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Running
File size: 2,776 Bytes
e0f5bb6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | """Route 1: machine-readable completeness audit of the paper's GAN experiment."""
from __future__ import annotations
import json
from pathlib import Path
def run(output_dir: Path) -> dict[str, object]:
output_dir.mkdir(parents=True, exist_ok=True)
disclosed = {
"datasets": ["CIFAR-10 32x32", "STL-10 64x64"],
"architectures": ["ResNet", "CNN"],
"framework": "generally follows improved Wasserstein GAN",
"optimizer": "simultaneous Adam-DA for generator and discriminator",
"learning_rate": 0.0002,
"batch_size": 64,
"beta_sweep_fixed_rho": 0.9,
"rho_sweep_fixed_beta": 0.0,
"rho_values_from_table_2": [0.3, 0.5, 0.7, 0.9],
"metric": "cumulative average parameter-gradient L1 norm and Inception Score",
}
required_reproduction_fields = {
"executable_author_code": False,
"exact_resnet_definition": False,
"exact_cnn_definition": False,
"dataset_split_and_preprocessing": False,
"latent_distribution_and_dimension": False,
"critic_to_generator_update_ratio": False,
"gradient_penalty_coefficient": False,
"adam_epsilon": False,
"training_seeds": False,
"main_experiment_training_horizon": False,
"inception_implementation_and_sample_count": False,
"raw_gradient_norm_timeseries": False,
"per_seed_inception_scores": False,
}
payload = {
"claim": 5,
"route": 1,
"route_name": "source completeness audit",
"source": {
"ar5iv_url": "https://ar5iv.labs.arxiv.org/html/2605.19392",
"retrieval_date_utc": "2026-07-29",
"html_sha256": "c7ebf813dc871eba1c0c93542fcf0a7d599c7c4d44a543a0000270ce48ae7998",
"source_tar_sha256": "9922a66ab5708f357aa8be09f207565791a6488f48b0d8d2cf27011964521265",
"anchors": [
"Section 5 Experimental Setting and Results",
"Appendix D / Additional Materials for Section 5",
"Table 1",
"Table 2",
],
},
"disclosed": disclosed,
"required_reproduction_fields": required_reproduction_fields,
"missing_critical_field_count": sum(not value for value in required_reproduction_fields.values()),
"author_source_uniquely_executable": all(required_reproduction_fields.values()),
"verdict": "BLOCKED",
"reason": (
"The source fixes the headline setting but does not identify a unique "
"architecture, training process, seed distribution, or IS evaluator."
),
}
(output_dir / "claim5_route1_source_audit.json").write_text(
json.dumps(payload, indent=2) + "\n"
)
return payload
|