Academic Writing for Computer Science

Overview

This skill provides end-to-end support for writing high-quality computer science research papers. It focuses on constructing clear, compelling technical narratives while adhering to field-specific conventions.

Core Philosophy:

• •Academic papers are narrative arcs (Problem → Solution → Evidence → Implications), not template fill-ins
• •Clarity comes from structure: place familiar information first, new information last
• •Every design choice must be justified; every claim must be supported

Scope:

• •Conference papers (6-12 pages, competitive venues)
• •Journal articles (15-30 pages, comprehensive)
• •Thesis chapters (flexible length, deep coverage)
• •All CS subfields: AI/ML, Systems, Theory, HCI, Security, etc.

When to Use This Skill

Invoke this skill when:

• •Planning paper structure and narrative flow
• •Drafting any section (Abstract, Introduction, Methods, Results, Discussion, Conclusion)
• •Revising for clarity, coherence, or compliance with venue requirements
• •Reviewing sentence-level writing for clarity issues
• •Seeking CS-specific conventions (notation, figures, citations)
• •Checking completeness with section-by-section quality checklists
• •Responding to reviewer comments

Workflow Decision Tree

Stage 1: Planning and Structure

When starting a new paper or major revision:

1. •

   Define the Narrative Arc

   • •What problem does this solve, and why does it matter? (1-2 sentences)
   • •What is the single main contribution? (1 sentence)
   • •What are the 3 key results that support the contribution?
   • •What are the main limitations?

   Reference: references/narrative_framework.md — Read the "Core Principle" and "Section-Level Narrative Structure" sections to understand how to structure the paper's story.

2. •

   Identify Target Venue and Constraints

   • •Conference or journal?
   • •Page limits, formatting requirements, anonymization rules?
   • •Subfield conventions (ML vs. Systems vs. Theory)?

   Reference: references/cs_conventions.md (Section 8: Venue-Specific Guidelines, Section 5: Subfield-Specific Conventions)

3. •

   Outline Section-by-Section

   • •For each major section, define:
     • •What is the purpose of this section?
     • •What are the 2-3 key points to convey?
     • •What figures/tables will support this?

   Tool: Use assets/section_checklists.md (Quick Pre-Draft Planning Checklist) to ensure all key questions are answered before writing begins.

Stage 2: Drafting

For each section, follow this process:

Abstract

1. •Use the 4-sentence structure: Context → Gap → Contribution → Impact
2. •Check against assets/section_checklists.md (Abstract Checklist)
3. •Ensure it's self-contained and within word limit (150-250 words)

Common mistakes:

• •Vague contribution: "We improve X" → Be specific: "We achieve 15% higher accuracy"
• •No concrete results: Always include numbers/metrics

Introduction

1. •

   Follow the funnel structure: Broad → Narrow → Specific

   • •Para 1: Problem domain and importance
   • •Para 2-3: Specific problem, motivation, why existing work falls short
   • •Para 4: Gap statement ("However, existing approaches lack...")
   • •Para 5: Contribution overview (what this paper provides)
   • •Para 6: Results summary (2-3 concrete findings)
   • •Para 7: Paper organization (optional)
2. •

   Key requirement: By the end of paragraph 4-5, the reader must clearly understand the contribution.

3. •

   Include at least one figure (architecture or key result) for ML/systems papers.

4. •

   Check against assets/section_checklists.md (Introduction Checklist)

Reference: references/narrative_framework.md (Introduction section) for detailed guidance and examples.

Related Work

1. •

   Organize thematically (not chronologically): Group into 3-5 categories

2. •

   For each category:

   • •Describe the general approach
   • •Cite 3-5 representative works with 1-sentence descriptions
   • •Point out limitations relevant to your contribution
3. •

   End with positioning paragraph: "In contrast to [X], our approach..."

   • •Clearly articulate differences and advantages
4. •

   Check against assets/section_checklists.md (Related Work Checklist)

Common mistakes:

• •Laundry list of citations without synthesis
• •Failing to position your work relative to prior work
• •Being dismissive (respect prior work while differentiating)

Methodology

1. •

   Dual objectives:

   • •Reproducibility: Enough detail for reimplementation
   • •Intuition: Explain why the approach works
2. •

   Structure varies by paper type:

   • •ML/AI papers: Problem Formulation → Overview + Figure → Detailed Design → Implementation → Complexity
   • •Systems papers: Architecture Overview → Component Design → Key Mechanisms → Implementation
   • •Theory papers: Formal Definitions → Main Results (theorems) → Proof Sketch
3. •

   Always include:

   • •Clear notation (define all symbols on first use)
   • •High-level overview before diving into details
   • •Justification for design choices (or defer to Ablations)
4. •

   Check against assets/section_checklists.md (Methodology Checklist)

Reference: references/narrative_framework.md (Methodology section) and references/cs_conventions.md (Section 1: Notation and Mathematical Writing)

Experiments/Results

1. •

   Experimental Setup (subsection):

   • •Datasets: Size, splits, preprocessing
   • •Baselines: What you compare against (with citations)
   • •Metrics: What you measure and why
   • •Hardware/Software: Infrastructure and versions
   • •Hyperparameters: How selected
2. •

   Main Results (subsection):

   • •Table/figure showing primary comparison
   • •Text: "Table 1 shows that our method outperforms..."
   • •Highlight key findings with concrete numbers
   • •Report statistical significance (confidence intervals, p-values, or std dev)
3. •

   Ablation Studies (subsection, critical):

   • •Demonstrate necessity of each component
   • •Table: effect of removing/modifying components
4. •

   Analysis (subsection):

   • •Where does the method excel? Where does it fail?
   • •Qualitative analysis, error analysis, failure cases
5. •

   Computational Cost (if relevant):

   • •Training time, inference time, memory usage
   • •Comparison with baselines
6. •

   Check against assets/section_checklists.md (Experiments/Results Checklist)

Reference: references/narrative_framework.md (Experiments/Results section)

Discussion

1. •

   Summarize findings (1 para): Restate key results

2. •

   Interpret results (1-2 paras): Why does the method work? What insights?

3. •

   Acknowledge limitations (0.5-1 para): Be honest about scope and failure cases

4. •

   Broader implications (0.5-1 para): Impact on the field, applications, future directions

5. •

   Check against assets/section_checklists.md (Discussion Checklist)

Tone: Balanced—confident but not overselling. Limitations increase credibility.

Conclusion

1. •

   Restate contribution (1 para): Recap problem, solution, key findings

2. •

   Broader impact (0.5 para): Significance and applications

3. •

   Future work (0.5 para): Open questions and extensions

   • •Phrase as opportunities: "An interesting direction is..." (not "In future work, we will...")
4. •

   Check against assets/section_checklists.md (Conclusion Checklist)

Do NOT: Introduce new ideas, copy-paste Abstract, or be vague.

Stage 3: Revision for Clarity

After drafting, apply sentence-level clarity principles:

The Three Golden Rules (Gopen & Swan)

1. •

   Old Before New: Start sentences with familiar information; end with new information

   • •This creates coherent flow where each sentence builds on what came before
2. •

   Subject-Verb Proximity: Keep the verb close to the subject

   • •Long gaps between subject and verb strain comprehension
3. •

   Stress Position Power: Place the most important information at sentence end

   • •Readers remember and emphasize what comes at the end

Apply these rules systematically:

• •For each paragraph, check that sentences flow (old-to-new)
• •For each sentence, check that:
  • •Topic position (start) contains familiar info
  • •Stress position (end) contains important new info
  • •Verb appears soon after subject

Reference: references/sentence_clarity.md — Read this in full for detailed principles, examples, and common anti-patterns.

Practical Checklist:

• • Familiar information at sentence start (topic position)
• • Important new information at sentence end (stress position)
• • Verb close to subject
• • Active voice (unless passive is intentionally better)
• • Parallel structures for parallel ideas

Common anti-patterns to fix:

• •"Buried Verb" Syndrome: Converting verbs to nouns (nominalization)
  • •❌ "The comparison of the methods is shown..."
  • •✅ "Table 1 compares the methods..."
• •"Throat-Clearing": Weak starts like "It is important to note that..."
  • •❌ "It is important to note that our method improves accuracy."
  • •✅ "Our method improves accuracy."
• •"Dangling Emphasis": Ending sentences with weak elements
  • •❌ "This approach significantly improves performance, as shown in [23]."
  • •✅ "As shown in [23], this approach significantly improves performance."

Stage 4: Polishing and Compliance

Language and Phrasing

When writing or revising specific academic functions, consult references/phrasebank.md:

• •Introducing work: Establishing territory, identifying gaps, stating contributions
• •Referring to sources: Integral vs. non-integral citations
• •Describing methods: Sequential actions, conditional logic, implementation details
• •Reporting results: Presenting findings, comparing baselines, interpreting
• •Discussing findings: Explaining success, acknowledging limitations, stating implications
• •Writing conclusions: Summarizing, broader impact, future work

General language functions:

• •Being cautious (hedging): "may", "appears to", "likely"
• •Being critical: Identifying weaknesses, questioning validity
• •Compare and contrast: Similarity, difference
• •Describing trends: Increasing, decreasing, stability
• •Explaining causality: Causes, effects, conditions

Usage: Adapt templates to your context; don't copy verbatim. Vary expressions to maintain natural flow.

CS-Specific Conventions

Ensure compliance with field norms:

1. •

   Notation:

   • •Define all symbols on first use
   • •Use consistent conventions (bold for vectors, italic for scalars, etc.)
   • •Integrate equations into sentences with punctuation
2. •

   Figures and Tables:

   • •Reference all figures/tables in text before they appear
   • •Self-contained captions
   • •High-resolution, readable fonts (≥8pt)
   • •Colorblind-friendly palettes
3. •

   Citations:

   • •Follow venue citation style (author-year or numbered)
   • •Cite all prior work you build on or compare against
   • •Accurate and complete bibliography
4. •

   Code and Reproducibility:

   • •State code availability
   • •Provide sufficient implementation details
   • •Report hyperparameters, random seeds, number of runs
5. •

   Subfield-Specific Variations:

   • •ML/AI: Emphasis on ablations, statistical significance, computational cost
   • •Systems: Architecture diagrams, throughput/latency, scalability
   • •Theory: Formal definitions, theorems, proofs, complexity bounds
   • •HCI: User studies, qualitative feedback, interface screenshots
   • •Security: Threat models, attack scenarios, defense mechanisms

Reference: references/cs_conventions.md — Comprehensive guide covering notation, figures, citations, code, subfield norms, and venue requirements.

Quality Assurance

Before submission, use assets/section_checklists.md:

1. •

   Section-by-Section Review:

   • •Run through each section's checklist
   • •Ensure all required elements are present
   • •Check for common pitfalls
2. •

   Pre-Submission Checklist:

   • •Content completeness (all sections, figures, citations)
   • •Formatting (venue template, page limits, margins)
   • •Anonymization (if double-blind)
   • •Reproducibility (sufficient detail, code availability)
   • •Final quality checks (spell-check, grammar, co-author review)
3. •

   Emergency Checklist (if deadline is imminent):

   • •Prioritize: Abstract, Introduction contribution statement, Main results table, At least one ablation, Readable figures, Correct bibliography

Stage 5: Responding to Reviews

After receiving reviewer feedback:

1. •

   Analyze comments systematically:

   • •Categorize: Major issues (experiments, clarity, claims) vs. Minor issues (typos, formatting)
   • •Prioritize: Address major issues first
2. •

   Plan revisions:

   • •List all changes to be made
   • •If experiments are requested, plan them carefully
   • •If clarifications are needed, identify which sections to revise
3. •

   Revise and respond:

   • •Address every comment (in rebuttal or revision)
   • •Use respectful, professional tone
   • •Clearly mark changes (if required by venue)
4. •

   Check revised version:

   • •Ensure all changes are integrated
   • •Re-run relevant checklists from assets/section_checklists.md (Revision Checklist)
   • •Verify still within page limits

Reference: assets/section_checklists.md (Revision Checklist)

Key Resources Summary

Narrative and Structure

• •references/narrative_framework.md: Core paper structure (Abstract, Introduction, Related Work, Methods, Results, Discussion, Conclusion). Use for understanding the narrative arc and section-specific guidance.

Sentence-Level Clarity

• •references/sentence_clarity.md: Gopen & Swan principles (topic position, stress position, old-to-new flow). Use for revising individual sentences and paragraphs for maximum clarity.

Academic Phrases

• •references/phrasebank.md: Templates for common academic writing functions (introducing work, citing sources, reporting results, discussing findings). Use when drafting or seeking variation in phrasing.

CS Conventions

• •references/cs_conventions.md: Field-specific norms (notation, figures, citations, code, subfield variations, venue requirements). Use for ensuring compliance with CS writing standards.

Quality Checklists

• •assets/section_checklists.md: Comprehensive checklists for every section, plus pre-submission, revision, and emergency checklists. Use for planning, reviewing, and final quality assurance.

Example Workflows

Workflow 1: Starting from Scratch

User: "I need to write a conference paper on my new semi-supervised learning method."

Process:

1. •

   Planning (Stage 1):

   • •Define narrative arc: Problem (labeled data is expensive) → Solution (our semi-supervised method) → Evidence (experiments on 3 datasets) → Implications (reduces labeling cost)
   • •Read references/narrative_framework.md (Core Principle)
   • •Use assets/section_checklists.md (Quick Pre-Draft Planning Checklist)
2. •

   Drafting (Stage 2):

   • •Abstract: 4-sentence structure (Context: deep learning needs data; Gap: labeling is expensive; Contribution: our method STCR; Impact: 82% accuracy with 10% labels)
   • •Introduction: Funnel (broad: DL success → narrow: labeling cost → gap: existing semi-supervised methods lack X → contribution: STCR leverages consistency → results: 7% improvement)
   • •Check each section against assets/section_checklists.md
3. •

   Revision (Stage 3):

   • •Apply references/sentence_clarity.md principles to every paragraph
   • •Ensure old-to-new flow, stress position usage
4. •

   Polishing (Stage 4):

   • •Use references/phrasebank.md for varied phrasing
   • •Ensure compliance with references/cs_conventions.md (ML/AI conventions)
   • •Run Pre-Submission Checklist from assets/section_checklists.md

Workflow 2: Revising for Clarity

User: "My introduction is confusing. Reviewers said they couldn't understand the contribution."

Process:

1. •

   Diagnose issue:

   • •Check against assets/section_checklists.md (Introduction Checklist)
   • •Is the contribution stated clearly by paragraph 4-5?
   • •Is the funnel structure followed (broad → narrow)?
2. •

   Restructure if needed:

   • •Read references/narrative_framework.md (Introduction section)
   • •Ensure: Opening → Background → Gap → Contribution → Results → Organization
   • •Explicitly state: "In this paper, we present [X], which addresses [Y] by [Z]."
3. •

   Revise at sentence level:

   • •Apply references/sentence_clarity.md principles
   • •Check that each sentence flows from the previous one (old-to-new)
   • •End key sentences with the important information (stress position)

Workflow 3: Drafting the Results Section

User: "How should I present my experimental results?"

Process:

1. •

   Structure:

   • •Read references/narrative_framework.md (Experiments/Results section)
   • •Follow: Setup → Main Results → Ablations → Analysis → Cost
2. •

   Create tables/figures:

   • •Main results table: Methods (rows) vs. Metrics (columns)
   • •Bold best results; include standard deviations
   • •Check references/cs_conventions.md (Figures and Tables section)
3. •

   Write accompanying text:

   • •"Table 1 shows that our method achieves X, outperforming the strongest baseline by Y%."
   • •Use references/phrasebank.md (Section 4: Reporting Results) for phrasing
4. •

   Quality check:

   • •Run through assets/section_checklists.md (Experiments/Results Checklist)
   • •Ensure: Statistical significance, Ablations present, Analysis included

Workflow 4: Ensuring CS Compliance

User: "Is my notation and citation style correct for ICML?"

Process:

1. •

   Check venue requirements:

   • •Read references/cs_conventions.md (Section 8: Venue-Specific Guidelines)
   • •ICML uses numbered citations [1], double-blind review, LaTeX template
2. •

   Notation:

   • •Read references/cs_conventions.md (Section 1: Notation and Mathematical Writing)
   • •Ensure: Vectors are bold, scalars are italic, all symbols defined
3. •

   Citations:

   • •Read references/cs_conventions.md (Section 3: Citations and References)
   • •Use numbered format: "Method X [1] achieves..."
   • •Anonymize self-citations for double-blind
4. •

   Final check:

   • •assets/section_checklists.md (Pre-Submission Checklist → Compliance section)

Common Pitfalls and How to Avoid Them

Pitfall 1: Vague Contributions

Problem: "We improve performance on X." Solution: Be specific. "We achieve 15% higher accuracy than the strongest baseline on ImageNet."

Pitfall 2: Missing Ablations

Problem: Claiming design choices are important without evidence. Solution: Include ablation studies. Remove each component and measure the performance drop.

Pitfall 3: Poor Information Flow

Problem: Sentences feel disjointed; readers get lost. Solution: Apply old-to-new flow. Each sentence should start with information from the previous sentence. Reference: references/sentence_clarity.md

Pitfall 4: Weak Stress Position

Problem: Sentences end with citations or minor details. Example: ❌ "This approach significantly improves performance, as shown in [23]." Solution: ✅ "As shown in [23], this approach significantly improves performance."

Pitfall 5: Ignoring Limitations

Problem: Overselling without acknowledging scope or failure cases. Solution: Dedicate a paragraph in Discussion to honest limitations. This increases credibility.

Pitfall 6: Inconsistent Notation

Problem: Using x for input in one section, X in another. Solution: Define all notation upfront. Create a notation table (appendix) if needed. Reference: references/cs_conventions.md (Section 1)

Tips for Efficient Writing

1. •

   Draft quickly, revise thoroughly:

   • •Don't aim for perfection in the first draft
   • •Get ideas down, then refine structure and clarity
2. •

   Write sections out of order:

   • •Start with Methods and Results (most concrete)
   • •Then Introduction and Related Work
   • •Finally Abstract and Conclusion
3. •

   Use figures early:

   • •Create key figures (architecture, main results) before writing
   • •Figures clarify your thinking and guide the narrative
4. •

   Get feedback early:

   • •Share drafts with co-authors and colleagues
   • •Mock reviews identify issues before submission
5. •

   Iterate on structure:

   • •If a section feels wrong, revisit the narrative arc
   • •Ensure every section advances Problem → Solution → Evidence → Implications
6. •

   Use the checklists proactively:

   • •Before drafting a section, read the checklist to know what to include
   • •After drafting, use the checklist to verify completeness

Advanced: Handling Special Cases

Writing for Top-Tier Venues

• •Higher bar for novelty and rigor: Ensure the contribution is significant, not incremental
• •Strong baselines: Compare against state-of-the-art, not just simple methods
• •Comprehensive evaluation: Multiple datasets, extensive ablations, sensitivity analyses
• •Polished presentation: High-quality figures, clear writing, consistent notation

Writing Rebuttals

• •Address all concerns: Even if you disagree, engage respectfully
• •Provide evidence: If reviewers doubt a claim, provide additional results or citations
• •Be concise: Rebuttals have strict length limits; prioritize major issues
• •Highlight changes: "We added an experiment (Table 3) showing..."

Writing Thesis Chapters

• •More comprehensive: Deeper background, extended related work, lessons learned
• •Narrative continuity: Ensure chapters connect (e.g., Chapter 3 builds on Chapter 2)
• •Broader scope: Can include negative results and explorations that didn't pan out
• •Use assets/section_checklists.md (Long-Form Paper Checklist)

Summary: The Golden Workflow

1. •Plan the narrative: Problem → Solution → Evidence → Implications
2. •Draft section-by-section: Use structure guidelines from references/narrative_framework.md
3. •Revise for clarity: Apply principles from references/sentence_clarity.md
4. •Polish and comply: Use references/phrasebank.md and references/cs_conventions.md
5. •Quality check: Run through assets/section_checklists.md

Remember:

• •Papers are stories, not templates
• •Clarity comes from structure (old-to-new, topic/stress positions)
• •Every claim needs evidence; every design choice needs justification
• •Honest limitations increase credibility

When in doubt, ask:

• •"Does this advance the narrative arc?"
• •"Can a reader reproduce this?"
• •"Is this claim supported?"
• •"Is this the simplest, clearest way to express this?"

Getting Started

For a new paper:

1. •Read references/narrative_framework.md (Core Principle)
2. •Use assets/section_checklists.md (Quick Pre-Draft Planning Checklist)
3. •Outline your paper's narrative arc in 4 sentences (Problem, Solution, Evidence, Implications)
4. •Draft section-by-section, checking checklists as you go

For revising an existing draft:

1. •Identify the issue (structure, clarity, compliance)
2. •Consult the relevant reference file
3. •Apply fixes systematically
4. •Re-check with the appropriate checklist

For sentence-level issues:

1. •Read references/sentence_clarity.md (Three Golden Rules)
2. •Apply to each problematic paragraph
3. •Check: Old-to-new flow, stress position usage, subject-verb proximity

Ready to write? Let's build a clear, compelling paper together.