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FA-59041 / Payroll withholding rules / Open access

401(k) deferral limit with catch-up: deferral percentage base · case 01

Deferrals are smaller than the election because they are computed on net pay.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The elected percentage is applied to net available pay instead of gross compensation.

VERIFIED REPAIR

Restore the contract rule at the deferral percentage base step: use `want = (x['gross'] * x['pct'] + 50) // 100`.

Unsuccessful approach: The attempt uses gross pay but truncates, a cent short on fractional elections.

Case contract

Input {gross, pct, ytd, birth_year, year, net_available}. Requested deferral = gross*pct/100 half-up. Annual cap 23,000.00 plus 7,500.00 catch-up when the employee turns 50 or older during the year. Deferral = min(request, max(0, cap - ytd), net_available). Return [deferral, remaining room after it].

Why this case matters

Deferral limits apply to the calendar year of the age milestone and cannot exceed pay available after taxes.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    want = (x['net_available'] * x['pct'] + 50) // 100
    age = x['year'] - x['birth_year']
    cap = 2300000 + (750000 if age >= 50 else 0)
    room = max(0, cap - x['ytd'])
    d = min(want, room)
    d = min(d, x['net_available'])
    return [d, room - d]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'gross': 1618397, 'pct': 3, 'ytd': 2121789, 'birth_year': 1961, 'year': 2025, 'net_available': 1350724}, [48552, 879659]), ('regression', {'gross': 50757, 'pct': 3, 'ytd': 2246489, 'birth_year': 2005, 'year': 2025, 'net_available': 492828}, [1523, 51988]), ('partial-repair probe', {'gross': 1114570, 'pct': 15, 'ytd': 2028957, 'birth_year': 1995, 'year': 2025, 'net_available': 1264377}, [167186, 103857]), ('partial-repair probe', {'gross': 195058, 'pct': 3, 'ytd': 2297594, 'birth_year': 1965, 'year': 2025, 'net_available': 1227790}, [5852, 746554]), ('normal control', {'gross': 1457089, 'pct': 0, 'ytd': 2996561, 'birth_year': 1976, 'year': 2025, 'net_available': 161315}, [0, 0]), ('normal control', {'gross': 742708, 'pct': 50, 'ytd': 2300000, 'birth_year': 1987, 'year': 2025, 'net_available': 358846}, [0, 0]), ('normal control', {'gross': 1338450, 'pct': 0, 'ytd': 895056, 'birth_year': 1965, 'year': 2025, 'net_available': 341182}, [0, 2154944]), ('normal control', {'gross': 1413796, 'pct': 0, 'ytd': 2267407, 'birth_year': 1974, 'year': 2024, 'net_available': 1155850}, [0, 782593])], [('regression', {'gross': 50757, 'pct': 3, 'ytd': 2246489, 'birth_year': 2005, 'year': 2025, 'net_available': 492828}, [1523, 51988]), ('regression', {'gross': 1114570, 'pct': 15, 'ytd': 2028957, 'birth_year': 1995, 'year': 2025, 'net_available': 1264377}, [167186, 103857]), ('partial-repair probe', {'gross': 195058, 'pct': 3, 'ytd': 2297594, 'birth_year': 1965, 'year': 2025, 'net_available': 1227790}, [5852, 746554]), ('partial-repair probe', {'gross': 320598, 'pct': 3, 'ytd': 257717, 'birth_year': 1973, 'year': 2024, 'net_available': 164909}, [9618, 2782665]), ('normal control', {'gross': 243481, 'pct': 10, 'ytd': 2857536, 'birth_year': 1999, 'year': 2024, 'net_available': 109046}, [0, 0]), ('normal control', {'gross': 1755234, 'pct': 75, 'ytd': 2857725, 'birth_year': 1965, 'year': 2025, 'net_available': 1444455}, [192275, 0]), ('normal control', {'gross': 1255430, 'pct': 0, 'ytd': 322436, 'birth_year': 1964, 'year': 2024, 'net_available': 213657}, [0, 2727564]), ('normal control', {'gross': 466414, 'pct': 33, 'ytd': 2300000, 'birth_year': 1994, 'year': 2025, 'net_available': 1301026}, [0, 0])], [('regression', {'gross': 1114570, 'pct': 15, 'ytd': 2028957, 'birth_year': 1995, 'year': 2025, 'net_available': 1264377}, [167186, 103857]), ('regression', {'gross': 721708, 'pct': 75, 'ytd': 453300, 'birth_year': 1975, 'year': 2024, 'net_available': 60633}, [60633, 1786067]), ('partial-repair probe', {'gross': 320598, 'pct': 3, 'ytd': 257717, 'birth_year': 1973, 'year': 2024, 'net_available': 164909}, [9618, 2782665]), ('partial-repair probe', {'gross': 73922, 'pct': 90, 'ytd': 2171484, 'birth_year': 1974, 'year': 2025, 'net_available': 651545}, [66530, 811986]), ('normal control', {'gross': 153394, 'pct': 50, 'ytd': 2232880, 'birth_year': 1994, 'year': 2024, 'net_available': 220513}, [67120, 0]), ('normal control', {'gross': 1415766, 'pct': 0, 'ytd': 2300000, 'birth_year': 1973, 'year': 2024, 'net_available': 1433510}, [0, 750000]), ('normal control', {'gross': 1325390, 'pct': 15, 'ytd': 3004038, 'birth_year': 1973, 'year': 2024, 'net_available': 1230228}, [45962, 0]), ('normal control', {'gross': 1693660, 'pct': 15, 'ytd': 2977024, 'birth_year': 1988, 'year': 2025, 'net_available': 9919}, [0, 0])], [('regression', {'gross': 195058, 'pct': 3, 'ytd': 2297594, 'birth_year': 1965, 'year': 2025, 'net_available': 1227790}, [5852, 746554]), ('regression', {'gross': 721708, 'pct': 75, 'ytd': 453300, 'birth_year': 1975, 'year': 2024, 'net_available': 60633}, [60633, 1786067]), ('partial-repair probe', {'gross': 1771224, 'pct': 15, 'ytd': 2300000, 'birth_year': 1973, 'year': 2024, 'net_available': 756642}, [265684, 484316]), ('partial-repair probe', {'gross': 66663, 'pct': 50, 'ytd': 852939, 'birth_year': 1983, 'year': 2025, 'net_available': 643352}, [33332, 1413729]), ('normal control', {'gross': 1791193, 'pct': 75, 'ytd': 2946558, 'birth_year': 1964, 'year': 2024, 'net_available': 587430}, [103442, 0]), ('normal control', {'gross': 1886269, 'pct': 0, 'ytd': 3022556, 'birth_year': 1974, 'year': 2025, 'net_available': 531609}, [0, 27444]), ('normal control', {'gross': 901600, 'pct': 0, 'ytd': 2179894, 'birth_year': 1975, 'year': 2024, 'net_available': 1421448}, [0, 120106]), ('normal control', {'gross': 1505848, 'pct': 6, 'ytd': 2895487, 'birth_year': 1975, 'year': 2024, 'net_available': 685023}, [0, 0])], [('regression', {'gross': 320598, 'pct': 3, 'ytd': 257717, 'birth_year': 1973, 'year': 2024, 'net_available': 164909}, [9618, 2782665]), ('regression', {'gross': 195058, 'pct': 3, 'ytd': 2297594, 'birth_year': 1965, 'year': 2025, 'net_available': 1227790}, [5852, 746554]), ('partial-repair probe', {'gross': 1552698, 'pct': 3, 'ytd': 926873, 'birth_year': 1974, 'year': 2024, 'net_available': 1490831}, [46581, 2076546]), ('partial-repair probe', {'gross': 160130, 'pct': 6, 'ytd': 2300000, 'birth_year': 1974, 'year': 2025, 'net_available': 888833}, [9608, 740392]), ('normal control', {'gross': 1731857, 'pct': 75, 'ytd': 2300000, 'birth_year': 1975, 'year': 2025, 'net_available': 1370216}, [750000, 0]), ('normal control', {'gross': 159714, 'pct': 50, 'ytd': 2300000, 'birth_year': 1996, 'year': 2024, 'net_available': 855993}, [0, 0]), ('normal control', {'gross': 1366584, 'pct': 0, 'ytd': 2421747, 'birth_year': 1974, 'year': 2024, 'net_available': 331646}, [0, 628253]), ('normal control', {'gross': 1043587, 'pct': 10, 'ytd': 2279748, 'birth_year': 1994, 'year': 2024, 'net_available': 991524}, [20252, 0])]]
for i, (label, args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (label, i), solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
regression 0[40522, 887689][48552, 879659]Failed
regression 1[14785, 38726][1523, 51988]Failed
partial-repair probe 2[189657, 81386][167186, 103857]Failed
partial-repair probe 3[36834, 715572][5852, 746554]Failed
normal control 4[0, 0][0, 0]Passed
normal control 5[0, 0][0, 0]Passed
normal control 6[0, 2154944][0, 2154944]Passed
normal control 7[0, 782593][0, 782593]Passed

SHA-256 / 060ffdb0ab026af0a1c1d22f9a03d52ab837bd5d720dd803833768b12c302724

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    want = x['gross'] * x['pct'] // 100
    age = x['year'] - x['birth_year']
    cap = 2300000 + (750000 if age >= 50 else 0)
    room = max(0, cap - x['ytd'])
    d = min(want, room)
    d = min(d, x['net_available'])
    return [d, room - d]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'gross': 1618397, 'pct': 3, 'ytd': 2121789, 'birth_year': 1961, 'year': 2025, 'net_available': 1350724}, [48552, 879659]), ('regression', {'gross': 50757, 'pct': 3, 'ytd': 2246489, 'birth_year': 2005, 'year': 2025, 'net_available': 492828}, [1523, 51988]), ('partial-repair probe', {'gross': 1114570, 'pct': 15, 'ytd': 2028957, 'birth_year': 1995, 'year': 2025, 'net_available': 1264377}, [167186, 103857]), ('partial-repair probe', {'gross': 195058, 'pct': 3, 'ytd': 2297594, 'birth_year': 1965, 'year': 2025, 'net_available': 1227790}, [5852, 746554]), ('normal control', {'gross': 1457089, 'pct': 0, 'ytd': 2996561, 'birth_year': 1976, 'year': 2025, 'net_available': 161315}, [0, 0]), ('normal control', {'gross': 742708, 'pct': 50, 'ytd': 2300000, 'birth_year': 1987, 'year': 2025, 'net_available': 358846}, [0, 0]), ('normal control', {'gross': 1338450, 'pct': 0, 'ytd': 895056, 'birth_year': 1965, 'year': 2025, 'net_available': 341182}, [0, 2154944]), ('normal control', {'gross': 1413796, 'pct': 0, 'ytd': 2267407, 'birth_year': 1974, 'year': 2024, 'net_available': 1155850}, [0, 782593])], [('regression', {'gross': 50757, 'pct': 3, 'ytd': 2246489, 'birth_year': 2005, 'year': 2025, 'net_available': 492828}, [1523, 51988]), ('regression', {'gross': 1114570, 'pct': 15, 'ytd': 2028957, 'birth_year': 1995, 'year': 2025, 'net_available': 1264377}, [167186, 103857]), ('partial-repair probe', {'gross': 195058, 'pct': 3, 'ytd': 2297594, 'birth_year': 1965, 'year': 2025, 'net_available': 1227790}, [5852, 746554]), ('partial-repair probe', {'gross': 320598, 'pct': 3, 'ytd': 257717, 'birth_year': 1973, 'year': 2024, 'net_available': 164909}, [9618, 2782665]), ('normal control', {'gross': 243481, 'pct': 10, 'ytd': 2857536, 'birth_year': 1999, 'year': 2024, 'net_available': 109046}, [0, 0]), ('normal control', {'gross': 1755234, 'pct': 75, 'ytd': 2857725, 'birth_year': 1965, 'year': 2025, 'net_available': 1444455}, [192275, 0]), ('normal control', {'gross': 1255430, 'pct': 0, 'ytd': 322436, 'birth_year': 1964, 'year': 2024, 'net_available': 213657}, [0, 2727564]), ('normal control', {'gross': 466414, 'pct': 33, 'ytd': 2300000, 'birth_year': 1994, 'year': 2025, 'net_available': 1301026}, [0, 0])], [('regression', {'gross': 1114570, 'pct': 15, 'ytd': 2028957, 'birth_year': 1995, 'year': 2025, 'net_available': 1264377}, [167186, 103857]), ('regression', {'gross': 721708, 'pct': 75, 'ytd': 453300, 'birth_year': 1975, 'year': 2024, 'net_available': 60633}, [60633, 1786067]), ('partial-repair probe', {'gross': 320598, 'pct': 3, 'ytd': 257717, 'birth_year': 1973, 'year': 2024, 'net_available': 164909}, [9618, 2782665]), ('partial-repair probe', {'gross': 73922, 'pct': 90, 'ytd': 2171484, 'birth_year': 1974, 'year': 2025, 'net_available': 651545}, [66530, 811986]), ('normal control', {'gross': 153394, 'pct': 50, 'ytd': 2232880, 'birth_year': 1994, 'year': 2024, 'net_available': 220513}, [67120, 0]), ('normal control', {'gross': 1415766, 'pct': 0, 'ytd': 2300000, 'birth_year': 1973, 'year': 2024, 'net_available': 1433510}, [0, 750000]), ('normal control', {'gross': 1325390, 'pct': 15, 'ytd': 3004038, 'birth_year': 1973, 'year': 2024, 'net_available': 1230228}, [45962, 0]), ('normal control', {'gross': 1693660, 'pct': 15, 'ytd': 2977024, 'birth_year': 1988, 'year': 2025, 'net_available': 9919}, [0, 0])], [('regression', {'gross': 195058, 'pct': 3, 'ytd': 2297594, 'birth_year': 1965, 'year': 2025, 'net_available': 1227790}, [5852, 746554]), ('regression', {'gross': 721708, 'pct': 75, 'ytd': 453300, 'birth_year': 1975, 'year': 2024, 'net_available': 60633}, [60633, 1786067]), ('partial-repair probe', {'gross': 1771224, 'pct': 15, 'ytd': 2300000, 'birth_year': 1973, 'year': 2024, 'net_available': 756642}, [265684, 484316]), ('partial-repair probe', {'gross': 66663, 'pct': 50, 'ytd': 852939, 'birth_year': 1983, 'year': 2025, 'net_available': 643352}, [33332, 1413729]), ('normal control', {'gross': 1791193, 'pct': 75, 'ytd': 2946558, 'birth_year': 1964, 'year': 2024, 'net_available': 587430}, [103442, 0]), ('normal control', {'gross': 1886269, 'pct': 0, 'ytd': 3022556, 'birth_year': 1974, 'year': 2025, 'net_available': 531609}, [0, 27444]), ('normal control', {'gross': 901600, 'pct': 0, 'ytd': 2179894, 'birth_year': 1975, 'year': 2024, 'net_available': 1421448}, [0, 120106]), ('normal control', {'gross': 1505848, 'pct': 6, 'ytd': 2895487, 'birth_year': 1975, 'year': 2024, 'net_available': 685023}, [0, 0])], [('regression', {'gross': 320598, 'pct': 3, 'ytd': 257717, 'birth_year': 1973, 'year': 2024, 'net_available': 164909}, [9618, 2782665]), ('regression', {'gross': 195058, 'pct': 3, 'ytd': 2297594, 'birth_year': 1965, 'year': 2025, 'net_available': 1227790}, [5852, 746554]), ('partial-repair probe', {'gross': 1552698, 'pct': 3, 'ytd': 926873, 'birth_year': 1974, 'year': 2024, 'net_available': 1490831}, [46581, 2076546]), ('partial-repair probe', {'gross': 160130, 'pct': 6, 'ytd': 2300000, 'birth_year': 1974, 'year': 2025, 'net_available': 888833}, [9608, 740392]), ('normal control', {'gross': 1731857, 'pct': 75, 'ytd': 2300000, 'birth_year': 1975, 'year': 2025, 'net_available': 1370216}, [750000, 0]), ('normal control', {'gross': 159714, 'pct': 50, 'ytd': 2300000, 'birth_year': 1996, 'year': 2024, 'net_available': 855993}, [0, 0]), ('normal control', {'gross': 1366584, 'pct': 0, 'ytd': 2421747, 'birth_year': 1974, 'year': 2024, 'net_available': 331646}, [0, 628253]), ('normal control', {'gross': 1043587, 'pct': 10, 'ytd': 2279748, 'birth_year': 1994, 'year': 2024, 'net_available': 991524}, [20252, 0])]]
for i, (label, args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (label, i), solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
regression 0[48551, 879660][48552, 879659]Failed
regression 1[1522, 51989][1523, 51988]Failed
partial-repair probe 2[167185, 103858][167186, 103857]Failed
partial-repair probe 3[5851, 746555][5852, 746554]Failed
normal control 4[0, 0][0, 0]Passed
normal control 5[0, 0][0, 0]Passed
normal control 6[0, 2154944][0, 2154944]Passed
normal control 7[0, 782593][0, 782593]Passed

SHA-256 / b2c1b4ee6876afb6c3b307393ce350b7014e77a83f7173395bb8d048d97319e5

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    want = (x['gross'] * x['pct'] + 50) // 100
    age = x['year'] - x['birth_year']
    cap = 2300000 + (750000 if age >= 50 else 0)
    room = max(0, cap - x['ytd'])
    d = min(want, room)
    d = min(d, x['net_available'])
    return [d, room - d]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'gross': 1618397, 'pct': 3, 'ytd': 2121789, 'birth_year': 1961, 'year': 2025, 'net_available': 1350724}, [48552, 879659]), ('regression', {'gross': 50757, 'pct': 3, 'ytd': 2246489, 'birth_year': 2005, 'year': 2025, 'net_available': 492828}, [1523, 51988]), ('partial-repair probe', {'gross': 1114570, 'pct': 15, 'ytd': 2028957, 'birth_year': 1995, 'year': 2025, 'net_available': 1264377}, [167186, 103857]), ('partial-repair probe', {'gross': 195058, 'pct': 3, 'ytd': 2297594, 'birth_year': 1965, 'year': 2025, 'net_available': 1227790}, [5852, 746554]), ('normal control', {'gross': 1457089, 'pct': 0, 'ytd': 2996561, 'birth_year': 1976, 'year': 2025, 'net_available': 161315}, [0, 0]), ('normal control', {'gross': 742708, 'pct': 50, 'ytd': 2300000, 'birth_year': 1987, 'year': 2025, 'net_available': 358846}, [0, 0]), ('normal control', {'gross': 1338450, 'pct': 0, 'ytd': 895056, 'birth_year': 1965, 'year': 2025, 'net_available': 341182}, [0, 2154944]), ('normal control', {'gross': 1413796, 'pct': 0, 'ytd': 2267407, 'birth_year': 1974, 'year': 2024, 'net_available': 1155850}, [0, 782593])], [('regression', {'gross': 50757, 'pct': 3, 'ytd': 2246489, 'birth_year': 2005, 'year': 2025, 'net_available': 492828}, [1523, 51988]), ('regression', {'gross': 1114570, 'pct': 15, 'ytd': 2028957, 'birth_year': 1995, 'year': 2025, 'net_available': 1264377}, [167186, 103857]), ('partial-repair probe', {'gross': 195058, 'pct': 3, 'ytd': 2297594, 'birth_year': 1965, 'year': 2025, 'net_available': 1227790}, [5852, 746554]), ('partial-repair probe', {'gross': 320598, 'pct': 3, 'ytd': 257717, 'birth_year': 1973, 'year': 2024, 'net_available': 164909}, [9618, 2782665]), ('normal control', {'gross': 243481, 'pct': 10, 'ytd': 2857536, 'birth_year': 1999, 'year': 2024, 'net_available': 109046}, [0, 0]), ('normal control', {'gross': 1755234, 'pct': 75, 'ytd': 2857725, 'birth_year': 1965, 'year': 2025, 'net_available': 1444455}, [192275, 0]), ('normal control', {'gross': 1255430, 'pct': 0, 'ytd': 322436, 'birth_year': 1964, 'year': 2024, 'net_available': 213657}, [0, 2727564]), ('normal control', {'gross': 466414, 'pct': 33, 'ytd': 2300000, 'birth_year': 1994, 'year': 2025, 'net_available': 1301026}, [0, 0])], [('regression', {'gross': 1114570, 'pct': 15, 'ytd': 2028957, 'birth_year': 1995, 'year': 2025, 'net_available': 1264377}, [167186, 103857]), ('regression', {'gross': 721708, 'pct': 75, 'ytd': 453300, 'birth_year': 1975, 'year': 2024, 'net_available': 60633}, [60633, 1786067]), ('partial-repair probe', {'gross': 320598, 'pct': 3, 'ytd': 257717, 'birth_year': 1973, 'year': 2024, 'net_available': 164909}, [9618, 2782665]), ('partial-repair probe', {'gross': 73922, 'pct': 90, 'ytd': 2171484, 'birth_year': 1974, 'year': 2025, 'net_available': 651545}, [66530, 811986]), ('normal control', {'gross': 153394, 'pct': 50, 'ytd': 2232880, 'birth_year': 1994, 'year': 2024, 'net_available': 220513}, [67120, 0]), ('normal control', {'gross': 1415766, 'pct': 0, 'ytd': 2300000, 'birth_year': 1973, 'year': 2024, 'net_available': 1433510}, [0, 750000]), ('normal control', {'gross': 1325390, 'pct': 15, 'ytd': 3004038, 'birth_year': 1973, 'year': 2024, 'net_available': 1230228}, [45962, 0]), ('normal control', {'gross': 1693660, 'pct': 15, 'ytd': 2977024, 'birth_year': 1988, 'year': 2025, 'net_available': 9919}, [0, 0])], [('regression', {'gross': 195058, 'pct': 3, 'ytd': 2297594, 'birth_year': 1965, 'year': 2025, 'net_available': 1227790}, [5852, 746554]), ('regression', {'gross': 721708, 'pct': 75, 'ytd': 453300, 'birth_year': 1975, 'year': 2024, 'net_available': 60633}, [60633, 1786067]), ('partial-repair probe', {'gross': 1771224, 'pct': 15, 'ytd': 2300000, 'birth_year': 1973, 'year': 2024, 'net_available': 756642}, [265684, 484316]), ('partial-repair probe', {'gross': 66663, 'pct': 50, 'ytd': 852939, 'birth_year': 1983, 'year': 2025, 'net_available': 643352}, [33332, 1413729]), ('normal control', {'gross': 1791193, 'pct': 75, 'ytd': 2946558, 'birth_year': 1964, 'year': 2024, 'net_available': 587430}, [103442, 0]), ('normal control', {'gross': 1886269, 'pct': 0, 'ytd': 3022556, 'birth_year': 1974, 'year': 2025, 'net_available': 531609}, [0, 27444]), ('normal control', {'gross': 901600, 'pct': 0, 'ytd': 2179894, 'birth_year': 1975, 'year': 2024, 'net_available': 1421448}, [0, 120106]), ('normal control', {'gross': 1505848, 'pct': 6, 'ytd': 2895487, 'birth_year': 1975, 'year': 2024, 'net_available': 685023}, [0, 0])], [('regression', {'gross': 320598, 'pct': 3, 'ytd': 257717, 'birth_year': 1973, 'year': 2024, 'net_available': 164909}, [9618, 2782665]), ('regression', {'gross': 195058, 'pct': 3, 'ytd': 2297594, 'birth_year': 1965, 'year': 2025, 'net_available': 1227790}, [5852, 746554]), ('partial-repair probe', {'gross': 1552698, 'pct': 3, 'ytd': 926873, 'birth_year': 1974, 'year': 2024, 'net_available': 1490831}, [46581, 2076546]), ('partial-repair probe', {'gross': 160130, 'pct': 6, 'ytd': 2300000, 'birth_year': 1974, 'year': 2025, 'net_available': 888833}, [9608, 740392]), ('normal control', {'gross': 1731857, 'pct': 75, 'ytd': 2300000, 'birth_year': 1975, 'year': 2025, 'net_available': 1370216}, [750000, 0]), ('normal control', {'gross': 159714, 'pct': 50, 'ytd': 2300000, 'birth_year': 1996, 'year': 2024, 'net_available': 855993}, [0, 0]), ('normal control', {'gross': 1366584, 'pct': 0, 'ytd': 2421747, 'birth_year': 1974, 'year': 2024, 'net_available': 331646}, [0, 628253]), ('normal control', {'gross': 1043587, 'pct': 10, 'ytd': 2279748, 'birth_year': 1994, 'year': 2024, 'net_available': 991524}, [20252, 0])]]
for i, (label, args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (label, i), solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
regression 0[48552, 879659][48552, 879659]Passed
regression 1[1523, 51988][1523, 51988]Passed
partial-repair probe 2[167186, 103857][167186, 103857]Passed
partial-repair probe 3[5852, 746554][5852, 746554]Passed
normal control 4[0, 0][0, 0]Passed
normal control 5[0, 0][0, 0]Passed
normal control 6[0, 2154944][0, 2154944]Passed
normal control 7[0, 782593][0, 782593]Passed

SHA-256 / 53e885be1a77477a64c403a648f956a8583aacb99d8eda485d999de8f533aa9a

Verification & scope

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.

Observations recorded using Python 3.12.14 at 2026-09-29T14:46:32.435597+00:00.

Case digest / 92ec2bb40cb56c5e1aa3eea4b47b8ed4ddd7b45e5185e680a88467fb4fa5af0e