repro-diffusion-differentiable-resampling / evidence /gms-selected-ids-0-99-validation.json
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{
"checked_at_utc": "2026-07-19T11:19:22.402493Z",
"checks": {
"current_source_identity_and_hashes": {
"checked_author_source_sha256": {
"diffres/integators.py": "4cbcdba683cf27532b21acfa9c3295354c52cc2f4494b4063a0459629eb72954",
"diffres/resampling.py": "12b33fe23e1f62e18909cbcff9fa45db36ed65f6f943668728c9fe61795aa06e",
"diffres/tools.py": "54c60e1ac4f484532f51be57a63aacbdfb7a53ef8cef621495176ea7a843a5df",
"experiments/gms/baselines.py": "7b258df0ab4a7ca8eb5c91f6d5efbabbe359e0130db1cb9e4d3263a9241f1d5c",
"experiments/gms/diffusion.py": "886496bab31d254ac8030286ea350e48db817bd680cccc8cdda50c58a94ba774",
"experiments/rnd_keys.npy": "6e60685445936e0380220f34fc3fd15b5b7720070dc8c8f7f1148575de4ff5df",
"experiments/run_gms.sh": "c01a4c88a3ef05d6a5ffb585b9d4233b1b1a9e8c1c46d40360eab44963b3eee5",
"experiments/summary/print_gms_errs.py": "1df81cadaaf35ca5b26e8bd1ce6fc19aa4a60a2f7e161075087e518b3a0d17cd"
},
"current_commit": "767effe3e755067eb8a04422597fbf37eb8ab754",
"current_origin_matches_expected": true,
"current_tracked_worktree_clean": true
},
"raw_record_integrity_and_aggregate_recomputation": {
"diffusion_lower_is_better_wins": {
"posterior_mean_residual_squared_l2": 51,
"sliced_wasserstein_l1": 54
},
"paired_diffusion_minus_multinomial_95_percent_t_intervals": {
"posterior_mean_residual_squared_l2": {
"critical_value": 1.9842169515086827,
"lower": -0.007123449582480549,
"mean": -0.00044225546225615345,
"sample_count": 100,
"sample_standard_deviation": 0.03367169157155121,
"standard_error": 0.003367169157155121,
"upper": 0.006238938657968242
},
"sliced_wasserstein_l1": {
"critical_value": 1.9842169515086827,
"lower": -0.004659334515850839,
"mean": -0.0016648057831093993,
"sample_count": 100,
"sample_standard_deviation": 0.015091740499770325,
"standard_error": 0.0015091740499770326,
"upper": 0.0013297229496320405
}
},
"recomputed_aggregate": {
"diffusion_resampling": {
"posterior_mean_residual_squared_l2": {
"maximum": 0.13674925583503703,
"mean": 0.037390775125730545,
"minimum": 0.003946685433082616,
"population_standard_deviation": 0.029935755902808506
},
"sliced_wasserstein_l1": {
"maximum": 0.13845705348834103,
"mean": 0.08076861937013251,
"minimum": 0.049275986886894586,
"population_standard_deviation": 0.021289921970778995
}
},
"multinomial_baseline": {
"posterior_mean_residual_squared_l2": {
"maximum": 0.34224144904965786,
"mean": 0.0378330305879867,
"minimum": 0.00328472168500068,
"population_standard_deviation": 0.044287731038823226
},
"sliced_wasserstein_l1": {
"maximum": 0.19381190521839362,
"mean": 0.08243342515324191,
"minimum": 0.04778862503625366,
"population_standard_deviation": 0.02503231412513419
}
}
},
"recomputed_paired_aggregate": {
"posterior_mean_residual_squared_l2": {
"maximum": 0.10671040850944102,
"mean": -0.00044225546225615345,
"minimum": -0.24669590547035306,
"population_standard_deviation": 0.03350291009982232
},
"sliced_wasserstein_l1": {
"maximum": 0.041779128105039046,
"mean": -0.0016648057831093993,
"minimum": -0.07544369540487143,
"population_standard_deviation": 0.015016092201344656
}
},
"record_count": 100
},
"selected_protocol": "passed",
"strict_json": "passed"
},
"claim_boundary": "This check independently recomputes the recorded aggregates and paired intervals from the 100 raw JSON records while checking the current pinned source identity. It does not rerun the numerical experiment, independently reproduce the paper, or establish an outperformance or significance claim.",
"input": {
"path": "study/gms-selected-ids-0-99.json",
"sha256": "c74851b88b5df723cbfb28edb48be09eb61e7a2dab086088f90d59591b68723b"
},
"schema_version": 1,
"validation_status": "passed",
"validation_type": "post-hoc integrity and aggregate recomputation; no numerical rerun",
"validator": {
"path": "study/validate_gms_selected_result.py",
"platform": {
"machine": "arm64",
"release": "25.5.0",
"system": "Darwin"
},
"python_version": "3.13.14 (main, Jun 23 2026, 15:20:43) [Clang 22.1.3 ]",
"scipy_version": "1.16.2",
"sha256": "80841d7e4395f60bcbf06880a92288ad7bfdd7e022ea60876911277f3b3e69e5"
}
}