name: academic-paper-writing description: | Guides AI agents to write high-quality academic papers following top-tier conference standards.

Use this skill when:

• Writing or drafting academic papers for AI/systems conferences
• Designing paper structure, sections, or experimental methodology
• Creating figures, tables, or data visualizations for papers
• Performing literature review or managing citations
• Responding to reviewer comments or preparing rebuttal
• Any task involving academic writing workflow

Do NOT use for:

• Non-academic writing (blogs, marketing copy, documentation)
• Papers outside AI/systems domains without adaptation
• Simple formatting tasks that don't require domain expertise

Academic Paper Writing

Guide agents through the complete academic paper writing process, from initial draft to final submission.

Core Philosophy

Writing = Building Arguments, Not Recording Work

Papers must construct an irrefutable argument system, not simply document what was done. The goal: make reviewers conclude "this problem is important, this method is sound, these results are credible."

The Argument Loop

Observation → Insight → Design → Evidence

Every section must form a verifiable takeaway that closes this loop.

Reviewer Psychology

Four Axioms

1. Default Distrust: Reviewers assume most papers are poor quality
2. Limited Patience: Key information must appear early; conclusions go first
3. Emotional Judgment: Visual presentation directly affects acceptance decisions
4. Logical Closure: Arguments must be self-consistent; gaps invite rejection

Reviewers seek reasons to REJECT. Give them reasons to FIGHT for your paper.

Writing Workflow

Three-Phase Process

Time Budget

Role Transition

Early phase: Engineer (deep in details)
Late phase: Researcher (above details)

Kill the Engineer in you; wake up the Researcher.

Paper Structure

Critical Sections (The Target)

These three sections define the problem and build consensus:

• Abstract: Mini Introduction; spend up to half on problem/motivation; show concrete numbers
• Introduction: Make reviewers want to read; structure: problem → gap → insight → contributions
• Background + Motivation: Not a tutorial; purposeful introduction; three-layer structure: Observation → Limitation → Opportunity

Supporting Sections

• Design: Explain trade-offs, not describe flow; modular presentation; abstract method, not implementation
• Implementation: Credibility boost, not workload display; keep brief
• Evaluation: Setup → Main results → Ablation → Supplementary; use concrete numbers in text
• Related Work: Categorize and contrast; highlight necessity of your approach
• Conclusion: Fill to full page; can include future work

Key Requirements

• Front two pages: include intuitive figures explaining the problem
• Design: minimum 3 points for top-tier; must cover all stated problems
• Evaluation: must close the loop—cover all design points and initial questions

Expression Standards

Paragraph Flow

Paragraphs should be like prayer beads—round, smooth bodies connected by a thread.

Each paragraph:承上 (connect up) → body → 启下 (connect down)

• Uniform length; no isolated short paragraphs
• Reader should flow through without thinking

Sentence Rules

Word Choice

Positive framing: fails to → is limited by → is designed for

Figure Standards

Information Density

• Compact layout; avoid "padding" accusations
• Unified color scheme across all figures
• Consider black-white printing; use textures

Dimensions

• Single column: 4inch × 100dpi = 400pt
• Double column: 7 inches wide
• Reserve 1-2 double-column figures for page filling

Data Visualization

• Global color palette; explicit color in all code
• Must show average/mean values
• Caption explains figure; self-contained understanding
• Matplotlib: bbox_inches='tight', pad_inches=0

Experimental Rigor

Structure

Setup → Main Experiment → Ablation → Supplementary

Analysis Formula

Phenomenon → Cause → Design Connection → Implication

Requirements

• SOTA comparison required: No strong baseline = unverifiable paper
• Ablation required: Prove gains come from design
• Anomaly analysis: Surprising/negative results need deep explanation
• Numbers in text: Don't make readers hunt in figures

Analysis should be thorough enough that reviewers don't want to read it.

Submission Process

Pre-submission

• Verify template (font size errors are fatal)
• Remove copyright information
• Collect all ORCID IDs early

Rebuttal Workflow

List questions → Answer each → Merge similar → Assign responsibility

Camera-ready

• Fix reviewer-identified issues
• Add copyright, authorship, acknowledgments
• Watch for page count changes

Common Pitfalls

Content Errors

Experiment Errors

AI Tool Usage

• LLM translation shows detectable patterns
• Use LLM for translation + polish, but apply your own voice
• Writing experience beats observation

Self-Check List

Format

• Template correct?
• Looks like a paper?
• Last page full?

Content

• Background/Related Work written as tutorial?
• Design covers all stated problems?
• Evaluation covers all Design points?

Structure

• No isolated paragraphs?
• No short paragraphs?
• No long sentences?

Data

• Concrete numbers in abstract/text?
• Average values on figures?

Consistency

• Terms consistent throughout?
• Figure colors unified?

Resources

• Technical paper writing: https://homes.cs.washington.edu/~mernst/advice/write-technical-paper.html
• Technical presentations: https://homes.cs.washington.edu/~mernst/advice/giving-talk.html
• Conference acceptance rates: https://csconferences.org/

Papers are labels you attach to yourself. What kind of person writes what kind of paper, and what kind of paper defines what kind of person.