The Ultimate Checklist for Writing High-Quality Computer Science Papers

Writing a compelling computer science research paper requires meticulous attention to detail, from crafting a precise title to structuring rigorous experiments. This guide distills essential checks across every stage of paper preparation, ensuring your work meets academic standards while maximizing reader engagement.

Part 1: Crafting Effective Titles and Abstracts

1.1 Title Guidelines

  • Brevity & Clarity: Limit titles to 15 words. Avoid vague phrases like “A Novel Framework” and prioritize specificity.

    • Example: “GraphPrompt: Optimizing Pre-trained Models via Graph Contrastive Learning”
  • Problem-Solution Structure: Explicitly state the research problem and your approach.

    • Include technical keywords: OOD detection, federated learning, knowledge distillation.
  • Avoid Ambiguous Abbreviations: Use widely recognized acronyms (e.g., AI, ML) but avoid niche shorthand (e.g., “AD” for anomaly detection).

1.2 Abstract Essentials

A strong abstract must address:

  1. Problem Definition: Clearly state the research challenge in the first two sentences.
  2. Methodology: Highlight your technical contribution using specific terms.
  3. Quantifiable Results: Include measurable outcomes (e.g., “11.2× faster inference speed”).
  4. Impact: Explain broader implications for academia or industry.

Template:
“Addressing [specific problem], we propose [method]. By leveraging [technical innovation], our approach achieves [metric improvement] on [dataset], offering a novel solution for [application].”

Part 2: Introduction and Contributions

2.1 Introduction Structure

  1. Context: Open with real-world relevance (e.g., “AI model energy consumption grows 120% annually”).
  2. Challenges: Critique existing methods, citing 3+ recent papers.
  3. Preview: Conclude with a 1-sentence summary of your method.

2.2 Contributions That Stand Out

Use the SMART framework:

  • Specific: “The first framework supporting dynamic pruning.”
  • Measurable: “15.7% average accuracy gain across benchmarks.”
  • Action-Oriented: “Release a 100,000-sample dataset.”
  • Avoid vague claims like “theoretical advancements” without evidence.

Part 3: Literature Review Best Practices

3.1 Key Requirements

  • Relevance: Cite works directly related to your method, baselines, or task.
  • Baseline Inclusion: Compare against 3+ established methods, including 1-2 SOTA approaches.
  • Conciseness: Keep the section under 1.5 pages (non-survey papers).

3.2 Avoiding Pitfalls

  • Verify All Citations: Cross-check AI-generated references via Google Scholar.
  • Highlight Differentiation: Use tables to contrast your method’s advantages.

Part 4: Methodology and Experiments

4.1 Method Section Checks

  • Notation Clarity: Define all symbols upon first use.
  • Formula Context: Explain equations in the text (e.g., “Equation 3 describes…”).
  • Visual Aids: Include diagrams for each module, aligned with an overview figure.

4.2 Experiment Design

  • Dataset Diversity: Use ≥3 datasets (unless introducing a new one).
  • Baseline Justification: Explain why selected baselines matter (e.g., SOTA status).
  • Ablation Studies: Test component removal and hyperparameter sensitivity.
  • Transparency: Report hardware specs, libraries, and failed cases.

Visualization Tips:

  • Use box plots for robustness analysis.
  • Highlight critical findings with heatmaps.

Part 5: Figures, Tables, and Formatting

5.1 Figure Standards

  • Readability: 8pt+ font size, grayscale-compatible colors.
  • Self-Contained Captions: Explain the “why,” not just the “what.”
  • Vector Formats: Save diagrams as PDF/EPS.

5.2 Table Essentials

  • Methodology Tags: Label all methods in legends.
  • Information Density: Each table should reveal new insights.

Part 6: Writing Style and SEO Optimization

6.1 Readability Enhancements

  • Short Paragraphs: Break text into ≤10-line blocks.
  • Active Voice: Keep passive constructions under 30%.
  • Define Abbreviations: Spell out “ML” (machine learning) on first mention.

6.2 Avoiding AI Artifacts

  • Replace verbose LLM-generated terms (e.g., “intricate methodology”) with plain language.
  • Use tools like Grammarly to check passive voice ratio.

Part 7: Pre-Submission Checklist

7.1 Technical Checks

  • [ ] Zero LaTeX errors or bad boxes.
  • [ ] Anonymous PDF (scrub metadata and filenames).
  • [ ] Page limit compliance (include appendices).

7.2 Peer Review

  • Have a non-author validate clarity.
  • Test PDF rendering across devices (Windows/macOS).

Part 8: References and Ethics

8.1 Citation Integrity

  • Format Compliance: Follow conference-specific guidelines (e.g., NeurIPS author abbreviations).
  • Self-Citation Limit: ≤20% of total references.
  • Hallucination Check: Manually verify every citation.

8.2 Tools for Researchers

  • Zotero: Manage BibTeX libraries.
  • Connected Papers: Map citation networks.

By systematically applying this checklist, authors can reduce submission risks while enhancing technical rigor. Remember: A well-structured paper not only communicates findings effectively but also reflects scholarly discipline.

For ongoing updates, contribute to the open-source cs-paper-checklist project.