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:

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.

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)

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

Use the

4-sentence structure

Context → Gap → Contribution → Impact

Check against

assets/section_checklists.md

(Abstract Checklist)

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

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)

Key requirement:

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

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

Check against

assets/section_checklists.md

(Introduction Checklist)

Reference:

references/narrative_framework.md

(Introduction section) for detailed guidance and examples.

Related Work

Organize thematically

(not chronologically): Group into 3-5 categories

For each category:

Describe the general approach

Cite 3-5 representative works with 1-sentence descriptions

Point out limitations relevant to your contribution

End with positioning paragraph

"In contrast to [X], our approach..."

Clearly articulate differences and advantages

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

Dual objectives:

Reproducibility: Enough detail for reimplementation

Intuition: Explain why the approach works

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

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)

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

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

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)

Ablation Studies

(subsection, critical):

Demonstrate necessity of each component

Table: effect of removing/modifying components

Analysis

(subsection):

Where does the method excel? Where does it fail?

Qualitative analysis, error analysis, failure cases

Computational Cost

(if relevant):

Training time, inference time, memory usage

Comparison with baselines

Check against

assets/section_checklists.md

(Experiments/Results Checklist)

Reference:

references/narrative_framework.md

(Experiments/Results section)

Discussion

Summarize findings

(1 para): Restate key results

Interpret results

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

Acknowledge limitations

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

Broader implications

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

Check against

assets/section_checklists.md

(Discussion Checklist)

Tone:

Balanced—confident but not overselling. Limitations increase credibility.

Conclusion

Restate contribution

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

Broader impact

(0.5 para): Significance and applications

Future work

(0.5 para): Open questions and extensions

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

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)

Old Before New

Start sentences with familiar information; end with new information

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

Subject-Verb Proximity

Keep the verb close to the subject

Long gaps between subject and verb strain comprehension

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:

Notation

:

Define all symbols on first use

Use consistent conventions (bold for vectors, italic for scalars, etc.)

Integrate equations into sentences with punctuation

Figures and Tables

:

Reference all figures/tables in text before they appear

Self-contained captions

High-resolution, readable fonts (≥8pt)

Colorblind-friendly palettes

Citations

:

Follow venue citation style (author-year or numbered)

Cite all prior work you build on or compare against

Accurate and complete bibliography

Code and Reproducibility

:

State code availability

Provide sufficient implementation details

Report hyperparameters, random seeds, number of runs

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

:

Section-by-Section Review

:

Run through each section's checklist

Ensure all required elements are present

Check for common pitfalls

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)

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:

Analyze comments systematically:

Categorize: Major issues (experiments, clarity, claims) vs. Minor issues (typos, formatting)

Prioritize: Address major issues first

Plan revisions:

List all changes to be made

If experiments are requested, plan them carefully

If clarifications are needed, identify which sections to revise

Revise and respond:

Address every comment (in rebuttal or revision)

Use respectful, professional tone

Clearly mark changes (if required by venue)

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:

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)

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

Revision

(Stage 3):

Apply

references/sentence_clarity.md

principles to every paragraph

Ensure old-to-new flow, stress position usage

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:

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)?

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]."

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:

Structure

:

Read

references/narrative_framework.md

(Experiments/Results section)

Follow: Setup → Main Results → Ablations → Analysis → Cost

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)

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

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:

Check venue requirements

:

Read

references/cs_conventions.md

(Section 8: Venue-Specific Guidelines)

ICML uses numbered citations [1], double-blind review, LaTeX template

Notation

:

Read

references/cs_conventions.md

(Section 1: Notation and Mathematical Writing)

Ensure: Vectors are bold, scalars are italic, all symbols defined

Citations

:

Read

references/cs_conventions.md

(Section 3: Citations and References)

Use numbered format: "Method X [1] achieves..."

Anonymize self-citations for double-blind

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

Draft quickly, revise thoroughly:

Don't aim for perfection in the first draft

Get ideas down, then refine structure and clarity

Write sections out of order:

Start with Methods and Results (most concrete)

Then Introduction and Related Work

Finally Abstract and Conclusion

Use figures early:

Create key figures (architecture, main results) before writing

Figures clarify your thinking and guide the narrative

Get feedback early:

Share drafts with co-authors and colleagues

Mock reviews identify issues before submission

Iterate on structure:

If a section feels wrong, revisit the narrative arc

Ensure every section advances Problem → Solution → Evidence → Implications

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

Plan the narrative

Problem → Solution → Evidence → Implications

Draft section-by-section

Use structure guidelines from

references/narrative_framework.md

Revise for clarity

Apply principles from

references/sentence_clarity.md

Polish and comply

Use

references/phrasebank.md

and

references/cs_conventions.md

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: Read references/narrative_framework.md (Core Principle) Use assets/section_checklists.md (Quick Pre-Draft Planning Checklist) Outline your paper's narrative arc in 4 sentences (Problem, Solution, Evidence, Implications) Draft section-by-section, checking checklists as you go For revising an existing draft: Identify the issue (structure, clarity, compliance) Consult the relevant reference file Apply fixes systematically Re-check with the appropriate checklist For sentence-level issues: Read references/sentence_clarity.md (Three Golden Rules) Apply to each problematic paragraph Check: Old-to-new flow, stress position usage, subject-verb proximity Ready to write? Let's build a clear, compelling paper together.