Peer Review Workflow

Execute rigorous peer review of synthesized papers.

The Three Reviewers

Spawn three reviewer subagents in parallel. Each has expanded focus.

1. Methodology Reviewer

Focus: Rigor + Completeness

• Arithmetic consistency (do numbers add up?)
• Mock data detection (np.random without disclosure = REJECT)
• Reproducibility (can someone replicate from description?)
• Honest failure reporting
• All RQs addressed (check against world_model.json)
• All artifacts used (no orphaned experiment results)
• PRISMA numbers consistent

2. Statistics Reviewer

Focus: Statistical correctness

• Number verification against source files
• Appropriate statistical tests for data type
• Effect sizes and confidence intervals (not just p-values)
• Multiple testing correction where needed
• Figures reference real data files

3. Impact Reviewer

Focus: Contribution + Provenance

• Scope vs claims honesty
• Failure disclosure (are negative results hidden?)
• Overclaiming detection
• Abstract matches actual results
• Every claim has DOI + exact quote
• Source types match confidence (blog ≤ 0.7, article ≤ 1.0)
• Spot-check 3-5 quotes are verbatim
• Run DOI validation:

  python3 .claude/hooks/validate-doi.py

MANDATORY: Compile PDF Before Review

Before spawning reviewers, compile the PDF:

cd $SESSION_DIR/synthesis
pdflatex -interaction=nonstopmode paper.tex
bibtex paper
pdflatex -interaction=nonstopmode paper.tex
pdflatex -interaction=nonstopmode paper.tex

# Verify PDF exists and has content
ls -lh paper.pdf  # Should be > 50KB

Why compile first?

• LaTeX errors block reviewer assessment
• Some issues (figure placement, citation format, encoding) only visible in PDF
• Reviewers should see what the reader will see

If compilation fails: Fix LaTeX errors before spawning reviewers. Common fixes:

• Missing packages:

  \usepackage{...}

• Undefined citations: Check references.bib has all cited keys
• Encoding issues: Ensure UTF-8 and escape special chars

Spawning Reviewers

# Spawn all 3 reviewers IN PARALLEL (single message, 3 Task tool calls)

# 1. Methodology reviewer (includes completeness checks)
Task: Spawn reviewer-methodology subagent
Prompt: "Review paper.tex AND paper.pdf for methodological rigor AND completeness.
Check: arithmetic, mock data, reproducibility.
Also verify: all RQs addressed, all artifacts used, PRISMA consistent.
Check PDF for: formatting issues, figure placement, readability.
Save methodology_review.json with verdict and issues."

# 2. Statistics reviewer
Task: Spawn reviewer-statistics subagent
Prompt: "Review paper.tex AND paper.pdf for statistical correctness.
Verify numbers match source files, appropriate tests, effect sizes.
Check figures reference real data files.
Check PDF for: figure rendering, table formatting, equation display.
Save statistics_review.json with verdict and issues."

# 3. Impact reviewer (includes provenance checks)
Task: Spawn reviewer-impact subagent
Prompt: "Review paper.tex AND paper.pdf for contribution AND provenance.
Check: scope vs claims, failures disclosed, no overclaiming.
Also verify: every claim has DOI+quote, spot-check 3 quotes verbatim.
Check PDF for: citation formatting, reference list completeness.
Run: python3 .claude/hooks/validate-doi.py
Save impact_review.json with verdict and issues."

External Validation (Recommended)

Include at least one Codex reviewer:

codex exec --full-auto -m gpt-5.2-codex -o codex_review.txt \
  "Review this paper for scientific validity. Check:
   1. Do claims match evidence?
   2. Are limitations honest?
   3. Any overclaiming?

   Paper: $(cat paper.tex)

   Output JSON with verdict (ACCEPT/REVISE/REJECT) and issues."

Acceptance Criteria

NOT majority vote. Unanimous acceptance required.

• All THREE reviewers must give ACCEPT
• If ANY reviewer gives REVISE/REJECT → revision cycle
• Codex reviewer has veto power if included

Revision Cycle

If not unanimous acceptance:

1. Collect all issues from reviewer JSONs
2. Resume synthesizer using saved agent ID (preserves context)
3. Synthesizer must address EVERY issue:
   • FIX: Make the change
   • REBUT: Explain why reviewer is wrong (with evidence)
   • ACKNOWLEDGE: If can't fix, explain in Limitations
4. Synthesizer creates

   revision_response.md

5. RE-COMPILE PDF after revisions:

   cd $SESSION_DIR/synthesis
   pdflatex -interaction=nonstopmode paper.tex
   bibtex paper
   pdflatex -interaction=nonstopmode paper.tex
   pdflatex -interaction=nonstopmode paper.tex
   

6. Resume same reviewers using saved agent IDs to verify fixes (they review updated PDF too)

Agent ID Tracking (MANDATORY)

⚠️ This is NOT optional. Without agent IDs, revision cycles spawn fresh agents that don't remember their prior analysis.

Step 1: CREATE the tracking file FIRST (before spawning)

mkdir -p $SESSION_DIR/peer_review
cat > $SESSION_DIR/peer_review/agent_ids.json << 'EOF'
{
  "synthesizer": null,
  "methodology": null,
  "statistics": null,
  "impact": null,
  "cycle": 1
}
EOF

Step 2: SAVE agent IDs IMMEDIATELY after spawning

The Task tool returns an

agent_id

when an agent completes. You MUST save it:

# After each Task tool completes, read the agent_id from the result
# Then immediately update the tracking file:

# Example - after methodology reviewer completes with ID "a7df9f1":
jq '.methodology = "a7df9f1"' $SESSION_DIR/peer_review/agent_ids.json > tmp.json && \
  mv tmp.json $SESSION_DIR/peer_review/agent_ids.json

# Also update world_model.json for cross-session visibility:
jq '.agents["reviewer-methodology"] = {"id": "a7df9f1", "status": "completed"}' \
  $SESSION_DIR/world_model.json > tmp.json && mv tmp.json $SESSION_DIR/world_model.json

Step 3: USE saved IDs when resuming for revision

In revision cycles, you MUST use the

resume

parameter:

Task tool with:
  resume: "<saved-agent-id>"   # ← Use the saved ID, NOT a fresh spawn
  prompt: "Continue from where you left off. Review revision_response.md
    and verify the following fixes were made: [list issues from review]
    Re-review paper.tex and paper.pdf. Update your review JSON."

Why This Matters

Without Resume	With Resume
Fresh agent re-reads entire paper	Agent remembers prior analysis
May raise already-fixed issues	Knows what was already fixed
Circular feedback ("you already fixed this")	Productive revision cycle
Wasted tokens re-processing	Efficient targeted review

Common Failure Mode

If you see reviewers raising the same issues in cycle 2 that were fixed in cycle 1: → You forgot to use

resume

with the saved agent ID → The reviewer is a fresh spawn with no memory

Check yourself: Before every revision cycle reviewer spawn, verify:

1. agent_ids.json

   exists and has non-null IDs
2. Your Task tool call includes

   resume: "<id>"

Convergence Detection

If 3 revision cycles with >70% similarity in issues:

• Escalate to user - something is stuck
• Don't keep looping indefinitely

Review File Locations

$SESSION_DIR/peer_review/
├── methodology_review.json     # Rigor + completeness
├── statistics_review.json      # Numbers + figures
├── impact_review.json          # Contribution + provenance
├── agent_ids.json              # For resume tracking
├── codex_review.txt (optional)
├── reviewer_feedback_for_revision.json  # Combined for synthesizer
└── revision_response.md  # Synthesizer's responses

Issue Format

Each reviewer issue must have:

{
  "id": "METH-1",
  "severity": "major|minor|critical",
  "location": "Section 2.1, line 45",
  "issue": "Clear description",
  "required_action": "What to do",
  "verification": "How to verify fix"
}

Workflow Loop

1. Spawn 3 reviewers (parallel, single message)
2. Wait for all reviews (poll for completion)
3. Check verdicts:
   - All 3 ACCEPT? → Done, paper passes
   - Any REVISE/REJECT? → Revision cycle
4. Revision cycle:
   a. Combine feedback for synthesizer
   b. Resume synthesizer for revisions (use saved agent ID)
   c. Resume reviewers to verify (use saved agent IDs)
   d. Go to step 3
5. Max 3 cycles, then escalate

Rigorous review. Actionable feedback. Unanimous acceptance.