FA-59316 / Payroll withholding rules / Open access
Nonresident alien wage add-on: student exemption window · case 01
Students in their fifth year lose their FICA exemption early.
ROOT CAUSE
The exemption window excludes the fifth year.
VERIFIED REPAIR
Restore the contract rule at the student exemption window step: use `x['years_present'] <= 5`.
Unsuccessful approach: The attempt keeps five years but drops J-visa scholars from the exemption.
Case contract
Input {wage, freq, nra, w4_year, visa, years_present}. Nonresident aliens add a per-period amount to wages for income-tax withholding only (2020+ forms: weekly 209.60, biweekly 419.20, semimonthly 454.20, monthly 908.30; older forms: 151.00, 302.00, 327.00, 654.00). FICA wages are the actual wages, or 0 for F and J visa holders present 5 years or fewer. Return [fit_wages, fica_wages].
Why this case matters
The nonresident add-on inflates only the income-tax base; student and scholar FICA exemptions are time-limited.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
new = {'weekly': 20960, 'biweekly': 41920, 'semimonthly': 45420, 'monthly': 90830}
old = {'weekly': 15100, 'biweekly': 30200, 'semimonthly': 32700, 'monthly': 65400}
add = new[x['freq']] if x['w4_year'] >= 2020 else old[x['freq']]
fit = x['wage'] + (add if x['nra'] else 0)
exempt = x['visa'] in ('F', 'J') and x['years_present'] < 5
fica = 0 if exempt else x['wage']
return [fit, fica]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'wage': 179956, 'freq': 'monthly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 5}, [270786, 0]), ('regression', {'wage': 40274, 'freq': 'monthly', 'nra': True, 'w4_year': 2018, 'visa': 'F', 'years_present': 5}, [105674, 0]), ('partial-repair probe', {'wage': 314655, 'freq': 'monthly', 'nra': False, 'w4_year': 2024, 'visa': 'J', 'years_present': 1}, [314655, 0]), ('partial-repair probe', {'wage': 437685, 'freq': 'weekly', 'nra': True, 'w4_year': 2019, 'visa': 'J', 'years_present': 4}, [452785, 0]), ('normal control', {'wage': 392169, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2019, 'visa': 'F', 'years_present': 1}, [424869, 0]), ('normal control', {'wage': 245306, 'freq': 'weekly', 'nra': False, 'w4_year': 2020, 'visa': 'F', 'years_present': 6}, [245306, 245306]), ('normal control', {'wage': 252469, 'freq': 'weekly', 'nra': True, 'w4_year': 2021, 'visa': None, 'years_present': 8}, [273429, 252469]), ('normal control', {'wage': 286154, 'freq': 'monthly', 'nra': True, 'w4_year': 2024, 'visa': 'H', 'years_present': 1}, [376984, 286154])], [('regression', {'wage': 217618, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [217618, 0]), ('regression', {'wage': 179956, 'freq': 'monthly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 5}, [270786, 0]), ('partial-repair probe', {'wage': 437685, 'freq': 'weekly', 'nra': True, 'w4_year': 2019, 'visa': 'J', 'years_present': 4}, [452785, 0]), ('partial-repair probe', {'wage': 474591, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 4}, [520011, 0]), ('normal control', {'wage': 2124, 'freq': 'monthly', 'nra': True, 'w4_year': 2024, 'visa': 'H', 'years_present': 6}, [92954, 2124]), ('normal control', {'wage': 350530, 'freq': 'weekly', 'nra': False, 'w4_year': 2018, 'visa': None, 'years_present': 1}, [350530, 350530]), ('normal control', {'wage': 210010, 'freq': 'biweekly', 'nra': True, 'w4_year': 2019, 'visa': None, 'years_present': 1}, [240210, 210010]), ('normal control', {'wage': 436984, 'freq': 'biweekly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 8}, [478904, 436984])], [('regression', {'wage': 310614, 'freq': 'weekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [310614, 0]), ('regression', {'wage': 217618, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [217618, 0]), ('partial-repair probe', {'wage': 54168, 'freq': 'weekly', 'nra': True, 'w4_year': 2018, 'visa': 'J', 'years_present': 4}, [69268, 0]), ('partial-repair probe', {'wage': 419635, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2018, 'visa': 'J', 'years_present': 4}, [452335, 0]), ('normal control', {'wage': 279817, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': None, 'years_present': 8}, [279817, 279817]), ('normal control', {'wage': 352005, 'freq': 'semimonthly', 'nra': False, 'w4_year': 2018, 'visa': 'H', 'years_present': 5}, [352005, 352005]), ('normal control', {'wage': 328051, 'freq': 'weekly', 'nra': False, 'w4_year': 2024, 'visa': None, 'years_present': 5}, [328051, 328051]), ('normal control', {'wage': 162523, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2020, 'visa': 'H', 'years_present': 5}, [207943, 162523])], [('regression', {'wage': 464578, 'freq': 'monthly', 'nra': False, 'w4_year': 2018, 'visa': 'J', 'years_present': 5}, [464578, 0]), ('regression', {'wage': 310614, 'freq': 'weekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [310614, 0]), ('partial-repair probe', {'wage': 76351, 'freq': 'semimonthly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 1}, [76351, 0]), ('partial-repair probe', {'wage': 367989, 'freq': 'biweekly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 4}, [409909, 0]), ('normal control', {'wage': 181833, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2020, 'visa': None, 'years_present': 1}, [227253, 181833]), ('normal control', {'wage': 141591, 'freq': 'biweekly', 'nra': True, 'w4_year': 2018, 'visa': 'H', 'years_present': 4}, [171791, 141591]), ('normal control', {'wage': 416568, 'freq': 'weekly', 'nra': True, 'w4_year': 2021, 'visa': 'H', 'years_present': 8}, [437528, 416568]), ('normal control', {'wage': 434085, 'freq': 'weekly', 'nra': False, 'w4_year': 2020, 'visa': None, 'years_present': 6}, [434085, 434085])], [('regression', {'wage': 64348, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [64348, 0]), ('regression', {'wage': 148120, 'freq': 'monthly', 'nra': False, 'w4_year': 2019, 'visa': 'F', 'years_present': 5}, [148120, 0]), ('partial-repair probe', {'wage': 217618, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [217618, 0]), ('partial-repair probe', {'wage': 310614, 'freq': 'weekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [310614, 0]), ('normal control', {'wage': 114598, 'freq': 'weekly', 'nra': True, 'w4_year': 2020, 'visa': 'H', 'years_present': 5}, [135558, 114598]), ('normal control', {'wage': 178971, 'freq': 'biweekly', 'nra': True, 'w4_year': 2018, 'visa': 'F', 'years_present': 1}, [209171, 0]), ('normal control', {'wage': 21555, 'freq': 'biweekly', 'nra': True, 'w4_year': 2019, 'visa': 'J', 'years_present': 6}, [51755, 21555]), ('normal control', {'wage': 498322, 'freq': 'biweekly', 'nra': True, 'w4_year': 2020, 'visa': None, 'years_present': 5}, [540242, 498322])]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression 0 | [270786, 179956] | [270786, 0] | Failed |
| regression 1 | [105674, 40274] | [105674, 0] | Failed |
| partial-repair probe 2 | [314655, 0] | [314655, 0] | Passed |
| partial-repair probe 3 | [452785, 0] | [452785, 0] | Passed |
| normal control 4 | [424869, 0] | [424869, 0] | Passed |
| normal control 5 | [245306, 245306] | [245306, 245306] | Passed |
| normal control 6 | [273429, 252469] | [273429, 252469] | Passed |
| normal control 7 | [376984, 286154] | [376984, 286154] | Passed |
SHA-256 / 49925d0202a9b2998709306b295ccbb21465dfdd849d7b6440f5b0e1e3435669
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
new = {'weekly': 20960, 'biweekly': 41920, 'semimonthly': 45420, 'monthly': 90830}
old = {'weekly': 15100, 'biweekly': 30200, 'semimonthly': 32700, 'monthly': 65400}
add = new[x['freq']] if x['w4_year'] >= 2020 else old[x['freq']]
fit = x['wage'] + (add if x['nra'] else 0)
exempt = x['visa'] in ('F', 'J') and x['years_present'] <= 5 and x['visa'] == 'F'
fica = 0 if exempt else x['wage']
return [fit, fica]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'wage': 179956, 'freq': 'monthly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 5}, [270786, 0]), ('regression', {'wage': 40274, 'freq': 'monthly', 'nra': True, 'w4_year': 2018, 'visa': 'F', 'years_present': 5}, [105674, 0]), ('partial-repair probe', {'wage': 314655, 'freq': 'monthly', 'nra': False, 'w4_year': 2024, 'visa': 'J', 'years_present': 1}, [314655, 0]), ('partial-repair probe', {'wage': 437685, 'freq': 'weekly', 'nra': True, 'w4_year': 2019, 'visa': 'J', 'years_present': 4}, [452785, 0]), ('normal control', {'wage': 392169, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2019, 'visa': 'F', 'years_present': 1}, [424869, 0]), ('normal control', {'wage': 245306, 'freq': 'weekly', 'nra': False, 'w4_year': 2020, 'visa': 'F', 'years_present': 6}, [245306, 245306]), ('normal control', {'wage': 252469, 'freq': 'weekly', 'nra': True, 'w4_year': 2021, 'visa': None, 'years_present': 8}, [273429, 252469]), ('normal control', {'wage': 286154, 'freq': 'monthly', 'nra': True, 'w4_year': 2024, 'visa': 'H', 'years_present': 1}, [376984, 286154])], [('regression', {'wage': 217618, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [217618, 0]), ('regression', {'wage': 179956, 'freq': 'monthly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 5}, [270786, 0]), ('partial-repair probe', {'wage': 437685, 'freq': 'weekly', 'nra': True, 'w4_year': 2019, 'visa': 'J', 'years_present': 4}, [452785, 0]), ('partial-repair probe', {'wage': 474591, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 4}, [520011, 0]), ('normal control', {'wage': 2124, 'freq': 'monthly', 'nra': True, 'w4_year': 2024, 'visa': 'H', 'years_present': 6}, [92954, 2124]), ('normal control', {'wage': 350530, 'freq': 'weekly', 'nra': False, 'w4_year': 2018, 'visa': None, 'years_present': 1}, [350530, 350530]), ('normal control', {'wage': 210010, 'freq': 'biweekly', 'nra': True, 'w4_year': 2019, 'visa': None, 'years_present': 1}, [240210, 210010]), ('normal control', {'wage': 436984, 'freq': 'biweekly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 8}, [478904, 436984])], [('regression', {'wage': 310614, 'freq': 'weekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [310614, 0]), ('regression', {'wage': 217618, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [217618, 0]), ('partial-repair probe', {'wage': 54168, 'freq': 'weekly', 'nra': True, 'w4_year': 2018, 'visa': 'J', 'years_present': 4}, [69268, 0]), ('partial-repair probe', {'wage': 419635, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2018, 'visa': 'J', 'years_present': 4}, [452335, 0]), ('normal control', {'wage': 279817, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': None, 'years_present': 8}, [279817, 279817]), ('normal control', {'wage': 352005, 'freq': 'semimonthly', 'nra': False, 'w4_year': 2018, 'visa': 'H', 'years_present': 5}, [352005, 352005]), ('normal control', {'wage': 328051, 'freq': 'weekly', 'nra': False, 'w4_year': 2024, 'visa': None, 'years_present': 5}, [328051, 328051]), ('normal control', {'wage': 162523, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2020, 'visa': 'H', 'years_present': 5}, [207943, 162523])], [('regression', {'wage': 464578, 'freq': 'monthly', 'nra': False, 'w4_year': 2018, 'visa': 'J', 'years_present': 5}, [464578, 0]), ('regression', {'wage': 310614, 'freq': 'weekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [310614, 0]), ('partial-repair probe', {'wage': 76351, 'freq': 'semimonthly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 1}, [76351, 0]), ('partial-repair probe', {'wage': 367989, 'freq': 'biweekly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 4}, [409909, 0]), ('normal control', {'wage': 181833, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2020, 'visa': None, 'years_present': 1}, [227253, 181833]), ('normal control', {'wage': 141591, 'freq': 'biweekly', 'nra': True, 'w4_year': 2018, 'visa': 'H', 'years_present': 4}, [171791, 141591]), ('normal control', {'wage': 416568, 'freq': 'weekly', 'nra': True, 'w4_year': 2021, 'visa': 'H', 'years_present': 8}, [437528, 416568]), ('normal control', {'wage': 434085, 'freq': 'weekly', 'nra': False, 'w4_year': 2020, 'visa': None, 'years_present': 6}, [434085, 434085])], [('regression', {'wage': 64348, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [64348, 0]), ('regression', {'wage': 148120, 'freq': 'monthly', 'nra': False, 'w4_year': 2019, 'visa': 'F', 'years_present': 5}, [148120, 0]), ('partial-repair probe', {'wage': 217618, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [217618, 0]), ('partial-repair probe', {'wage': 310614, 'freq': 'weekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [310614, 0]), ('normal control', {'wage': 114598, 'freq': 'weekly', 'nra': True, 'w4_year': 2020, 'visa': 'H', 'years_present': 5}, [135558, 114598]), ('normal control', {'wage': 178971, 'freq': 'biweekly', 'nra': True, 'w4_year': 2018, 'visa': 'F', 'years_present': 1}, [209171, 0]), ('normal control', {'wage': 21555, 'freq': 'biweekly', 'nra': True, 'w4_year': 2019, 'visa': 'J', 'years_present': 6}, [51755, 21555]), ('normal control', {'wage': 498322, 'freq': 'biweekly', 'nra': True, 'w4_year': 2020, 'visa': None, 'years_present': 5}, [540242, 498322])]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression 0 | [270786, 179956] | [270786, 0] | Failed |
| regression 1 | [105674, 0] | [105674, 0] | Passed |
| partial-repair probe 2 | [314655, 314655] | [314655, 0] | Failed |
| partial-repair probe 3 | [452785, 437685] | [452785, 0] | Failed |
| normal control 4 | [424869, 0] | [424869, 0] | Passed |
| normal control 5 | [245306, 245306] | [245306, 245306] | Passed |
| normal control 6 | [273429, 252469] | [273429, 252469] | Passed |
| normal control 7 | [376984, 286154] | [376984, 286154] | Passed |
SHA-256 / 3c444451a878100a2c5ddde3f3ae386c21a926fef7efe6de7c714f2e0f628ee1
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
new = {'weekly': 20960, 'biweekly': 41920, 'semimonthly': 45420, 'monthly': 90830}
old = {'weekly': 15100, 'biweekly': 30200, 'semimonthly': 32700, 'monthly': 65400}
add = new[x['freq']] if x['w4_year'] >= 2020 else old[x['freq']]
fit = x['wage'] + (add if x['nra'] else 0)
exempt = x['visa'] in ('F', 'J') and x['years_present'] <= 5
fica = 0 if exempt else x['wage']
return [fit, fica]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'wage': 179956, 'freq': 'monthly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 5}, [270786, 0]), ('regression', {'wage': 40274, 'freq': 'monthly', 'nra': True, 'w4_year': 2018, 'visa': 'F', 'years_present': 5}, [105674, 0]), ('partial-repair probe', {'wage': 314655, 'freq': 'monthly', 'nra': False, 'w4_year': 2024, 'visa': 'J', 'years_present': 1}, [314655, 0]), ('partial-repair probe', {'wage': 437685, 'freq': 'weekly', 'nra': True, 'w4_year': 2019, 'visa': 'J', 'years_present': 4}, [452785, 0]), ('normal control', {'wage': 392169, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2019, 'visa': 'F', 'years_present': 1}, [424869, 0]), ('normal control', {'wage': 245306, 'freq': 'weekly', 'nra': False, 'w4_year': 2020, 'visa': 'F', 'years_present': 6}, [245306, 245306]), ('normal control', {'wage': 252469, 'freq': 'weekly', 'nra': True, 'w4_year': 2021, 'visa': None, 'years_present': 8}, [273429, 252469]), ('normal control', {'wage': 286154, 'freq': 'monthly', 'nra': True, 'w4_year': 2024, 'visa': 'H', 'years_present': 1}, [376984, 286154])], [('regression', {'wage': 217618, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [217618, 0]), ('regression', {'wage': 179956, 'freq': 'monthly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 5}, [270786, 0]), ('partial-repair probe', {'wage': 437685, 'freq': 'weekly', 'nra': True, 'w4_year': 2019, 'visa': 'J', 'years_present': 4}, [452785, 0]), ('partial-repair probe', {'wage': 474591, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 4}, [520011, 0]), ('normal control', {'wage': 2124, 'freq': 'monthly', 'nra': True, 'w4_year': 2024, 'visa': 'H', 'years_present': 6}, [92954, 2124]), ('normal control', {'wage': 350530, 'freq': 'weekly', 'nra': False, 'w4_year': 2018, 'visa': None, 'years_present': 1}, [350530, 350530]), ('normal control', {'wage': 210010, 'freq': 'biweekly', 'nra': True, 'w4_year': 2019, 'visa': None, 'years_present': 1}, [240210, 210010]), ('normal control', {'wage': 436984, 'freq': 'biweekly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 8}, [478904, 436984])], [('regression', {'wage': 310614, 'freq': 'weekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [310614, 0]), ('regression', {'wage': 217618, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [217618, 0]), ('partial-repair probe', {'wage': 54168, 'freq': 'weekly', 'nra': True, 'w4_year': 2018, 'visa': 'J', 'years_present': 4}, [69268, 0]), ('partial-repair probe', {'wage': 419635, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2018, 'visa': 'J', 'years_present': 4}, [452335, 0]), ('normal control', {'wage': 279817, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': None, 'years_present': 8}, [279817, 279817]), ('normal control', {'wage': 352005, 'freq': 'semimonthly', 'nra': False, 'w4_year': 2018, 'visa': 'H', 'years_present': 5}, [352005, 352005]), ('normal control', {'wage': 328051, 'freq': 'weekly', 'nra': False, 'w4_year': 2024, 'visa': None, 'years_present': 5}, [328051, 328051]), ('normal control', {'wage': 162523, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2020, 'visa': 'H', 'years_present': 5}, [207943, 162523])], [('regression', {'wage': 464578, 'freq': 'monthly', 'nra': False, 'w4_year': 2018, 'visa': 'J', 'years_present': 5}, [464578, 0]), ('regression', {'wage': 310614, 'freq': 'weekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [310614, 0]), ('partial-repair probe', {'wage': 76351, 'freq': 'semimonthly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 1}, [76351, 0]), ('partial-repair probe', {'wage': 367989, 'freq': 'biweekly', 'nra': True, 'w4_year': 2021, 'visa': 'J', 'years_present': 4}, [409909, 0]), ('normal control', {'wage': 181833, 'freq': 'semimonthly', 'nra': True, 'w4_year': 2020, 'visa': None, 'years_present': 1}, [227253, 181833]), ('normal control', {'wage': 141591, 'freq': 'biweekly', 'nra': True, 'w4_year': 2018, 'visa': 'H', 'years_present': 4}, [171791, 141591]), ('normal control', {'wage': 416568, 'freq': 'weekly', 'nra': True, 'w4_year': 2021, 'visa': 'H', 'years_present': 8}, [437528, 416568]), ('normal control', {'wage': 434085, 'freq': 'weekly', 'nra': False, 'w4_year': 2020, 'visa': None, 'years_present': 6}, [434085, 434085])], [('regression', {'wage': 64348, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [64348, 0]), ('regression', {'wage': 148120, 'freq': 'monthly', 'nra': False, 'w4_year': 2019, 'visa': 'F', 'years_present': 5}, [148120, 0]), ('partial-repair probe', {'wage': 217618, 'freq': 'biweekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [217618, 0]), ('partial-repair probe', {'wage': 310614, 'freq': 'weekly', 'nra': False, 'w4_year': 2019, 'visa': 'J', 'years_present': 5}, [310614, 0]), ('normal control', {'wage': 114598, 'freq': 'weekly', 'nra': True, 'w4_year': 2020, 'visa': 'H', 'years_present': 5}, [135558, 114598]), ('normal control', {'wage': 178971, 'freq': 'biweekly', 'nra': True, 'w4_year': 2018, 'visa': 'F', 'years_present': 1}, [209171, 0]), ('normal control', {'wage': 21555, 'freq': 'biweekly', 'nra': True, 'w4_year': 2019, 'visa': 'J', 'years_present': 6}, [51755, 21555]), ('normal control', {'wage': 498322, 'freq': 'biweekly', 'nra': True, 'w4_year': 2020, 'visa': None, 'years_present': 5}, [540242, 498322])]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression 0 | [270786, 0] | [270786, 0] | Passed |
| regression 1 | [105674, 0] | [105674, 0] | Passed |
| partial-repair probe 2 | [314655, 0] | [314655, 0] | Passed |
| partial-repair probe 3 | [452785, 0] | [452785, 0] | Passed |
| normal control 4 | [424869, 0] | [424869, 0] | Passed |
| normal control 5 | [245306, 245306] | [245306, 245306] | Passed |
| normal control 6 | [273429, 252469] | [273429, 252469] | Passed |
| normal control 7 | [376984, 286154] | [376984, 286154] | Passed |
SHA-256 / 9d9a29cda2260eb0bb38eb55b5f2a97bc876202653be8692cff6fed294dffcfd
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 2026-09-29T14:46:35.164877+00:00.
Case digest / 94d9cf8c9aa996d5e14f00e9b7033f1f6154bf43ae9bc28cbfa7b2935708905f