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FA-59281 / Payroll withholding rules / Open access

Employment tax deposit due date: monthly lookback boundary · case 01

Employers with exactly 50,000 of lookback liability are put on the semiweekly schedule.

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

ROOT CAUSE

The monthly schedule test excludes a lookback of exactly 50,000.00.

VERIFIED REPAIR

Restore the contract rule at the monthly lookback boundary step: use `if x['lookback_total'] <= 5000000:`.

Unsuccessful approach: The attempt tests the current accumulated liability instead of the lookback period.

Case contract

Input {pay_date, lookback_total, accumulated}. If accumulated liability is 100,000.00 or more, deposit the next weekday after pay date. Else if lookback liability is 50,000.00 or less, deposit monthly by the 15th of the following month. Else semiweekly: Wed/Thu/Fri paydays deposit the following Wednesday; Sat-Tue paydays the following Friday. Return [schedule, ISO due date].

Why this case matters

Deposit schedules depend on lookback liability and weekday of payment; a wrong due date means penalties.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(x):
    d = datetime.date(*x['pay_date'])
    if x['accumulated'] >= 10000000:
        nd = d + datetime.timedelta(days=1)
        while nd.weekday() >= 5:
            nd += datetime.timedelta(days=1)
        return ['next-day', nd.isoformat()]
    if x['lookback_total'] < 5000000:
        y, m = (d.year + 1, 1) if d.month == 12 else (d.year, d.month + 1)
        return ['monthly', datetime.date(y, m, 15).isoformat()]
    wd = d.weekday()
    if wd in (2, 3, 4):
        delta = (2 - wd) % 7 or 7
    else:
        delta = (4 - wd) % 7
    return ['semiweekly', (d + datetime.timedelta(days=delta)).isoformat()]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'pay_date': [2025, 7, 24], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-08-15']), ('regression', {'pay_date': [2025, 11, 30], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-12-15']), ('partial-repair probe', {'pay_date': [2025, 7, 23], 'lookback_total': 4030366, 'accumulated': 7676976}, ['monthly', '2025-08-15']), ('partial-repair probe', {'pay_date': [2025, 3, 12], 'lookback_total': 5000000, 'accumulated': 5561044}, ['monthly', '2025-04-15']), ('normal control', {'pay_date': [2026, 1, 10], 'lookback_total': 8559426, 'accumulated': 10000000}, ['next-day', '2026-01-12']), ('normal control', {'pay_date': [2025, 10, 17], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-10-22']), ('normal control', {'pay_date': [2025, 7, 6], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2025-07-07']), ('normal control', {'pay_date': [2026, 1, 1], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2026-01-02'])], [('regression', {'pay_date': [2025, 11, 30], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-12-15']), ('regression', {'pay_date': [2025, 3, 12], 'lookback_total': 5000000, 'accumulated': 5561044}, ['monthly', '2025-04-15']), ('partial-repair probe', {'pay_date': [2026, 1, 15], 'lookback_total': 5000001, 'accumulated': 128367}, ['semiweekly', '2026-01-21']), ('partial-repair probe', {'pay_date': [2025, 8, 20], 'lookback_total': 15073801, 'accumulated': 1953477}, ['semiweekly', '2025-08-27']), ('normal control', {'pay_date': [2025, 7, 12], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2025-07-14']), ('normal control', {'pay_date': [2025, 1, 9], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-01-15']), ('normal control', {'pay_date': [2025, 7, 15], 'lookback_total': 7386577, 'accumulated': 7242712}, ['semiweekly', '2025-07-18']), ('normal control', {'pay_date': [2025, 2, 13], 'lookback_total': 2864521, 'accumulated': 10000000}, ['next-day', '2025-02-14'])], [('regression', {'pay_date': [2025, 3, 12], 'lookback_total': 5000000, 'accumulated': 5561044}, ['monthly', '2025-04-15']), ('regression', {'pay_date': [2024, 12, 14], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-01-15']), ('partial-repair probe', {'pay_date': [2026, 1, 15], 'lookback_total': 5000001, 'accumulated': 128367}, ['semiweekly', '2026-01-21']), ('partial-repair probe', {'pay_date': [2025, 8, 20], 'lookback_total': 15073801, 'accumulated': 1953477}, ['semiweekly', '2025-08-27']), ('normal control', {'pay_date': [2024, 12, 22], 'lookback_total': 5000001, 'accumulated': 10000000}, ['next-day', '2024-12-23']), ('normal control', {'pay_date': [2025, 2, 28], 'lookback_total': 5934057, 'accumulated': 9999999}, ['semiweekly', '2025-03-05']), ('normal control', {'pay_date': [2025, 1, 28], 'lookback_total': 14733386, 'accumulated': 9999999}, ['semiweekly', '2025-01-31']), ('normal control', {'pay_date': [2025, 10, 25], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-10-31'])], [('regression', {'pay_date': [2024, 12, 14], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-01-15']), ('regression', {'pay_date': [2025, 4, 9], 'lookback_total': 5000000, 'accumulated': 17594}, ['monthly', '2025-05-15']), ('partial-repair probe', {'pay_date': [2025, 1, 28], 'lookback_total': 5000001, 'accumulated': 2892395}, ['semiweekly', '2025-01-31']), ('partial-repair probe', {'pay_date': [2025, 12, 21], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2026-01-15']), ('normal control', {'pay_date': [2025, 12, 8], 'lookback_total': 5000001, 'accumulated': 10000000}, ['next-day', '2025-12-09']), ('normal control', {'pay_date': [2026, 1, 21], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2026-01-22']), ('normal control', {'pay_date': [2025, 11, 21], 'lookback_total': 130512, 'accumulated': 10000000}, ['next-day', '2025-11-24']), ('normal control', {'pay_date': [2025, 11, 22], 'lookback_total': 19257323, 'accumulated': 9999999}, ['semiweekly', '2025-11-28'])], [('regression', {'pay_date': [2025, 12, 21], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2026-01-15']), ('regression', {'pay_date': [2025, 4, 9], 'lookback_total': 5000000, 'accumulated': 17594}, ['monthly', '2025-05-15']), ('partial-repair probe', {'pay_date': [2024, 12, 8], 'lookback_total': 7946403, 'accumulated': 200852}, ['semiweekly', '2024-12-13']), ('partial-repair probe', {'pay_date': [2025, 9, 18], 'lookback_total': 5000001, 'accumulated': 171509}, ['semiweekly', '2025-09-24']), ('normal control', {'pay_date': [2024, 11, 22], 'lookback_total': 5000001, 'accumulated': 10000000}, ['next-day', '2024-11-25']), ('normal control', {'pay_date': [2025, 4, 26], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2025-04-28']), ('normal control', {'pay_date': [2025, 10, 18], 'lookback_total': 17305535, 'accumulated': 10000000}, ['next-day', '2025-10-20']), ('normal control', {'pay_date': [2025, 4, 28], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-05-02'])]]
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['semiweekly', '2025-07-30']['monthly', '2025-08-15']Failed
regression 1['semiweekly', '2025-12-05']['monthly', '2025-12-15']Failed
partial-repair probe 2['monthly', '2025-08-15']['monthly', '2025-08-15']Passed
partial-repair probe 3['semiweekly', '2025-03-19']['monthly', '2025-04-15']Failed
normal control 4['next-day', '2026-01-12']['next-day', '2026-01-12']Passed
normal control 5['semiweekly', '2025-10-22']['semiweekly', '2025-10-22']Passed
normal control 6['next-day', '2025-07-07']['next-day', '2025-07-07']Passed
normal control 7['next-day', '2026-01-02']['next-day', '2026-01-02']Passed

SHA-256 / 2ff41f1958d6136e7d1540fa32c04d8250c7dabd53db578a6a40e6f38a12328f

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(x):
    d = datetime.date(*x['pay_date'])
    if x['accumulated'] >= 10000000:
        nd = d + datetime.timedelta(days=1)
        while nd.weekday() >= 5:
            nd += datetime.timedelta(days=1)
        return ['next-day', nd.isoformat()]
    if x['accumulated'] <= 5000000:
        y, m = (d.year + 1, 1) if d.month == 12 else (d.year, d.month + 1)
        return ['monthly', datetime.date(y, m, 15).isoformat()]
    wd = d.weekday()
    if wd in (2, 3, 4):
        delta = (2 - wd) % 7 or 7
    else:
        delta = (4 - wd) % 7
    return ['semiweekly', (d + datetime.timedelta(days=delta)).isoformat()]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'pay_date': [2025, 7, 24], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-08-15']), ('regression', {'pay_date': [2025, 11, 30], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-12-15']), ('partial-repair probe', {'pay_date': [2025, 7, 23], 'lookback_total': 4030366, 'accumulated': 7676976}, ['monthly', '2025-08-15']), ('partial-repair probe', {'pay_date': [2025, 3, 12], 'lookback_total': 5000000, 'accumulated': 5561044}, ['monthly', '2025-04-15']), ('normal control', {'pay_date': [2026, 1, 10], 'lookback_total': 8559426, 'accumulated': 10000000}, ['next-day', '2026-01-12']), ('normal control', {'pay_date': [2025, 10, 17], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-10-22']), ('normal control', {'pay_date': [2025, 7, 6], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2025-07-07']), ('normal control', {'pay_date': [2026, 1, 1], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2026-01-02'])], [('regression', {'pay_date': [2025, 11, 30], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-12-15']), ('regression', {'pay_date': [2025, 3, 12], 'lookback_total': 5000000, 'accumulated': 5561044}, ['monthly', '2025-04-15']), ('partial-repair probe', {'pay_date': [2026, 1, 15], 'lookback_total': 5000001, 'accumulated': 128367}, ['semiweekly', '2026-01-21']), ('partial-repair probe', {'pay_date': [2025, 8, 20], 'lookback_total': 15073801, 'accumulated': 1953477}, ['semiweekly', '2025-08-27']), ('normal control', {'pay_date': [2025, 7, 12], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2025-07-14']), ('normal control', {'pay_date': [2025, 1, 9], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-01-15']), ('normal control', {'pay_date': [2025, 7, 15], 'lookback_total': 7386577, 'accumulated': 7242712}, ['semiweekly', '2025-07-18']), ('normal control', {'pay_date': [2025, 2, 13], 'lookback_total': 2864521, 'accumulated': 10000000}, ['next-day', '2025-02-14'])], [('regression', {'pay_date': [2025, 3, 12], 'lookback_total': 5000000, 'accumulated': 5561044}, ['monthly', '2025-04-15']), ('regression', {'pay_date': [2024, 12, 14], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-01-15']), ('partial-repair probe', {'pay_date': [2026, 1, 15], 'lookback_total': 5000001, 'accumulated': 128367}, ['semiweekly', '2026-01-21']), ('partial-repair probe', {'pay_date': [2025, 8, 20], 'lookback_total': 15073801, 'accumulated': 1953477}, ['semiweekly', '2025-08-27']), ('normal control', {'pay_date': [2024, 12, 22], 'lookback_total': 5000001, 'accumulated': 10000000}, ['next-day', '2024-12-23']), ('normal control', {'pay_date': [2025, 2, 28], 'lookback_total': 5934057, 'accumulated': 9999999}, ['semiweekly', '2025-03-05']), ('normal control', {'pay_date': [2025, 1, 28], 'lookback_total': 14733386, 'accumulated': 9999999}, ['semiweekly', '2025-01-31']), ('normal control', {'pay_date': [2025, 10, 25], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-10-31'])], [('regression', {'pay_date': [2024, 12, 14], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-01-15']), ('regression', {'pay_date': [2025, 4, 9], 'lookback_total': 5000000, 'accumulated': 17594}, ['monthly', '2025-05-15']), ('partial-repair probe', {'pay_date': [2025, 1, 28], 'lookback_total': 5000001, 'accumulated': 2892395}, ['semiweekly', '2025-01-31']), ('partial-repair probe', {'pay_date': [2025, 12, 21], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2026-01-15']), ('normal control', {'pay_date': [2025, 12, 8], 'lookback_total': 5000001, 'accumulated': 10000000}, ['next-day', '2025-12-09']), ('normal control', {'pay_date': [2026, 1, 21], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2026-01-22']), ('normal control', {'pay_date': [2025, 11, 21], 'lookback_total': 130512, 'accumulated': 10000000}, ['next-day', '2025-11-24']), ('normal control', {'pay_date': [2025, 11, 22], 'lookback_total': 19257323, 'accumulated': 9999999}, ['semiweekly', '2025-11-28'])], [('regression', {'pay_date': [2025, 12, 21], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2026-01-15']), ('regression', {'pay_date': [2025, 4, 9], 'lookback_total': 5000000, 'accumulated': 17594}, ['monthly', '2025-05-15']), ('partial-repair probe', {'pay_date': [2024, 12, 8], 'lookback_total': 7946403, 'accumulated': 200852}, ['semiweekly', '2024-12-13']), ('partial-repair probe', {'pay_date': [2025, 9, 18], 'lookback_total': 5000001, 'accumulated': 171509}, ['semiweekly', '2025-09-24']), ('normal control', {'pay_date': [2024, 11, 22], 'lookback_total': 5000001, 'accumulated': 10000000}, ['next-day', '2024-11-25']), ('normal control', {'pay_date': [2025, 4, 26], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2025-04-28']), ('normal control', {'pay_date': [2025, 10, 18], 'lookback_total': 17305535, 'accumulated': 10000000}, ['next-day', '2025-10-20']), ('normal control', {'pay_date': [2025, 4, 28], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-05-02'])]]
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['semiweekly', '2025-07-30']['monthly', '2025-08-15']Failed
regression 1['semiweekly', '2025-12-05']['monthly', '2025-12-15']Failed
partial-repair probe 2['semiweekly', '2025-07-30']['monthly', '2025-08-15']Failed
partial-repair probe 3['semiweekly', '2025-03-19']['monthly', '2025-04-15']Failed
normal control 4['next-day', '2026-01-12']['next-day', '2026-01-12']Passed
normal control 5['semiweekly', '2025-10-22']['semiweekly', '2025-10-22']Passed
normal control 6['next-day', '2025-07-07']['next-day', '2025-07-07']Passed
normal control 7['next-day', '2026-01-02']['next-day', '2026-01-02']Passed

SHA-256 / 21e2bd1094c48376df6dbdafce05eb401edef2def479cddf2cfff64606af7fd5

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(x):
    d = datetime.date(*x['pay_date'])
    if x['accumulated'] >= 10000000:
        nd = d + datetime.timedelta(days=1)
        while nd.weekday() >= 5:
            nd += datetime.timedelta(days=1)
        return ['next-day', nd.isoformat()]
    if x['lookback_total'] <= 5000000:
        y, m = (d.year + 1, 1) if d.month == 12 else (d.year, d.month + 1)
        return ['monthly', datetime.date(y, m, 15).isoformat()]
    wd = d.weekday()
    if wd in (2, 3, 4):
        delta = (2 - wd) % 7 or 7
    else:
        delta = (4 - wd) % 7
    return ['semiweekly', (d + datetime.timedelta(days=delta)).isoformat()]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'pay_date': [2025, 7, 24], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-08-15']), ('regression', {'pay_date': [2025, 11, 30], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-12-15']), ('partial-repair probe', {'pay_date': [2025, 7, 23], 'lookback_total': 4030366, 'accumulated': 7676976}, ['monthly', '2025-08-15']), ('partial-repair probe', {'pay_date': [2025, 3, 12], 'lookback_total': 5000000, 'accumulated': 5561044}, ['monthly', '2025-04-15']), ('normal control', {'pay_date': [2026, 1, 10], 'lookback_total': 8559426, 'accumulated': 10000000}, ['next-day', '2026-01-12']), ('normal control', {'pay_date': [2025, 10, 17], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-10-22']), ('normal control', {'pay_date': [2025, 7, 6], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2025-07-07']), ('normal control', {'pay_date': [2026, 1, 1], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2026-01-02'])], [('regression', {'pay_date': [2025, 11, 30], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-12-15']), ('regression', {'pay_date': [2025, 3, 12], 'lookback_total': 5000000, 'accumulated': 5561044}, ['monthly', '2025-04-15']), ('partial-repair probe', {'pay_date': [2026, 1, 15], 'lookback_total': 5000001, 'accumulated': 128367}, ['semiweekly', '2026-01-21']), ('partial-repair probe', {'pay_date': [2025, 8, 20], 'lookback_total': 15073801, 'accumulated': 1953477}, ['semiweekly', '2025-08-27']), ('normal control', {'pay_date': [2025, 7, 12], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2025-07-14']), ('normal control', {'pay_date': [2025, 1, 9], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-01-15']), ('normal control', {'pay_date': [2025, 7, 15], 'lookback_total': 7386577, 'accumulated': 7242712}, ['semiweekly', '2025-07-18']), ('normal control', {'pay_date': [2025, 2, 13], 'lookback_total': 2864521, 'accumulated': 10000000}, ['next-day', '2025-02-14'])], [('regression', {'pay_date': [2025, 3, 12], 'lookback_total': 5000000, 'accumulated': 5561044}, ['monthly', '2025-04-15']), ('regression', {'pay_date': [2024, 12, 14], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-01-15']), ('partial-repair probe', {'pay_date': [2026, 1, 15], 'lookback_total': 5000001, 'accumulated': 128367}, ['semiweekly', '2026-01-21']), ('partial-repair probe', {'pay_date': [2025, 8, 20], 'lookback_total': 15073801, 'accumulated': 1953477}, ['semiweekly', '2025-08-27']), ('normal control', {'pay_date': [2024, 12, 22], 'lookback_total': 5000001, 'accumulated': 10000000}, ['next-day', '2024-12-23']), ('normal control', {'pay_date': [2025, 2, 28], 'lookback_total': 5934057, 'accumulated': 9999999}, ['semiweekly', '2025-03-05']), ('normal control', {'pay_date': [2025, 1, 28], 'lookback_total': 14733386, 'accumulated': 9999999}, ['semiweekly', '2025-01-31']), ('normal control', {'pay_date': [2025, 10, 25], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-10-31'])], [('regression', {'pay_date': [2024, 12, 14], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2025-01-15']), ('regression', {'pay_date': [2025, 4, 9], 'lookback_total': 5000000, 'accumulated': 17594}, ['monthly', '2025-05-15']), ('partial-repair probe', {'pay_date': [2025, 1, 28], 'lookback_total': 5000001, 'accumulated': 2892395}, ['semiweekly', '2025-01-31']), ('partial-repair probe', {'pay_date': [2025, 12, 21], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2026-01-15']), ('normal control', {'pay_date': [2025, 12, 8], 'lookback_total': 5000001, 'accumulated': 10000000}, ['next-day', '2025-12-09']), ('normal control', {'pay_date': [2026, 1, 21], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2026-01-22']), ('normal control', {'pay_date': [2025, 11, 21], 'lookback_total': 130512, 'accumulated': 10000000}, ['next-day', '2025-11-24']), ('normal control', {'pay_date': [2025, 11, 22], 'lookback_total': 19257323, 'accumulated': 9999999}, ['semiweekly', '2025-11-28'])], [('regression', {'pay_date': [2025, 12, 21], 'lookback_total': 5000000, 'accumulated': 9999999}, ['monthly', '2026-01-15']), ('regression', {'pay_date': [2025, 4, 9], 'lookback_total': 5000000, 'accumulated': 17594}, ['monthly', '2025-05-15']), ('partial-repair probe', {'pay_date': [2024, 12, 8], 'lookback_total': 7946403, 'accumulated': 200852}, ['semiweekly', '2024-12-13']), ('partial-repair probe', {'pay_date': [2025, 9, 18], 'lookback_total': 5000001, 'accumulated': 171509}, ['semiweekly', '2025-09-24']), ('normal control', {'pay_date': [2024, 11, 22], 'lookback_total': 5000001, 'accumulated': 10000000}, ['next-day', '2024-11-25']), ('normal control', {'pay_date': [2025, 4, 26], 'lookback_total': 5000000, 'accumulated': 10000000}, ['next-day', '2025-04-28']), ('normal control', {'pay_date': [2025, 10, 18], 'lookback_total': 17305535, 'accumulated': 10000000}, ['next-day', '2025-10-20']), ('normal control', {'pay_date': [2025, 4, 28], 'lookback_total': 5000001, 'accumulated': 9999999}, ['semiweekly', '2025-05-02'])]]
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['monthly', '2025-08-15']['monthly', '2025-08-15']Passed
regression 1['monthly', '2025-12-15']['monthly', '2025-12-15']Passed
partial-repair probe 2['monthly', '2025-08-15']['monthly', '2025-08-15']Passed
partial-repair probe 3['monthly', '2025-04-15']['monthly', '2025-04-15']Passed
normal control 4['next-day', '2026-01-12']['next-day', '2026-01-12']Passed
normal control 5['semiweekly', '2025-10-22']['semiweekly', '2025-10-22']Passed
normal control 6['next-day', '2025-07-07']['next-day', '2025-07-07']Passed
normal control 7['next-day', '2026-01-02']['next-day', '2026-01-02']Passed

SHA-256 / 3bad7f6fa996f03e19c86445f4f27b2073809e9389268af7f38b74f73e4e5ae9

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 2026-09-29T14:46:34.792972+00:00.

Case digest / da3c0621b4b2862e3e0d55a0f6dcb53d7916b4c001255f780f828ec7bec1e2ff