Skills
skills/sipengxie2024/helios-writing/academic-writing-cs

academic-writing-cs

SKILL.md

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.

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