paper-review-sim

name	paper-review-sim
description	Simulate a NeurIPS/SC/ICSE-style peer review with 5 reviewer personas (HPC, ML, Stats, Reproducibility, Devil's Advocate). Use before paper submission, after major methodology changes, or when stress-testing a draft against expected objections. Each reviewer verifies claims against actual result data.
auto-activate	false

Paper Review Simulation

Use when preparing a paper draft for submission, stress-testing claims before writing, or wanting structured feedback on methodology, presentation, or rigor. Simulates a 5-reviewer conference panel.

Trigger: When user types /paper-review-sim with optional arguments.

Arguments

• $ARGUMENTS --- path to paper draft file(s) or specific section to review. If omitted, searches for docs/paper_draft.md or asks the user.

Iron Law

NO REVIEWER MAY ACCEPT A CLAIM WITHOUT TRACING IT TO DATA.
Every number, percentage, comparison, or performance claim in the paper must be
verified against actual files in results/evaluation/ or results/augmentation/.
"The paper says X" is not evidence. The data file says X is evidence.

Anti-Rationalization Table

Excuse	Reality
"The numbers are from a previous session"	Previous sessions can have bugs. Verify against current disk state.
"This is just a rough draft"	Rough drafts with wrong numbers become final drafts with wrong numbers.
"I'll verify during camera-ready"	Camera-ready is too late. conference reviewers see the submitted version.
"The reviewer personas are too harsh"	conference acceptance rate is ~25%. Real reviewers ARE this harsh.
"One reviewer disagreeing is fine"	One dissenting reviewer can sink a paper. Address all concerns.

Red Flags --- STOP

If any of these occur, halt the review and flag immediately:

• A number in the paper cannot be traced to any file on disk
• A claim about model A vs model B contradicts the actual result JSONs
• "All models" or "consistently" used without checking every model's data
• Performance claims based on wall-clock time (unreliable --- see Timing Caveat below)
• Missing error bars, confidence intervals, or sample sizes for quantitative claims
• Related work section missing key papers (LASSI, CodeRosetta, HPC-Coder-v2, OMPify, HPCorpus)

Workflow

Phase 1: Locate and Read the Draft

1. Find the paper draft at the path in $ARGUMENTS or docs/paper_draft.md.
2. Read the full draft to understand structure, claims, and data references.
3. Identify every quantitative claim (pass rates, failure counts, comparisons).

Verification gate: Draft located and read. All quantitative claims catalogued.

Phase 2: Data Verification Sweep

Before spawning reviewers, verify every number in the paper against actual data:

# Count result files per model
for model_dir in {{PROJECT_ROOT}}/results/evaluation/*/; do
  echo "$(basename $model_dir): $(find "$model_dir" -name '*.json' | wc -l) files"
done

Read specific result JSONs to verify claimed pass rates. Use overall_status field (not top-level run_status) as the authoritative verdict.

Build a verification table:

=== DATA VERIFICATION ===
| Claim in paper | Source file(s) | Verified value | Match? |
|----------------|---------------|----------------|--------|
| "34% overall pass rate" | eval_summary.json | <actual> | YES/NO |
| "BUILD_FAIL is 36%" | <files> | <actual> | YES/NO |

Verification gate: Every number traced to a file. Any mismatches flagged BEFORE the review proceeds.

Phase 3: Spawn Review Panel

Launch 5 subagents in parallel. Each reviewer gets:

• The full paper draft (or relevant section)
• The data verification table from Phase 2
• Their specific review focus (below)
• Access to read files in results/evaluation/ and results/augmentation/

Reviewer R1: HPC Domain Expert

Focus: GPU architecture correctness, performance claims, parallelism semantics.

Review checklist:

• Are CUDA/OpenMP/OpenCL semantics described accurately?
• Do performance claims use kernel time (not wall-clock)?
• Is the CUDA-to-OpenMP thread mapping discussion correct (SPMD vs fork-join)?
• Are warp divergence, memory coalescing, and occupancy discussed where relevant?
• Is the hardware (RTX 4070, CUDA 12, nvcc from HPC SDK 24.3) correctly documented?
• Are data movement patterns (cudaMemcpy elimination in OMP translations) addressed?

Score: 0-100. Must cite specific paper sections and result files.

Reviewer R2: ML/AI Researcher

Focus: LLM evaluation methodology, model comparison fairness, prompt engineering.

Review checklist:

• Is the comparison between models fair? Same prompt, same retries, same specs?
• Are model versions pinned (exact model IDs, not just "Claude" or "Gemini")?
• Is the thinking/reasoning confound addressed (Gemini Flash Lite thinking OFF by default)?
• Are temperature and sampling parameters documented?
• Is the prompt format (system prompt, code context, augmentation) fully specified?
• Are self-repair attempts (retry mechanism) documented and controlled across models?
• Is BUILD_FAIL analysis distinguishing model capability from prompt issues?

Score: 0-100. Must cite specific paper sections and result files.

Reviewer R3: Benchmarking Methodologist

Focus: Statistical rigor, reproducibility, baseline validity.

Review checklist:

• Is sample size (N specs x M models x K directions) sufficient for claims?
• Are results reported with appropriate statistical measures (not just percentages)?
• Is the baseline clearly defined?
• Are known-fail items excluded consistently across all analyses?
• Are invariance claims verified against data?
• Are per-item anomalies addressed as noise vs signal?
• Is the failure taxonomy consistently applied?

Score: 0-100. Must cite specific paper sections and result files.

Reviewer R4: Reproducibility Reviewer

Focus: Environment capture, result versioning, open-source readiness.

Review checklist:

• Can the full pipeline be re-run from a fresh clone?
• Are all dependencies documented (Python version, CUDA SDK, compiler flags)?
• Are all data source versions (submodule commits, dataset versions) recorded?
• Are API keys the only external dependency? Are they documented?
• Is --project-root documented as required (auto-detection broken)?
• Are result JSONs committed and version-controlled?
• Is the spec schema (v1.0.0) documented?
• Could a reviewer reconstruct any table or figure from the raw data?

Score: 0-100. Must cite specific paper sections and result files.

Reviewer R5: Devil's Advocate

Focus: Strongest objections, alternative explanations, novelty assessment.

Review checklist:

• What is the strongest argument that this work is NOT novel? (Compare to SWE-bench, HumanEval, TransCoder, LASSI, CodeRosetta, HPC-Coder-v2, OMPify)
• Could the results be explained by prompt engineering rather than model capability?
• Is "22% pass rate" actually useful? What would a practitioner do with this?
• Are the benchmark kernels representative of real HPC workloads?
• Could a simpler approach (regex-based translation, template matching) achieve similar results?
• What is the paper's contribution beyond "we ran LLMs on code and measured pass rates"?
• Is the threat to validity section honest about limitations?

Score: 0-100. Must cite specific paper sections and result files.

Verification gate: All 5 reviewers have returned scores and written reviews.

Phase 4: Aggregate and Synthesize

Collect all reviews and produce a unified panel report:

=== conference SIMULATED REVIEW PANEL ===

PAPER: <title>
DATE:  <date>

┌─────────┬───────┬──────────────────────────────────────────┐
│ Reviewer │ Score │ One-line verdict                         │
├─────────┼───────┼──────────────────────────────────────────┤
│ R1 (HPC)│  XX   │ <verdict>                                │
│ R2 (ML) │  XX   │ <verdict>                                │
│ R3 (Stats)│ XX  │ <verdict>                                │
│ R4 (Repro)│ XX  │ <verdict>                                │
│ R5 (Adv) │  XX  │ <verdict>                                │
├─────────┼───────┼──────────────────────────────────────────┤
│ AVERAGE  │  XX   │                                          │
└─────────┴───────┴──────────────────────────────────────────┘

conference CRITERIA:
  Novelty:          [1-5] <justification>
  Reproducibility:  [1-5] <justification>
  Significance:     [1-5] <justification>
  Presentation:     [1-5] <justification>

DECISION: [STRONG ACCEPT / WEAK ACCEPT / BORDERLINE / WEAK REJECT / STRONG REJECT]

=== PRIORITY-RANKED ACTION ITEMS ===

P0 (must fix before submission):
1. <action> --- raised by R<N>
2. <action> --- raised by R<N>

P1 (strongly recommended):
1. <action> --- raised by R<N>

P2 (nice to have):
1. <action> --- raised by R<N>

=== QUESTIONS FOR AUTHORS ===
(Questions reviewers would ask in the rebuttal phase)
1. <question> --- R<N>
2. <question> --- R<N>

=== SUGGESTED EXPERIMENTS ===
(Additional experiments that would strengthen the paper)
1. <experiment> --- R<N>

Verification gate: All action items are concrete and actionable. Every data mismatch from Phase 2 appears as a P0 item.

Phase 5: Author Response Coaching

If requested, help the user draft responses to each reviewer's concerns:

• For each P0 item: propose a concrete fix with file paths and commands
• For each question: draft a factual response with data citations
• For each suggested experiment: estimate effort and prioritize

Timing Caveat (Self-Contained)

All existing eval results use timing_method: "wall_time". Sub-millisecond baseline wall times produce unreliable speedup ratios. Do NOT let the paper claim speedup numbers from speedup_ratio in result JSONs. Valid performance measurement requires nvprof/ncu for CUDA kernel time and omp_get_wtime() for OMP.

Project Context (Self-Contained)

• Project root: {{PROJECT_ROOT}}
• Paper draft: docs/paper_draft.md (or as specified in arguments)
• Results: results/ directory (check CLAUDE.md for structure)
• Check CLAUDE.md for: models, dataset counts, conference target, related work gaps
• Check .claude/rules/known-issues.md for exclusions and known failures