{"abstract":"Withholding collapses to a single period's share of tax despite prior withholding being subtracted.","category":"Payroll withholding rules","checks":8,"contract":"Input {ytd_wages, ytd_wh, wage, period_index k (1-based, includes this period), periods}. Annualized = (ytd_wages + wage)*periods/k exactly. Annual tax: 0% to 10,000.00, 12% to 40,000.00, 24% above. Tax to date = annual tax * k/periods. Withholding = max(0, tax_to_date - ytd_wh) rounded half-up at the end.","evaluation_group":"w2-payroll-withholding-cumulative-wage-method","failed_approach":"The attempt uses periods elapsed before this one, omitting the current period's share.","family":"w2-payroll-withholding-cumulative-wage-method-tax-to-date-proration","id":"FA-59186","implementations":{"attempt":{"sha256":"a562e1944d2072577f74ca489918580e9e5b4b9b851289aa9787dcaba7cd3b42","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nimport math\nN = 1\nobservations = []\ndef solve(x):\n    k = x['period_index']\n    total = x['ytd_wages'] + x['wage']\n    annual = Fraction(total * x['periods'], k)\n    def tax(a):\n        t = Fraction(0)\n        if a > 1000000:\n            t += (min(a, 4000000) - 1000000) * Fraction(12, 100)\n        if a > 4000000:\n            t += (a - 4000000) * Fraction(24, 100)\n        return t\n    due = tax(annual) * (k - 1) / x['periods']\n    wh = max(0, due - x['ytd_wh'])\n    return math.floor(wh + Fraction(1, 2))\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'ytd_wages': 7060061, 'ytd_wh': 424656, 'wage': 491569, 'period_index': 24, 'periods': 52}, 1110812), ('regression', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('partial-repair probe', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('partial-repair probe', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('normal control', {'ytd_wages': 2182615, 'ytd_wh': 379871, 'wage': 414670, 'period_index': 20, 'periods': 26}, 0), ('normal control', {'ytd_wages': 3504142, 'ytd_wh': 423680, 'wage': 219362, 'period_index': 26, 'periods': 26}, 0), ('normal control', {'ytd_wages': 953768, 'ytd_wh': 32122, 'wage': 67782, 'period_index': 21, 'periods': 26}, 0), ('normal control', {'ytd_wages': 185427, 'ytd_wh': 36091, 'wage': 108159, 'period_index': 2, 'periods': 12}, 0)], [('regression', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('regression', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('partial-repair probe', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('partial-repair probe', {'ytd_wages': 0, 'ytd_wh': 1, 'wage': 366567, 'period_index': 1, 'periods': 12}, 37975), ('normal control', {'ytd_wages': 2268722, 'ytd_wh': 255963, 'wage': 370037, 'period_index': 8, 'periods': 12}, 0), ('normal control', {'ytd_wages': 141603, 'ytd_wh': 11497, 'wage': 15563, 'period_index': 7, 'periods': 12}, 0), ('normal control', {'ytd_wages': 29165, 'ytd_wh': 3348, 'wage': 5167, 'period_index': 8, 'periods': 52}, 0), ('normal control', {'ytd_wages': 32735, 'ytd_wh': 3167, 'wage': 155253, 'period_index': 2, 'periods': 12}, 0)], [('regression', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('regression', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('partial-repair probe', {'ytd_wages': 0, 'ytd_wh': 1, 'wage': 366567, 'period_index': 1, 'periods': 12}, 37975), ('partial-repair probe', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('normal control', {'ytd_wages': 507467, 'ytd_wh': 39498, 'wage': 384392, 'period_index': 10, 'periods': 12}, 0), ('normal control', {'ytd_wages': 2675147, 'ytd_wh': 522064, 'wage': 171429, 'period_index': 26, 'periods': 26}, 0), ('normal control', {'ytd_wages': 5196549, 'ytd_wh': 1001382, 'wage': 210690, 'period_index': 42, 'periods': 52}, 0), ('normal control', {'ytd_wages': 1078164, 'ytd_wh': 161049, 'wage': 458540, 'period_index': 6, 'periods': 12}, 0)], [('regression', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('regression', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('partial-repair probe', {'ytd_wages': 2724662, 'ytd_wh': 86701, 'wage': 332622, 'period_index': 7, 'periods': 52}, 566278), ('partial-repair probe', {'ytd_wages': 1665992, 'ytd_wh': 58323, 'wage': 387492, 'period_index': 4, 'periods': 12}, 234513), ('normal control', {'ytd_wages': 978329, 'ytd_wh': 122337, 'wage': 298469, 'period_index': 9, 'periods': 26}, 0), ('normal control', {'ytd_wages': 3416481, 'ytd_wh': 668694, 'wage': 525477, 'period_index': 15, 'periods': 26}, 0), ('normal control', {'ytd_wages': 78474, 'ytd_wh': 12616, 'wage': 2681, 'period_index': 35, 'periods': 52}, 0), ('normal control', {'ytd_wages': 780083, 'ytd_wh': 115357, 'wage': 60520, 'period_index': 12, 'periods': 26}, 0)], [('regression', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('regression', {'ytd_wages': 2724662, 'ytd_wh': 86701, 'wage': 332622, 'period_index': 7, 'periods': 52}, 566278), ('partial-repair probe', {'ytd_wages': 3055036, 'ytd_wh': 132701, 'wage': 516493, 'period_index': 12, 'periods': 12}, 175882), ('partial-repair probe', {'ytd_wages': 17382079, 'ytd_wh': 703861, 'wage': 564605, 'period_index': 25, 'periods': 52}, 3314882), ('normal control', {'ytd_wages': 343578, 'ytd_wh': 51820, 'wage': 340882, 'period_index': 8, 'periods': 12}, 0), ('normal control', {'ytd_wages': 30504, 'ytd_wh': 1425, 'wage': 14017, 'period_index': 11, 'periods': 12}, 0), ('normal control', {'ytd_wages': 255421, 'ytd_wh': 47741, 'wage': 37513, 'period_index': 11, 'periods': 12}, 0), ('normal control', {'ytd_wages': 4101816, 'ytd_wh': 781382, 'wage': 93819, 'period_index': 46, 'periods': 52}, 0)]]\nfor i, (label, args, expected) in enumerate(fixtures[N-1]):\n    check(\"%s %d\" % (label, i), solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"f9cb3a6eba71f223c861fd432138e6c5288cb96afb90adcc4398d07989160031","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nimport math\nN = 1\nobservations = []\ndef solve(x):\n    k = x['period_index']\n    total = x['ytd_wages'] + x['wage']\n    annual = Fraction(total * x['periods'], k)\n    def tax(a):\n        t = Fraction(0)\n        if a > 1000000:\n            t += (min(a, 4000000) - 1000000) * Fraction(12, 100)\n        if a > 4000000:\n            t += (a - 4000000) * Fraction(24, 100)\n        return t\n    due = tax(annual) / x['periods']\n    wh = max(0, due - x['ytd_wh'])\n    return math.floor(wh + Fraction(1, 2))\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'ytd_wages': 7060061, 'ytd_wh': 424656, 'wage': 491569, 'period_index': 24, 'periods': 52}, 1110812), ('regression', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('partial-repair probe', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('partial-repair probe', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('normal control', {'ytd_wages': 2182615, 'ytd_wh': 379871, 'wage': 414670, 'period_index': 20, 'periods': 26}, 0), ('normal control', {'ytd_wages': 3504142, 'ytd_wh': 423680, 'wage': 219362, 'period_index': 26, 'periods': 26}, 0), ('normal control', {'ytd_wages': 953768, 'ytd_wh': 32122, 'wage': 67782, 'period_index': 21, 'periods': 26}, 0), ('normal control', {'ytd_wages': 185427, 'ytd_wh': 36091, 'wage': 108159, 'period_index': 2, 'periods': 12}, 0)], [('regression', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('regression', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('partial-repair probe', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('partial-repair probe', {'ytd_wages': 0, 'ytd_wh': 1, 'wage': 366567, 'period_index': 1, 'periods': 12}, 37975), ('normal control', {'ytd_wages': 2268722, 'ytd_wh': 255963, 'wage': 370037, 'period_index': 8, 'periods': 12}, 0), ('normal control', {'ytd_wages': 141603, 'ytd_wh': 11497, 'wage': 15563, 'period_index': 7, 'periods': 12}, 0), ('normal control', {'ytd_wages': 29165, 'ytd_wh': 3348, 'wage': 5167, 'period_index': 8, 'periods': 52}, 0), ('normal control', {'ytd_wages': 32735, 'ytd_wh': 3167, 'wage': 155253, 'period_index': 2, 'periods': 12}, 0)], [('regression', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('regression', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('partial-repair probe', {'ytd_wages': 0, 'ytd_wh': 1, 'wage': 366567, 'period_index': 1, 'periods': 12}, 37975), ('partial-repair probe', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('normal control', {'ytd_wages': 507467, 'ytd_wh': 39498, 'wage': 384392, 'period_index': 10, 'periods': 12}, 0), ('normal control', {'ytd_wages': 2675147, 'ytd_wh': 522064, 'wage': 171429, 'period_index': 26, 'periods': 26}, 0), ('normal control', {'ytd_wages': 5196549, 'ytd_wh': 1001382, 'wage': 210690, 'period_index': 42, 'periods': 52}, 0), ('normal control', {'ytd_wages': 1078164, 'ytd_wh': 161049, 'wage': 458540, 'period_index': 6, 'periods': 12}, 0)], [('regression', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('regression', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('partial-repair probe', {'ytd_wages': 2724662, 'ytd_wh': 86701, 'wage': 332622, 'period_index': 7, 'periods': 52}, 566278), ('partial-repair probe', {'ytd_wages': 1665992, 'ytd_wh': 58323, 'wage': 387492, 'period_index': 4, 'periods': 12}, 234513), ('normal control', {'ytd_wages': 978329, 'ytd_wh': 122337, 'wage': 298469, 'period_index': 9, 'periods': 26}, 0), ('normal control', {'ytd_wages': 3416481, 'ytd_wh': 668694, 'wage': 525477, 'period_index': 15, 'periods': 26}, 0), ('normal control', {'ytd_wages': 78474, 'ytd_wh': 12616, 'wage': 2681, 'period_index': 35, 'periods': 52}, 0), ('normal control', {'ytd_wages': 780083, 'ytd_wh': 115357, 'wage': 60520, 'period_index': 12, 'periods': 26}, 0)], [('regression', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('regression', {'ytd_wages': 2724662, 'ytd_wh': 86701, 'wage': 332622, 'period_index': 7, 'periods': 52}, 566278), ('partial-repair probe', {'ytd_wages': 3055036, 'ytd_wh': 132701, 'wage': 516493, 'period_index': 12, 'periods': 12}, 175882), ('partial-repair probe', {'ytd_wages': 17382079, 'ytd_wh': 703861, 'wage': 564605, 'period_index': 25, 'periods': 52}, 3314882), ('normal control', {'ytd_wages': 343578, 'ytd_wh': 51820, 'wage': 340882, 'period_index': 8, 'periods': 12}, 0), ('normal control', {'ytd_wages': 30504, 'ytd_wh': 1425, 'wage': 14017, 'period_index': 11, 'periods': 12}, 0), ('normal control', {'ytd_wages': 255421, 'ytd_wh': 47741, 'wage': 37513, 'period_index': 11, 'periods': 12}, 0), ('normal control', {'ytd_wages': 4101816, 'ytd_wh': 781382, 'wage': 93819, 'period_index': 46, 'periods': 52}, 0)]]\nfor i, (label, args, expected) in enumerate(fixtures[N-1]):\n    check(\"%s %d\" % (label, i), solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"fixed":{"sha256":"b90fe5cc9125bc1992fc792f0dda608f0bbf3a46bcab25b3fd95ad5e65af1857","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nimport math\nN = 1\nobservations = []\ndef solve(x):\n    k = x['period_index']\n    total = x['ytd_wages'] + x['wage']\n    annual = Fraction(total * x['periods'], k)\n    def tax(a):\n        t = Fraction(0)\n        if a > 1000000:\n            t += (min(a, 4000000) - 1000000) * Fraction(12, 100)\n        if a > 4000000:\n            t += (a - 4000000) * Fraction(24, 100)\n        return t\n    due = tax(annual) * k / x['periods']\n    wh = max(0, due - x['ytd_wh'])\n    return math.floor(wh + Fraction(1, 2))\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'ytd_wages': 7060061, 'ytd_wh': 424656, 'wage': 491569, 'period_index': 24, 'periods': 52}, 1110812), ('regression', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('partial-repair probe', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('partial-repair probe', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('normal control', {'ytd_wages': 2182615, 'ytd_wh': 379871, 'wage': 414670, 'period_index': 20, 'periods': 26}, 0), ('normal control', {'ytd_wages': 3504142, 'ytd_wh': 423680, 'wage': 219362, 'period_index': 26, 'periods': 26}, 0), ('normal control', {'ytd_wages': 953768, 'ytd_wh': 32122, 'wage': 67782, 'period_index': 21, 'periods': 26}, 0), ('normal control', {'ytd_wages': 185427, 'ytd_wh': 36091, 'wage': 108159, 'period_index': 2, 'periods': 12}, 0)], [('regression', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('regression', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('partial-repair probe', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('partial-repair probe', {'ytd_wages': 0, 'ytd_wh': 1, 'wage': 366567, 'period_index': 1, 'periods': 12}, 37975), ('normal control', {'ytd_wages': 2268722, 'ytd_wh': 255963, 'wage': 370037, 'period_index': 8, 'periods': 12}, 0), ('normal control', {'ytd_wages': 141603, 'ytd_wh': 11497, 'wage': 15563, 'period_index': 7, 'periods': 12}, 0), ('normal control', {'ytd_wages': 29165, 'ytd_wh': 3348, 'wage': 5167, 'period_index': 8, 'periods': 52}, 0), ('normal control', {'ytd_wages': 32735, 'ytd_wh': 3167, 'wage': 155253, 'period_index': 2, 'periods': 12}, 0)], [('regression', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('regression', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('partial-repair probe', {'ytd_wages': 0, 'ytd_wh': 1, 'wage': 366567, 'period_index': 1, 'periods': 12}, 37975), ('partial-repair probe', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('normal control', {'ytd_wages': 507467, 'ytd_wh': 39498, 'wage': 384392, 'period_index': 10, 'periods': 12}, 0), ('normal control', {'ytd_wages': 2675147, 'ytd_wh': 522064, 'wage': 171429, 'period_index': 26, 'periods': 26}, 0), ('normal control', {'ytd_wages': 5196549, 'ytd_wh': 1001382, 'wage': 210690, 'period_index': 42, 'periods': 52}, 0), ('normal control', {'ytd_wages': 1078164, 'ytd_wh': 161049, 'wage': 458540, 'period_index': 6, 'periods': 12}, 0)], [('regression', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('regression', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('partial-repair probe', {'ytd_wages': 2724662, 'ytd_wh': 86701, 'wage': 332622, 'period_index': 7, 'periods': 52}, 566278), ('partial-repair probe', {'ytd_wages': 1665992, 'ytd_wh': 58323, 'wage': 387492, 'period_index': 4, 'periods': 12}, 234513), ('normal control', {'ytd_wages': 978329, 'ytd_wh': 122337, 'wage': 298469, 'period_index': 9, 'periods': 26}, 0), ('normal control', {'ytd_wages': 3416481, 'ytd_wh': 668694, 'wage': 525477, 'period_index': 15, 'periods': 26}, 0), ('normal control', {'ytd_wages': 78474, 'ytd_wh': 12616, 'wage': 2681, 'period_index': 35, 'periods': 52}, 0), ('normal control', {'ytd_wages': 780083, 'ytd_wh': 115357, 'wage': 60520, 'period_index': 12, 'periods': 26}, 0)], [('regression', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('regression', {'ytd_wages': 2724662, 'ytd_wh': 86701, 'wage': 332622, 'period_index': 7, 'periods': 52}, 566278), ('partial-repair probe', {'ytd_wages': 3055036, 'ytd_wh': 132701, 'wage': 516493, 'period_index': 12, 'periods': 12}, 175882), ('partial-repair probe', {'ytd_wages': 17382079, 'ytd_wh': 703861, 'wage': 564605, 'period_index': 25, 'periods': 52}, 3314882), ('normal control', {'ytd_wages': 343578, 'ytd_wh': 51820, 'wage': 340882, 'period_index': 8, 'periods': 12}, 0), ('normal control', {'ytd_wages': 30504, 'ytd_wh': 1425, 'wage': 14017, 'period_index': 11, 'periods': 12}, 0), ('normal control', {'ytd_wages': 255421, 'ytd_wh': 47741, 'wage': 37513, 'period_index': 11, 'periods': 12}, 0), ('normal control', {'ytd_wages': 4101816, 'ytd_wh': 781382, 'wage': 93819, 'period_index': 46, 'periods': 52}, 0)]]\nfor i, (label, args, expected) in enumerate(fixtures[N-1]):\n    check(\"%s %d\" % (label, i), solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"A deterministic teaching model of a stipulated payroll rule with toy thresholds and rates. It makes no claim of conformance to any tax authority, statute or jurisdiction and is not payroll software. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-payroll-withholding-cumulative-wage-method-tax-to-date-proration","generated_at":"2026-09-29T14:46:33.822294+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"The cumulative method spreads irregular pay across elapsed periods and trues up against prior withholding.","repair":"Restore the contract rule at the tax to date proration step: use `due = tax(annual) * k / x['periods']`.","root_cause":"The annual tax is de-annualized for one period instead of all elapsed periods.","sha256":"9e891c79467b8dc706d5bfcb380cd3db4d9ba420251458aae02ee8c79272eb99","title":"Cumulative wages withholding method: tax to date proration · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":46.657,"exit_code":1,"observations":[{"actual":1046834,"check":"regression 0","expected":1110812,"passed":false},{"actual":1597460,"check":"regression 1","expected":1684840,"passed":false},{"actual":0,"check":"partial-repair probe 2","expected":15666,"passed":false},{"actual":1065901,"check":"partial-repair probe 3","expected":1157747,"passed":false},{"actual":0,"check":"normal control 4","expected":0,"passed":true},{"actual":0,"check":"normal control 5","expected":0,"passed":true},{"actual":0,"check":"normal control 6","expected":0,"passed":true},{"actual":0,"check":"normal control 7","expected":0,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 0\", \"actual\": 1046834, \"expected\": 1110812, \"passed\": false}, {\"check\": \"regression 1\", \"actual\": 1597460, \"expected\": 1684840, 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