{"abstract":"Employees with modest coverage have imputed income on the first 50,000 of coverage.","category":"Payroll withholding rules","checks":8,"contract":"Input {coverage cents, age, months, employee_paid}. Monthly cost per 1,000.00 of coverage by age band (<25:5, <30:6, <35:8, <40:9, <45:10, <50:15, <55:23, <60:43, <65:66, <70:127, else 206 cents). Excess = max(0, coverage - 50,000.00). Imputed = max(0, excess/100000*rate*months - employee_paid), half-up. Return imputed cents.","contract_signature":"x","evaluation_group":"w2-payroll-withholding-group-term-life-imputed","failed_approach":"The attempt subtracts 50,000 cents instead of 50,000 dollars.","family":"w2-payroll-withholding-group-term-life-imputed-coverage-exclusion","id":"FA-59116","implementations":{"attempt":{"sha256":"64261d2f809a1037b370d2402e7fe44f295db6d5f4d69632628b1b7ae1d222a2","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nimport math\nN = 1\nobservations = []\ndef solve(x):\n    bands = [(25, 5), (30, 6), (35, 8), (40, 9), (45, 10), (50, 15), (55, 23), (60, 43), (65, 66), (70, 127)]\n    rate = 206\n    for upper, r in bands:\n        if x['age'] < upper:\n            rate = r\n            break\n    excess = max(0, x['coverage'] - 50000)\n    cost = Fraction(excess * rate * x['months'], 100000)\n    imputed = max(0, cost - x['employee_paid'])\n    return math.floor(imputed + Fraction(1, 2))\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'coverage': 31951582, 'age': 24, 'months': 2, 'employee_paid': 0}, 2695), ('regression', {'coverage': 5000000, 'age': 30, 'months': 6, 'employee_paid': 0}, 0), ('partial-repair probe', {'coverage': 5000000, 'age': 64, 'months': 3, 'employee_paid': 0}, 0), ('partial-repair probe', {'coverage': 23127161, 'age': 70, 'months': 4, 'employee_paid': 0}, 149368), ('normal control', {'coverage': 7307633, 'age': 24, 'months': 3, 'employee_paid': 19462}, 0), ('normal control', {'coverage': 4843457, 'age': 30, 'months': 9, 'employee_paid': 37040}, 0), ('normal control', {'coverage': 19400395, 'age': 25, 'months': 7, 'employee_paid': 36789}, 0), ('normal control', {'coverage': 22529249, 'age': 29, 'months': 1, 'employee_paid': 38785}, 0)], [('regression', {'coverage': 5000000, 'age': 30, 'months': 6, 'employee_paid': 0}, 0), ('regression', {'coverage': 5000000, 'age': 64, 'months': 3, 'employee_paid': 0}, 0), ('partial-repair probe', {'coverage': 23127161, 'age': 70, 'months': 4, 'employee_paid': 0}, 149368), ('partial-repair probe', {'coverage': 5000000, 'age': 24, 'months': 10, 'employee_paid': 0}, 0), ('normal control', {'coverage': 9261111, 'age': 29, 'months': 1, 'employee_paid': 24425}, 0), ('normal control', {'coverage': 20304981, 'age': 29, 'months': 10, 'employee_paid': 12373}, 0), ('normal control', {'coverage': 635952, 'age': 25, 'months': 4, 'employee_paid': 16597}, 0), ('normal control', {'coverage': 5000000, 'age': 29, 'months': 2, 'employee_paid': 22644}, 0)], [('regression', {'coverage': 5000000, 'age': 64, 'months': 3, 'employee_paid': 0}, 0), ('regression', {'coverage': 23127161, 'age': 70, 'months': 4, 'employee_paid': 0}, 149368), ('partial-repair probe', {'coverage': 5000000, 'age': 24, 'months': 10, 'employee_paid': 0}, 0), ('partial-repair probe', {'coverage': 26680167, 'age': 24, 'months': 8, 'employee_paid': 0}, 8672), ('normal control', {'coverage': 15550480, 'age': 24, 'months': 6, 'employee_paid': 26462}, 0), ('normal control', {'coverage': 10839712, 'age': 47, 'months': 6, 'employee_paid': 13304}, 0), ('normal control', {'coverage': 5000000, 'age': 25, 'months': 6, 'employee_paid': 12164}, 0), ('normal control', {'coverage': 25580732, 'age': 25, 'months': 5, 'employee_paid': 10831}, 0)], [('regression', {'coverage': 23127161, 'age': 70, 'months': 4, 'employee_paid': 0}, 149368), ('regression', {'coverage': 5000000, 'age': 24, 'months': 10, 'employee_paid': 0}, 0), ('partial-repair probe', {'coverage': 16856564, 'age': 70, 'months': 8, 'employee_paid': 0}, 195396), ('partial-repair probe', {'coverage': 9181727, 'age': 25, 'months': 6, 'employee_paid': 0}, 1505), ('normal control', {'coverage': 37417908, 'age': 30, 'months': 2, 'employee_paid': 18235}, 0), ('normal control', {'coverage': 23651725, 'age': 29, 'months': 9, 'employee_paid': 22226}, 0), ('normal control', {'coverage': 5000000, 'age': 29, 'months': 9, 'employee_paid': 46974}, 0), ('normal control', {'coverage': 5000000, 'age': 30, 'months': 4, 'employee_paid': 34676}, 0)], [('regression', {'coverage': 5000000, 'age': 24, 'months': 10, 'employee_paid': 0}, 0), ('regression', {'coverage': 26680167, 'age': 24, 'months': 8, 'employee_paid': 0}, 8672), ('partial-repair probe', {'coverage': 20685038, 'age': 29, 'months': 4, 'employee_paid': 4881}, 0), ('partial-repair probe', {'coverage': 15762067, 'age': 70, 'months': 10, 'employee_paid': 0}, 221699), ('normal control', {'coverage': 20510722, 'age': 24, 'months': 2, 'employee_paid': 48846}, 0), ('normal control', {'coverage': 24414800, 'age': 24, 'months': 4, 'employee_paid': 35815}, 0), ('normal control', {'coverage': 9498973, 'age': 24, 'months': 7, 'employee_paid': 18100}, 0), ('normal control', {'coverage': 22999814, 'age': 29, 'months': 10, 'employee_paid': 30014}, 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":"aff58dab86f31b05ae283ecece7deab0350a3e06b9d815008ba0c47212f51ee6","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nimport math\nN = 1\nobservations = []\ndef solve(x):\n    bands = [(25, 5), (30, 6), (35, 8), (40, 9), (45, 10), (50, 15), (55, 23), (60, 43), (65, 66), (70, 127)]\n    rate = 206\n    for upper, r in bands:\n        if x['age'] < upper:\n            rate = r\n            break\n    excess = max(0, x['coverage'])\n    cost = Fraction(excess * rate * x['months'], 100000)\n    imputed = max(0, cost - x['employee_paid'])\n    return math.floor(imputed + Fraction(1, 2))\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'coverage': 31951582, 'age': 24, 'months': 2, 'employee_paid': 0}, 2695), ('regression', {'coverage': 5000000, 'age': 30, 'months': 6, 'employee_paid': 0}, 0), ('partial-repair probe', {'coverage': 5000000, 'age': 64, 'months': 3, 'employee_paid': 0}, 0), ('partial-repair probe', {'coverage': 23127161, 'age': 70, 'months': 4, 'employee_paid': 0}, 149368), ('normal control', {'coverage': 7307633, 'age': 24, 'months': 3, 'employee_paid': 19462}, 0), ('normal control', {'coverage': 4843457, 'age': 30, 'months': 9, 'employee_paid': 37040}, 0), ('normal control', {'coverage': 19400395, 'age': 25, 'months': 7, 'employee_paid': 36789}, 0), ('normal control', {'coverage': 22529249, 'age': 29, 'months': 1, 'employee_paid': 38785}, 0)], [('regression', {'coverage': 5000000, 'age': 30, 'months': 6, 'employee_paid': 0}, 0), ('regression', {'coverage': 5000000, 'age': 64, 'months': 3, 'employee_paid': 0}, 0), ('partial-repair probe', {'coverage': 23127161, 'age': 70, 'months': 4, 'employee_paid': 0}, 149368), ('partial-repair probe', {'coverage': 5000000, 'age': 24, 'months': 10, 'employee_paid': 0}, 0), ('normal control', {'coverage': 9261111, 'age': 29, 'months': 1, 'employee_paid': 24425}, 0), ('normal control', {'coverage': 20304981, 'age': 29, 'months': 10, 'employee_paid': 12373}, 0), ('normal control', {'coverage': 635952, 'age': 25, 'months': 4, 'employee_paid': 16597}, 0), ('normal control', {'coverage': 5000000, 'age': 29, 'months': 2, 'employee_paid': 22644}, 0)], [('regression', {'coverage': 5000000, 'age': 64, 'months': 3, 'employee_paid': 0}, 0), ('regression', {'coverage': 23127161, 'age': 70, 'months': 4, 'employee_paid': 0}, 149368), ('partial-repair probe', {'coverage': 5000000, 'age': 24, 'months': 10, 'employee_paid': 0}, 0), ('partial-repair probe', {'coverage': 26680167, 'age': 24, 'months': 8, 'employee_paid': 0}, 8672), ('normal control', {'coverage': 15550480, 'age': 24, 'months': 6, 'employee_paid': 26462}, 0), ('normal control', {'coverage': 10839712, 'age': 47, 'months': 6, 'employee_paid': 13304}, 0), ('normal control', {'coverage': 5000000, 'age': 25, 'months': 6, 'employee_paid': 12164}, 0), ('normal control', {'coverage': 25580732, 'age': 25, 'months': 5, 'employee_paid': 10831}, 0)], [('regression', {'coverage': 23127161, 'age': 70, 'months': 4, 'employee_paid': 0}, 149368), ('regression', {'coverage': 5000000, 'age': 24, 'months': 10, 'employee_paid': 0}, 0), ('partial-repair probe', {'coverage': 16856564, 'age': 70, 'months': 8, 'employee_paid': 0}, 195396), ('partial-repair probe', {'coverage': 9181727, 'age': 25, 'months': 6, 'employee_paid': 0}, 1505), ('normal control', {'coverage': 37417908, 'age': 30, 'months': 2, 'employee_paid': 18235}, 0), ('normal control', {'coverage': 23651725, 'age': 29, 'months': 9, 'employee_paid': 22226}, 0), ('normal control', {'coverage': 5000000, 'age': 29, 'months': 9, 'employee_paid': 46974}, 0), ('normal control', {'coverage': 5000000, 'age': 30, 'months': 4, 'employee_paid': 34676}, 0)], [('regression', {'coverage': 5000000, 'age': 24, 'months': 10, 'employee_paid': 0}, 0), ('regression', {'coverage': 26680167, 'age': 24, 'months': 8, 'employee_paid': 0}, 8672), ('partial-repair probe', {'coverage': 20685038, 'age': 29, 'months': 4, 'employee_paid': 4881}, 0), ('partial-repair probe', {'coverage': 15762067, 'age': 70, 'months': 10, 'employee_paid': 0}, 221699), ('normal control', {'coverage': 20510722, 'age': 24, 'months': 2, 'employee_paid': 48846}, 0), ('normal control', {'coverage': 24414800, 'age': 24, 'months': 4, 'employee_paid': 35815}, 0), ('normal control', {'coverage': 9498973, 'age': 24, 'months': 7, 'employee_paid': 18100}, 0), ('normal control', {'coverage': 22999814, 'age': 29, 'months': 10, 'employee_paid': 30014}, 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-group-term-life-imputed-coverage-exclusion","generated_at":"2026-09-29T14:46:33.142400+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Imputed income for employer-paid life insurance depends on age bands, the 50,000 exclusion and after-tax employee payments.","root_cause":"The 50,000 exclusion is not subtracted from coverage.","sha256":"c61d6449d846436a2e215ba9e3244659fc34d729372f65979d27b901f31508c1","title":"Group-term life imputed income: coverage exclusion · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":47.934,"exit_code":1,"observations":[{"actual":3190,"check":"regression 0","expected":2695,"passed":false},{"actual":2376,"check":"regression 1","expected":0,"passed":false},{"actual":9801,"check":"partial-repair probe 2","expected":0,"passed":false},{"actual":190156,"check":"partial-repair probe 3","expected":149368,"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\": 3190, \"expected\": 2695, \"passed\": false}, {\"check\": \"regression 1\", \"actual\": 2376, \"expected\": 0, \"passed\": false}, {\"check\": \"partial-repair probe 2\", \"actual\": 9801, \"expected\": 0, \"passed\": false}, {\"check\": \"partial-repair probe 3\", \"actual\": 190156, \"expected\": 149368, \"passed\": false}, {\"check\": \"normal control 4\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 6\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 7\", \"actual\": 0, \"expected\": 0, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":46.154,"exit_code":1,"observations":[{"actual":3195,"check":"regression 0","expected":2695,"passed":false},{"actual":2400,"check":"regression 1","expected":0,"passed":false},{"actual":9900,"check":"partial-repair probe 2","expected":0,"passed":false},{"actual":190568,"check":"partial-repair probe 3","expected":149368,"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\": 3195, \"expected\": 2695, \"passed\": false}, {\"check\": \"regression 1\", \"actual\": 2400, \"expected\": 0, \"passed\": false}, {\"check\": \"partial-repair probe 2\", \"actual\": 9900, \"expected\": 0, \"passed\": false}, {\"check\": \"partial-repair probe 3\", \"actual\": 190568, \"expected\": 149368, \"passed\": false}, {\"check\": \"normal control 4\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 6\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"normal control 7\", \"actual\": 0, \"expected\": 0, \"passed\": true}], \"passed\": false}\n"}},"member_only":{"stages":["fixed"],"fields":["implementations.fixed","verification.fixed","harness","repair"],"note":"The verified repair, its recorded checks, the repair description, and the scoring harness are available to members."}}