fresh_audit/summary.json

{
  "checks": [
    {
      "check": "walk stitching identity",
      "value": 0.0,
      "criterion": "< 1e-14",
      "passed": true
    },
    {
      "check": "Monte Carlo endpoint error",
      "value": 0.004408945788646434,
      "criterion": "< 0.04",
      "passed": true
    },
    {
      "check": "sampling-error exponent",
      "value": -0.5463869462377622,
      "criterion": "-0.9 to -0.15",
      "passed": true
    },
    {
      "check": "JL 95% relative distortion",
      "value": 0.13452640911381045,
      "criterion": "< 0.2",
      "passed": true
    }
  ],
  "scope": "Small graph audit of exact walk stitching, Monte Carlo kernel estimation, and JL distance preservation; the benchmark graph tasks were not rerun.",
  "paper_id": "NvJPE1oiKd",
  "title": "Computationally-efficient Graph Modeling with Refined Graph Random Features",
  "seed": 3082026,
  "executed_at": "2026-08-02T16:33:12.957586+00:00",
  "all_checks_passed": true,
  "environment": {
    "python": "3.10.12",
    "numpy": "1.26.4",
    "scipy": "1.14.0",
    "platform": "Linux-5.15.0-139-generic-x86_64-with-glibc2.35"
  },
  "reference_evidence": {
    "space": "Srishti280992/repro-computationally-efficient-graph-modeling-with-refined-graph-random-features",
    "sha": "616ebbd7ec1751a68a0bf930608b2eb082cc50ed",
    "relationship": "separately attributed public reference"
  }
}