using the appropriate conference template.

Writing order:

1. Copy conference template to workspace (from @skill/templates/)
2. Draft Figure 1 — core idea or most compelling result
3. Draft Abstract (5-sentence formula)
4. Draft Introduction (1-1.5 pages max)
5. Draft Methods / System Design
6. Draft Experiments / Evaluation
7. Draft Related Work (from collected literature)
8. Draft Limitations
9. Complete conference checklist
10. Self-review pass

Phase 3: Citation Assembly

Build the bibliography from verified sources:

1. Primary source: Paper artifacts already collected via literature-search across sessions. These are pre-verified.
2. Fill gaps: Use literature-search and web-search for any additional citations needed (e.g., baselines mentioned in experiments, recent concurrent work).
3. Verify all entries: Every citation in the .bib file must have a DOI or arXiv ID. No exceptions.
4. Mark unknowns: If a citation cannot be verified, mark it as [PLACEHOLDER - VERIFY] and flag it explicitly.

See @skill/references/citation-workflow.md for API details if you need to verify entries beyond what the tools provide.

Phase 4: Review and Deliver

Once the full draft is complete:

1. Perform a self-review against the checklist below
2. Save the .tex file as an artifact
3. Present the draft to the user with:
   • Summary of the paper's contribution and target venue
   • List of any placeholder citations requiring verification
   • Key areas where user input is needed

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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
The What	1-3 specific novel claims within a cohesive theme
The Why	Rigorous empirical evidence supporting claims
The So What	Why readers should care

If you cannot state your contribution in one sentence, you don't yet have a paper.

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Paper Structure Guide

Writing the 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..."

Writing the 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

Writing the Methods Section

Enable reimplementation:

• Conceptual outline or pseudocode
• All hyperparameters listed
• Architectural details sufficient for reproduction
• Present final design decisions; ablations go in experiments

Writing the 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

Writing 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."

Draw primarily from collected paper artifacts. Cite generously — reviewers likely authored relevant papers.

Writing the 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

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Writing Philosophy for Top ML Conferences

The Sources Behind This Guidance

Source	Key Contribution
Neel Nanda (Google DeepMind)	The Narrative Principle, What/Why/So What framework
Sebastian Farquhar (DeepMind)	5-sentence abstract formula
Gopen & Swan	7 principles of reader expectations
Zachary Lipton	Word choice, eliminating hedging
Jacob Steinhardt (UC Berkeley)	Precision, consistent terminology
Ethan Perez (Anthropic)	Micro-level clarity tips
Andrej Karpathy	Single contribution focus

For deeper dives: See @skill/references/writing-guide.md and @skill/references/sources.md.

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

Explain, Don't Defend

Write to explain an idea to a colleague, not to defend it against an imagined reviewer. Diagnostic: "Would a human author actually feel the need to say this, or am I performing rigor?" If the latter, cut it.

Cut: defensive X is not Y framings, disqualification clauses (an X without these properties does not qualify), defensive scope statements (this is not a report of X), posturing connectives (crucially, importantly, it is worth noting, we draw a sharp boundary), and em-dash asides that interrupt one idea with another. Prefer plain declarative sentences and neutral connectives (here, to be specific, in this work).

For the full catalog and rewrite workflow, load rewrite-humanize and see @rewrite-humanize/references/lexicon.md. This complements (not replaces) the principles below.

Sentence-Level Clarity (Gopen & Swan's 7 Principles)

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 @skill/references/writing-guide.md.

Micro-Level Tips (Ethan Perez)

• 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"

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

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Conference Requirements Quick Reference

ML/AI Conferences

Conference	Page Limit	Extra for Camera-Ready	Key Requirement
NeurIPS 2025	9 pages	+0	Mandatory checklist, lay summary for accepted
ICML 2026	8 pages	+1	Broader Impact Statement required
ICLR 2026	9 pages	+1	LLM disclosure required, reciprocal reviewing
ACL 2025	8 pages (long)	varies	Limitations section mandatory
AAAI 2026	7 pages	+1	Strict style file adherence
COLM 2025	9 pages	+1	Focus on language models

Systems Conferences

Conference	Page Limit	Extra for Camera-Ready	Key Requirement	Template
OSDI 2026	12 pages	+2 (14 pages)	Research + Operational Systems tracks	USENIX
NSDI 2027	12 pages	varies	Prescreening via Introduction; 3 tracks	USENIX
ASPLOS 2027	12 pages (ACM)	varies	Rapid review on first 2 pages; dual cycles	ACM SIGPLAN
SOSP 2026	12 pages	varies	Optional artifact evaluation; author response	ACM SIGPLAN

Detailed Systems conference info: See @skill/references/systems-conferences.md.

Universal Requirements:

• Double-blind review (anonymize submissions)
• References don't count toward page limit
• Appendices unlimited but reviewers not required to read
• LaTeX required for all venues
• Systems venues: USENIX uses custom .sty; ACM uses acmart.cls

LaTeX Templates: See @skill/templates/ directory for all conference templates.

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Using LaTeX Templates

Setting Up from Template

Always copy the entire template directory first, then write within it.

# Copy template to workspace
cp -r @skill/templates/neurips2025/ paper/
cd paper/

# Verify template compiles as-is before any changes
latexmk -pdf main.tex

Copy the ENTIRE directory, not just main.tex. Templates include style files (.sty), bibliography styles (.bst), and Makefiles.

Template Quick Reference

ML/AI Conferences

Conference	Main File	Key Style File
NeurIPS 2025	main.tex	neurips.sty
ICML 2026	example_paper.tex	icml2026.sty
ICLR 2026	iclr2026_conference.tex	iclr2026_conference.sty
ACL	acl_latex.tex	acl.sty
AAAI 2026	aaai2026-unified-template.tex	aaai2026.sty
COLM 2025	colm2025_conference.tex	colm2025_conference.sty

Systems Conferences

Conference	Main File	Key Style File
OSDI 2026	main.tex	usenix-2020-09.sty
NSDI 2027	main.tex	usenix-2020-09.sty
ASPLOS 2027	main.tex	acmart.cls (sigplan)
SOSP 2026	main.tex	acmart.cls (sigplan)

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
Not compiling frequently	Errors accumulate	Compile after each section

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Conference Resubmission & Format Conversion

When a paper is rejected or withdrawn from one venue and resubmitted to another.

Key Template Differences

ML/AI Conversions

From → To	Page Change	Key Adjustments
NeurIPS → ICML	9 → 8 pages	Cut 1 page, add Broader Impact if missing
ICML → ICLR	8 → 9 pages	Can expand experiments, add LLM disclosure
NeurIPS → ACL	9 → 8 pages	Restructure for NLP conventions, add Limitations
ICLR → AAAI	9 → 7 pages	Significant cuts needed, strict style adherence
Any → COLM	varies → 9	Reframe for language model focus

Systems Conference Conversions

From → To	Key Adjustments
ML → OSDI/NSDI	USENIX template; add system design + implementation sections
ML → ASPLOS/SOSP	ACM SIGPLAN template; reframe for systems contribution
OSDI ↔ SOSP	USENIX ↔ ACM SIGPLAN; similar page limits, different style files

Full conversion guide: See @skill/references/systems-conferences.md.

Content Migration (NOT Template Merge)

Never copy LaTeX preambles between templates. Instead:

1. Start fresh with target template
2. Copy ONLY content sections from old paper (between \section{} commands)
3. Copy figures, tables, bibliography entries
4. Paste into target template structure

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

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Self-Review Checklist

Before presenting the draft to the user, verify:

Narrative:

• Can state contribution in one sentence
• Three pillars (What/Why/So What) clear in intro
• Every experiment supports a specific claim

Structure:

• Abstract follows 5-sentence formula
• Introduction ≤ 1.5 pages
• Methods start by page 2-3
• 2-4 contribution bullets included
• Limitations section present

Writing:

• Consistent terminology throughout
• No generic opening sentences
• Hedging removed unless necessary
• All figures have self-contained captions

Technical:

• All citations verified via literature-search (no memory-generated BibTeX)
• Error bars included with methodology
• Compute resources documented
• Code/data availability stated

Conference-specific:

• Correct template used
• Within page limit
• Double-blind anonymization
• Required checklist completed (see @skill/references/checklists.md)

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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 @skill/references/reviewer-guidelines.md for detailed reviewer instructions.

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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

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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.

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References & Resources

Reference Documents (Deep Dives)

Document	Contents
@skill/references/writing-guide.md	Gopen & Swan 7 principles, Ethan Perez micro-tips, word choice
@skill/references/citation-workflow.md	Citation APIs, Python code, BibTeX management
@skill/references/checklists.md	NeurIPS 16-item, ICML, ICLR, ACL requirements
@skill/references/reviewer-guidelines.md	Evaluation criteria, scoring, rebuttals
@skill/references/systems-conferences.md	OSDI/NSDI/ASPLOS/SOSP deadlines, tracks, rules
@skill/references/sources.md	Complete bibliography of all sources

LaTeX Templates

Templates in @skill/templates/ directory:

• ML/AI: ICML 2026, ICLR 2026, NeurIPS 2025, ACL/EMNLP, AAAI 2026, COLM 2025
• Systems: OSDI 2026, NSDI 2027, ASPLOS 2027, SOSP 2026

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