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.

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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

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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.

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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

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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.

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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)

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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)

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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)

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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.

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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.

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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."

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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.

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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.

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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

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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)

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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.

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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

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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)

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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

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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)

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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 perform