job-run/summary.json

{
  "elapsed_seconds": 3.4524173270001484,
  "environment": {
    "device": "CPU",
    "matplotlib": "3.9.2",
    "numpy": "1.26.4",
    "platform": "Linux-6.12.94-123.190.amzn2023.x86_64-x86_64-with-glibc2.36",
    "python": "3.12.12"
  },
  "lower_bound": {
    "altered_tail_loglog_slope": -1.99974228320285,
    "altered_tail_r2": 0.9999999898005991,
    "paper_optimal_c_max": 0.998813608370125,
    "paper_optimal_c_min": 0.45200130664867044,
    "paper_tail_loglog_slope": -0.9911752799013261,
    "paper_tail_r2": 0.9999830005474569,
    "paper_tail_scaled_excess_max": 0.10319880244799151,
    "paper_tail_scaled_excess_min": 0.0998532304334141,
    "quadrature_order": 128
  },
  "mode": "independent deterministic population numerical audit",
  "paper": {
    "arxiv": "2605.01082v1",
    "arxiv_pdf_sha256": "ca7263e8fd591b27cbd3f869d93ffe1818af27baaa439f278a27a5248ddd92b7",
    "arxiv_source_sha256": "d17612d6f31795fe581812dd7e20bf00ce5fabc7408e61411b439232b9917417",
    "official_code": null,
    "official_code_search_result": "No executable author repository linked in arXiv/OpenReview or found by title/arXiv/author search.",
    "openreview_id": "mrtg4NmvAe",
    "title": "Networked Information Aggregation for Binary Classification"
  },
  "projection": {
    "max_decomposition_abs_error": 6.245004513516506e-17,
    "max_orthogonality_abs": 4.1179353763715065e-17,
    "min_nonoptimal_decomposition_abs_error": 0.2373465204309922,
    "min_nonoptimal_residual_l2": 0.17030339354175572,
    "min_pinsker_slack": 0.0022688127986700557
  },
  "protocol_and_upper_bound": {
    "covered_bound_passes": 28,
    "covered_cells": 28,
    "max_covered_excess_to_bound_ratio": 3.7490538702120246e-07,
    "max_logit_loss_increase": 1.1102230246251565e-16,
    "mean_probability_minus_logit": 0.24415601790216954,
    "no_coverage_naive_bound_violations": 4,
    "no_coverage_plateau_excess": 0.5022822094535031,
    "probability_comparison_cells": 28,
    "probability_worse_cells": 28
  },
  "verdicts": {
    "claim_1": "SUPPORTED under all stated Theorem 3.7 assumptions; inequality audit is not a rate proof.",
    "claim_2": "SUPPORTED on the exact Gaussian end-of-pass family; quadrature is not a proof.",
    "claim_3": "REPRODUCED to numerical precision with nonoptimal-reference control.",
    "claim_4": "REPRODUCED to numerical precision with Pinsker and nonoptimal-reference control.",
    "claim_5": "REPRODUCED on exact finite populations; probability-passing control is inferior in nearly all cells."
  }
}