Keshav Three Pass Reading

Keshav Three-Pass Paper Reading Skill

Based on: S. Keshav, "How to Read a Paper" (ACM SIGCOMM 2007, updated 2013)

Purpose: Guide researchers through effective paper reading using three progressive passes, with AI assistance at each stage while preserving human judgment. Produces structured comprehension artifacts.

The Three-Pass Method

Structured Outputs (Generated After Pass 2 or 3)

The skill produces five standardized artifacts capturing comprehension:

Workflow

Prerequisites

• Paper accessible as text (.txt, .md) or PDF (use pdftotext first)
• Paper path or content provided

Pass 1: The 5Cs Scan (5–10 minutes)

Goal: Answer the 5Cs to decide whether to continue.

Read: Title, Abstract, Introduction, Section Headings, Conclusion, References.

AI Assistance Prompt:

You are a research assistant helping with Pass 1 of Keshav's three-pass method.
Paper text provided below. Extract and present:

1. CATEGORY: What type of paper? (survey, novel method, benchmark, position, theory, system, empirical study)
2. CONTEXT: Which field/subfield? Key prior works cited? Who is the audience?
3. CORRECTNESS: Do assumptions seem reasonable? Any red flags in claims?
4. CONTRIBUTIONS: What are the authors' stated contributions (usually in Introduction)?
5. CLARITY: How well-written? Accessible to non-specialists?

Output as a structured brief. DO NOT DECIDE for the user — just present the 5Cs.
Paper:
[paper_text_first_2000_chars_or_full_if_short]

Human Decision Point: Based on 5Cs, decide: Skip / Defer / Continue to Pass 2.

Record Decision: Save to pass1_decision.md with rationale.

Pass 2: The Main Thread (30–60 minutes)

Goal: Understand the paper's argument and evidence without getting lost in proofs.

Read: Full paper linearly, but skip proof details (appendices, derivations). Focus on:

• Figures and tables (what they show, trends, surprises)
• Experimental setup (datasets, baselines, metrics, hardware)
• Which key works are cited and how they're positioned
• The "story arc": problem → insight → method → results → implications

AI Assistance Prompt:

You are a research assistant helping with Pass 2 of Keshav's three-pass method.
Paper text provided below. Produce structured comprehension:

## SUMMARY (3-5 sentences)
Executive summary of the paper's core argument and findings.

## PROBLEM
- Core problem addressed
- Motivation / real-world impact
- Gap in prior work this paper fills
- Research questions or hypotheses

## METHOD
- Core insight / innovation (1-2 paragraphs)
- Algorithm / architecture / approach description
- Key design choices and why they matter
- Theoretical claims (if any)

## RESULTS
- Headline numbers (exact metrics from paper)
- Key figures/tables described with takeaways
- Baselines compared and outcomes
- Ablation studies (if any)
- Surprising or counterintuitive findings

## CITATIONS OF NOTE
- 3-5 most important prior works cited and how this paper relates to each

IMPORTANT: Cross-reference every number/claim against the provided text. Flag any discrepancies.
Paper:
[full_paper_text]

Human Verification: For each key number in Results, verify against original text. Do not trust AI extraction blindly.

Output Files Created:

• summary.md — from SUMMARY section
• problem.md — from PROBLEM section
• method.md — from METHOD section
• results.md — from RESULTS section (with verified numbers)

Pass 3: Deep Reconstruction (1–2+ hours)

Goal: Reconstruct the author's thinking; scrutinize validity.

Activities:

• Could you reproduce the method from the description alone?
• Are assumptions valid? (data distribution, compute budget, task formulation)
• Do experiments support conclusions? (missing baselines, cherry-picked metrics, data leaks)
• Evaluation biases? (oracle choice, prompt sensitivity, seed variance)
• Overclaims? (generalization beyond evidence, "solves X" when only shows improvement on Y)
• What would you change / extend / challenge?

AI Assistance Prompt (Reviewer Mode):

You are a rigorous peer reviewer for this paper. Paper text and Pass 2 outputs provided.
Generate probing questions organized by category:

## ASSUMPTIONS
- What assumptions underlie the method? Are they explicit?
- What would break if assumption X is violated?

## EXPERIMENTAL DESIGN
- Are baselines appropriate and strong? Any missing?
- Is the evaluation metric aligned with the claimed contribution?
- Any data leakage risks (train/test overlap, oracle contamination)?
- Statistical rigor: seeds, variance, significance testing?

## CLAIMS VS EVIDENCE
- Which claims are well-supported? Which overreach?
- Does "we solve X" actually mean "we improve on benchmark Y by Z%"?
- External validity: do results generalize beyond the experimental setup?

## REPRODUCIBILITY
- Sufficient detail to reimplement? Missing hyperparameters?
- Compute requirements stated? Code/data availability?

## EXTENSIONS & FOLLOW-UPS
- What's the most natural next experiment?
- What would a strong ablation look like?
- How might this method fail in deployment?

Output as structured questions. Do NOT answer them — the human researcher answers.
Paper: [full_paper_text]
Pass 2 outputs: [summary.md, problem.md, method.md, results.md]

Human Synthesis: Answer the reviewer questions. Write takeaways.md with:

# Takeaways: [Paper Title]

## What I'll Use / Cite
- [Specific technique, insight, dataset, baseline]

## Limitations Noted
- [Assumption fragility, eval gap, scope restriction]

## Open Questions / Extensions
- [Concrete follow-up experiments or analyses]

## Verdict
- [Skip / Reference / Build Upon / Reproduce]

Usage Examples

Quick Start (All Passes)

User: "Read this paper with the three-pass method: /path/to/paper.txt"
Agent: Runs Pass 1 → presents 5Cs → user decides → Pass 2 → structured outputs → Pass 3 → takeaways

Specific Pass

User: "Just do Pass 1 on this paper"
Agent: Runs 5Cs scan only, saves pass1_decision.md

AI-Assisted Pass 2 on Existing Paper

User: "I've read the paper. Generate the 4 structured outputs from Pass 2."
Agent: Uses AI prompt with full paper text → produces summary.md, problem.md, method.md, results.md

Reviewer Mode (Pass 3 Only)

User: "Act as reviewer on this paper I've already read. Here are my Pass 2 notes."
Agent: Runs reviewer prompt → generates probing questions → user answers → takeaways.md

File Output Structure

paper_reading/
├── pass1_decision.md        # 5Cs + continue/skip decision
├── summary.md               # 3-5 sentence executive summary
├── problem.md               # Problem, motivation, gap, RQs
├── method.md                # Core approach, algorithm, design choices
├── results.md               # Verified headline numbers, figures, ablations
└── takeaways.md             # Personal synthesis: use, limits, extensions, verdict

Quality Standards

• Never fabricate numbers. Every metric in results.md must trace to paper text.
• Flag uncertainty. If paper is vague on a detail, state: "Paper does not specify X."
• Separate summary from judgment. summary.md/problem.md/method.md/results.md = what the paper says. takeaways.md = what you think.
• Cross-check Pass 2 extractions against original text before saving.
• Pass 3 questions are probes, not answers. The human provides answers in takeaways.md.

Common Pitfalls

❌ Skipping Pass 1 — leads to wasting time on irrelevant papers ❌ Trusting AI extraction without verification — hallucinated numbers propagate ❌ Conflating summary with critique — keep results.md descriptive; save critique for takeaways.md ❌ Reading proofs in Pass 2 — saves them for Pass 3 (or skip entirely if not reproducing) ❌ Not making a Pass 1 decision — the 5Cs are useless without the continue/skip call

Integration Notes

• Works with any paper source: arXiv PDF, local .txt, conference PDF
• Pair with arxiv skill to fetch paper, pdftotext to convert
• Complements academic-paper-review skill: this is for your reading; that skill produces review deliverables for a collection
• Can be used in batch: run Pass 1 on 20 papers, continue only on 3–5

Teaching Mode

When mode=teach, the skill explains the method and runs a worked example on a short sample paper (provided in references/sample-paper.txt), showing:

• Annotated Pass 1 5Cs output
• Annotated Pass 2 structured outputs
• Annotated Pass 3 reviewer questions and takeaways
• Common mistakes highlighted

Use when onboarding new researchers to the method.

Reference Files

• references/keshav-original-article.md — Full text of Keshav's "How to Read a Paper"
• references/sample-paper.txt — Short ML paper for teaching mode
• references/ai-prompts.md — Full prompt templates for each pass (copy-paste ready)
• templates/output-structure.md — Markdown templates for the 5 output files

Success Checklist

• Pass 1: 5Cs extracted, decision recorded with rationale
• Pass 2: 4 structured files created, all numbers verified against paper
• Pass 3: Reviewer questions generated, human answers synthesized into takeaways.md
• No fabricated metrics in any output
• Clear separation: descriptive (what paper says) vs. evaluative (what I think)
• Decision recorded: Skip / Reference / Build Upon / Reproduce