Read Exp

Review Experiments

Review experiment design for fairness and rigor, then produce a prioritized plan for reproduction.

Workflow

- [ ] Step 1: Load claims and paper
- [ ] Step 2: Review experiment design
- [ ] Step 3: Plan reproduction
- [ ] Step 4: Save report

Step 1: Load Claims and Paper

• {paper_dir}/reports/check_claim.json — filter claims with evidence_type including "experiment"

Each claim's id, source, quote, claim, evidence_type fields should be copied as-is into the output — do not modify them.

• Paper text from {paper_dir}/metadata/{arxiv_id}/mathpix/{arxiv_id}.md
• Repo README.md — for setup instructions, datasets, checkpoints, commands

Step 2: Review Experiment Design

For each experiment claim, assess the design quality:

• Fair comparison — same training setting, same data splits, same evaluation protocol as baselines?
• Ablation sufficiency — are the key components isolated and tested?
• Statistical rigor — are results averaged over multiple runs? error bars or std reported?
• Baseline selection — are the baselines relevant and recent enough?
• Cherry-picking risk — are only favorable configurations shown, or is coverage broad?

Each design issue:

{
  "id": 9,
  "source": "experiments",
  "quote": "Results are averaged over 5 independent runs",
  "claim": "Main results are averaged over 5 runs",
  "evidence_type": ["experiment"],
  "status": "warning",
  "reason": "No standard deviation or confidence interval reported despite claiming 5 runs",
  "suggest": "Add standard deviations or confidence intervals across the 5 runs in Table 2."
}

When status is warning or error, include a suggest field with a concrete one-sentence fix.

Step 3: Plan Reproduction

Scan the repo for datasets and checkpoints. For each experiment claim, assign:

• priority: high (main results) / medium (secondary) / low (very expensive)
• feasibility: ready (can run easily) / blocked (missing data, needs training, or too expensive)
• mode: eval / train / other
• command: exact command to run
• goal: key metric(s) and target value(s) from the paper

Data/checkpoint availability:

• provided — already exists locally
• downloadable — public URL, < 1 GB
• restricted — registration required, or > 1 GB

Step 4: Save Report

Output: {paper_dir}/reports/check_exp.json

{
  "summary": {
    "total_claims": 10,
    "design_issues": 3,
    "experiments": 5,
    "high": 2, "medium": 2, "low": 1,
    "ready": 1, "blocked": 4
  },
  "results": [
    {
      "id": 9,
      "source": "experiments",
      "quote": "Results are averaged over 5 independent runs",
      "claim": "Main results are averaged over 5 runs",
      "evidence_type": ["experiment"],
      "status": "warning",
      "reason": "No standard deviation or confidence interval reported despite claiming 5 runs",
      "suggest": "Add standard deviations or confidence intervals across the 5 runs in Table 2."
    }
  ],
  "plan": [
    {
      "id": 4,
      "source": "experiments",
      "quote": "Our method achieves 76.5% top-1 accuracy on CIFAR-100",
      "claim": "Proposed distillation method reaches 76.5% on CIFAR-100",
      "evidence_type": ["experiment"],
      "priority": "high",
      "feasibility": "ready",
      "mode": "eval",
      "command": "python eval.py --config configs/cifar100_resnet32x4.yaml --ckpt checkpoints/best.pth",
      "goal": {"top1_acc": 76.5}
    }
  ],
}

Coverage Rule

Every claim with experiment in evidence_type MUST appear in either results or plan. No claim may be silently skipped. If a claim is not worth a full review, still include it in results with status: "pass" and a brief reason.

Tips

• Missing error bars on a table that claims "average over N trials" is a warning
• Comparing against a 3-year-old baseline when newer ones exist is a warning
• Using different training epochs or data for your method vs baselines is an error