FA-59141 / Payroll withholding rules / Open access

W-4 exempt claim expiration: claim effective date · case 01

A W-4 received on the pay date is ignored for that paycheck.

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

ROOT CAUSE

Claims dated on the pay date are excluded by a strict comparison.

VERIFIED REPAIR

Restore the contract rule at the claim effective date step: use `if tuple(date) <= pay:`.

Unsuccessful approach: The attempt compares years only, applying forms filed later in the same year to earlier paychecks.

Case contract

Input {claims: [[[y,m,d], exempt]], pay_date [y,m,d]}. The active claim is the latest-dated claim on or before the pay date (claims processed by date; equal dates keep input order, last wins). No claim -> 'single-default'. Non-exempt claim -> 'per-form'. Exempt claim is honored through February 15 of the year after it was filed, inclusive; afterwards 'single-default'.

Why this case matters

Exempt status must lapse on a fixed date, reverting to default withholding until a new form arrives.

1 / The failure

Exit 1

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

N = 1
observations = []
def solve(x):
    pay = tuple(x['pay_date'])
    active = None
    for date, exempt in sorted(x['claims'], key=lambda c: tuple(c[0])):
        if tuple(date) < pay:
            active = (tuple(date), exempt)
    if active is None:
        return 'single-default'
    if not active[1]:
        return 'per-form'
    expiry = (active[0][0] + 1, 2, 15)
    return 'exempt' if pay <= expiry else 'single-default'
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'claims': [[[2022, 4, 21], True], [[2022, 12, 3], False]], 'pay_date': [2022, 4, 21]}, 'exempt'), ('regression', {'claims': [[[2023, 10, 8], True], [[2024, 3, 27], True]], 'pay_date': [2023, 10, 8]}, 'exempt'), ('partial-repair probe', {'claims': [[[2023, 10, 8], False], [[2024, 8, 1], False]], 'pay_date': [2023, 2, 16]}, 'single-default'), ('partial-repair probe', {'claims': [[[2024, 11, 25], False], [[2022, 6, 11], True], [[2024, 3, 25], True]], 'pay_date': [2024, 4, 3]}, 'exempt'), ('normal control', {'claims': [[[2022, 2, 13], True], [[2023, 3, 1], False], [[2022, 3, 22], False]], 'pay_date': [2024, 2, 27]}, 'per-form'), ('normal control', {'claims': [[[2023, 2, 25], False]], 'pay_date': [2025, 12, 24]}, 'per-form'), ('normal control', {'claims': [[[2023, 9, 19], True], [[2023, 8, 5], True], [[2022, 3, 16], False]], 'pay_date': [2023, 9, 19]}, 'exempt'), ('normal control', {'claims': [], 'pay_date': [2025, 2, 4]}, 'single-default')], [('regression', {'claims': [[[2024, 3, 28], False], [[2024, 8, 12], True], [[2022, 9, 27], True]], 'pay_date': [2024, 3, 28]}, 'per-form'), ('regression', {'claims': [[[2023, 9, 14], False], [[2024, 8, 7], True], [[2024, 1, 1], True]], 'pay_date': [2023, 9, 14]}, 'per-form'), ('partial-repair probe', {'claims': [[[2023, 3, 12], True]], 'pay_date': [2023, 2, 16]}, 'single-default'), ('partial-repair probe', {'claims': [[[2024, 8, 16], True], [[2023, 10, 2], True]], 'pay_date': [2023, 2, 18]}, 'single-default'), ('normal control', {'claims': [[[2023, 7, 12], False], [[2023, 2, 5], False], [[2024, 5, 28], False]], 'pay_date': [2024, 2, 16]}, 'per-form'), ('normal control', {'claims': [[[2022, 7, 27], False], [[2022, 1, 5], True], [[2022, 8, 16], True]], 'pay_date': [2024, 5, 27]}, 'single-default'), ('normal control', {'claims': [[[2023, 4, 5], True]], 'pay_date': [2024, 11, 6]}, 'single-default'), ('normal control', {'claims': [], 'pay_date': [2023, 2, 16]}, 'single-default')], [('regression', {'claims': [[[2023, 4, 5], False], [[2023, 8, 19], True]], 'pay_date': [2023, 4, 5]}, 'per-form'), ('regression', {'claims': [[[2022, 5, 10], True]], 'pay_date': [2022, 5, 10]}, 'exempt'), ('partial-repair probe', {'claims': [[[2022, 9, 2], True], [[2024, 9, 25], False], [[2023, 4, 4], True]], 'pay_date': [2024, 2, 26]}, 'single-default'), ('partial-repair probe', {'claims': [[[2024, 8, 28], False]], 'pay_date': [2024, 2, 16]}, 'single-default'), ('normal control', {'claims': [], 'pay_date': [2023, 2, 15]}, 'single-default'), ('normal control', {'claims': [[[2024, 1, 17], False]], 'pay_date': [2025, 2, 16]}, 'per-form'), ('normal control', {'claims': [[[2022, 1, 6], False], [[2024, 2, 7], False]], 'pay_date': [2024, 2, 12]}, 'per-form'), ('normal control', {'claims': [[[2023, 2, 6], True]], 'pay_date': [2024, 2, 13]}, 'exempt')], [('regression', {'claims': [[[2024, 2, 22], True], [[2023, 11, 10], False], [[2024, 4, 24], False]], 'pay_date': [2024, 2, 22]}, 'exempt'), ('regression', {'claims': [[[2023, 4, 26], True], [[2022, 11, 26], False]], 'pay_date': [2023, 4, 26]}, 'exempt'), ('partial-repair probe', {'claims': [[[2024, 4, 16], True], [[2024, 7, 4], True]], 'pay_date': [2024, 2, 15]}, 'single-default'), ('partial-repair probe', {'claims': [[[2023, 12, 28], True], [[2022, 5, 21], False]], 'pay_date': [2023, 2, 16]}, 'per-form'), ('normal control', {'claims': [[[2023, 4, 9], False], [[2024, 8, 26], False], [[2022, 5, 3], False]], 'pay_date': [2024, 7, 21]}, 'per-form'), ('normal control', {'claims': [], 'pay_date': [2023, 2, 27]}, 'single-default'), ('normal control', {'claims': [[[2024, 6, 18], True], [[2024, 1, 24], True], [[2024, 8, 3], True]], 'pay_date': [2025, 2, 16]}, 'single-default'), ('normal control', {'claims': [[[2023, 9, 6], True], [[2023, 1, 17], False], [[2022, 11, 5], True]], 'pay_date': [2025, 2, 16]}, 'single-default')], [('regression', {'claims': [[[2023, 2, 24], False], [[2022, 4, 18], True], [[2023, 7, 8], True]], 'pay_date': [2023, 2, 24]}, 'per-form'), ('regression', {'claims': [[[2023, 2, 17], True], [[2024, 9, 16], False]], 'pay_date': [2023, 2, 17]}, 'exempt'), ('partial-repair probe', {'claims': [[[2024, 3, 6], True], [[2024, 11, 28], True], [[2022, 3, 25], True]], 'pay_date': [2024, 2, 15]}, 'single-default'), ('partial-repair probe', {'claims': [[[2022, 4, 14], True], [[2023, 9, 6], False], [[2023, 3, 2], True]], 'pay_date': [2023, 2, 16]}, 'single-default'), ('normal control', {'claims': [[[2024, 12, 15], False], [[2023, 9, 28], False]], 'pay_date': [2025, 2, 3]}, 'per-form'), ('normal control', {'claims': [], 'pay_date': [2024, 7, 6]}, 'single-default'), ('normal control', {'claims': [[[2022, 7, 16], True], [[2024, 7, 18], True]], 'pay_date': [2025, 2, 15]}, 'exempt'), ('normal control', {'claims': [[[2022, 1, 12], True], [[2022, 3, 16], True]], 'pay_date': [2024, 5, 17]}, 'single-default')]]
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	`single-default`	`exempt`	Failed
regression 1	`single-default`	`exempt`	Failed
partial-repair probe 2	`single-default`	`single-default`	Passed
partial-repair probe 3	`exempt`	`exempt`	Passed
normal control 4	`per-form`	`per-form`	Passed
normal control 5	`per-form`	`per-form`	Passed
normal control 6	`exempt`	`exempt`	Passed
normal control 7	`single-default`	`single-default`	Passed

SHA-256 / 3843c04311791981c0d05186baa07af44bc6918a1b8e1dbfeba2c65c3126225e

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(x):
    pay = tuple(x['pay_date'])
    active = None
    for date, exempt in sorted(x['claims'], key=lambda c: tuple(c[0])):
        if date[0] <= pay[0]:
            active = (tuple(date), exempt)
    if active is None:
        return 'single-default'
    if not active[1]:
        return 'per-form'
    expiry = (active[0][0] + 1, 2, 15)
    return 'exempt' if pay <= expiry else 'single-default'
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'claims': [[[2022, 4, 21], True], [[2022, 12, 3], False]], 'pay_date': [2022, 4, 21]}, 'exempt'), ('regression', {'claims': [[[2023, 10, 8], True], [[2024, 3, 27], True]], 'pay_date': [2023, 10, 8]}, 'exempt'), ('partial-repair probe', {'claims': [[[2023, 10, 8], False], [[2024, 8, 1], False]], 'pay_date': [2023, 2, 16]}, 'single-default'), ('partial-repair probe', {'claims': [[[2024, 11, 25], False], [[2022, 6, 11], True], [[2024, 3, 25], True]], 'pay_date': [2024, 4, 3]}, 'exempt'), ('normal control', {'claims': [[[2022, 2, 13], True], [[2023, 3, 1], False], [[2022, 3, 22], False]], 'pay_date': [2024, 2, 27]}, 'per-form'), ('normal control', {'claims': [[[2023, 2, 25], False]], 'pay_date': [2025, 12, 24]}, 'per-form'), ('normal control', {'claims': [[[2023, 9, 19], True], [[2023, 8, 5], True], [[2022, 3, 16], False]], 'pay_date': [2023, 9, 19]}, 'exempt'), ('normal control', {'claims': [], 'pay_date': [2025, 2, 4]}, 'single-default')], [('regression', {'claims': [[[2024, 3, 28], False], [[2024, 8, 12], True], [[2022, 9, 27], True]], 'pay_date': [2024, 3, 28]}, 'per-form'), ('regression', {'claims': [[[2023, 9, 14], False], [[2024, 8, 7], True], [[2024, 1, 1], True]], 'pay_date': [2023, 9, 14]}, 'per-form'), ('partial-repair probe', {'claims': [[[2023, 3, 12], True]], 'pay_date': [2023, 2, 16]}, 'single-default'), ('partial-repair probe', {'claims': [[[2024, 8, 16], True], [[2023, 10, 2], True]], 'pay_date': [2023, 2, 18]}, 'single-default'), ('normal control', {'claims': [[[2023, 7, 12], False], [[2023, 2, 5], False], [[2024, 5, 28], False]], 'pay_date': [2024, 2, 16]}, 'per-form'), ('normal control', {'claims': [[[2022, 7, 27], False], [[2022, 1, 5], True], [[2022, 8, 16], True]], 'pay_date': [2024, 5, 27]}, 'single-default'), ('normal control', {'claims': [[[2023, 4, 5], True]], 'pay_date': [2024, 11, 6]}, 'single-default'), ('normal control', {'claims': [], 'pay_date': [2023, 2, 16]}, 'single-default')], [('regression', {'claims': [[[2023, 4, 5], False], [[2023, 8, 19], True]], 'pay_date': [2023, 4, 5]}, 'per-form'), ('regression', {'claims': [[[2022, 5, 10], True]], 'pay_date': [2022, 5, 10]}, 'exempt'), ('partial-repair probe', {'claims': [[[2022, 9, 2], True], [[2024, 9, 25], False], [[2023, 4, 4], True]], 'pay_date': [2024, 2, 26]}, 'single-default'), ('partial-repair probe', {'claims': [[[2024, 8, 28], False]], 'pay_date': [2024, 2, 16]}, 'single-default'), ('normal control', {'claims': [], 'pay_date': [2023, 2, 15]}, 'single-default'), ('normal control', {'claims': [[[2024, 1, 17], False]], 'pay_date': [2025, 2, 16]}, 'per-form'), ('normal control', {'claims': [[[2022, 1, 6], False], [[2024, 2, 7], False]], 'pay_date': [2024, 2, 12]}, 'per-form'), ('normal control', {'claims': [[[2023, 2, 6], True]], 'pay_date': [2024, 2, 13]}, 'exempt')], [('regression', {'claims': [[[2024, 2, 22], True], [[2023, 11, 10], False], [[2024, 4, 24], False]], 'pay_date': [2024, 2, 22]}, 'exempt'), ('regression', {'claims': [[[2023, 4, 26], True], [[2022, 11, 26], False]], 'pay_date': [2023, 4, 26]}, 'exempt'), ('partial-repair probe', {'claims': [[[2024, 4, 16], True], [[2024, 7, 4], True]], 'pay_date': [2024, 2, 15]}, 'single-default'), ('partial-repair probe', {'claims': [[[2023, 12, 28], True], [[2022, 5, 21], False]], 'pay_date': [2023, 2, 16]}, 'per-form'), ('normal control', {'claims': [[[2023, 4, 9], False], [[2024, 8, 26], False], [[2022, 5, 3], False]], 'pay_date': [2024, 7, 21]}, 'per-form'), ('normal control', {'claims': [], 'pay_date': [2023, 2, 27]}, 'single-default'), ('normal control', {'claims': [[[2024, 6, 18], True], [[2024, 1, 24], True], [[2024, 8, 3], True]], 'pay_date': [2025, 2, 16]}, 'single-default'), ('normal control', {'claims': [[[2023, 9, 6], True], [[2023, 1, 17], False], [[2022, 11, 5], True]], 'pay_date': [2025, 2, 16]}, 'single-default')], [('regression', {'claims': [[[2023, 2, 24], False], [[2022, 4, 18], True], [[2023, 7, 8], True]], 'pay_date': [2023, 2, 24]}, 'per-form'), ('regression', {'claims': [[[2023, 2, 17], True], [[2024, 9, 16], False]], 'pay_date': [2023, 2, 17]}, 'exempt'), ('partial-repair probe', {'claims': [[[2024, 3, 6], True], [[2024, 11, 28], True], [[2022, 3, 25], True]], 'pay_date': [2024, 2, 15]}, 'single-default'), ('partial-repair probe', {'claims': [[[2022, 4, 14], True], [[2023, 9, 6], False], [[2023, 3, 2], True]], 'pay_date': [2023, 2, 16]}, 'single-default'), ('normal control', {'claims': [[[2024, 12, 15], False], [[2023, 9, 28], False]], 'pay_date': [2025, 2, 3]}, 'per-form'), ('normal control', {'claims': [], 'pay_date': [2024, 7, 6]}, 'single-default'), ('normal control', {'claims': [[[2022, 7, 16], True], [[2024, 7, 18], True]], 'pay_date': [2025, 2, 15]}, 'exempt'), ('normal control', {'claims': [[[2022, 1, 12], True], [[2022, 3, 16], True]], 'pay_date': [2024, 5, 17]}, 'single-default')]]
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	`per-form`	`exempt`	Failed
regression 1	`exempt`	`exempt`	Passed
partial-repair probe 2	`per-form`	`single-default`	Failed
partial-repair probe 3	`per-form`	`exempt`	Failed
normal control 4	`per-form`	`per-form`	Passed
normal control 5	`per-form`	`per-form`	Passed
normal control 6	`exempt`	`exempt`	Passed
normal control 7	`single-default`	`single-default`	Passed

SHA-256 / 5deda439d3fd2ca17f83e68adac918181d2ee10604d20f371ef4abbafc2c8e65

3 / The verified repair

Exit 0

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

N = 1
observations = []
def solve(x):
    pay = tuple(x['pay_date'])
    active = None
    for date, exempt in sorted(x['claims'], key=lambda c: tuple(c[0])):
        if tuple(date) <= pay:
            active = (tuple(date), exempt)
    if active is None:
        return 'single-default'
    if not active[1]:
        return 'per-form'
    expiry = (active[0][0] + 1, 2, 15)
    return 'exempt' if pay <= expiry else 'single-default'
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'claims': [[[2022, 4, 21], True], [[2022, 12, 3], False]], 'pay_date': [2022, 4, 21]}, 'exempt'), ('regression', {'claims': [[[2023, 10, 8], True], [[2024, 3, 27], True]], 'pay_date': [2023, 10, 8]}, 'exempt'), ('partial-repair probe', {'claims': [[[2023, 10, 8], False], [[2024, 8, 1], False]], 'pay_date': [2023, 2, 16]}, 'single-default'), ('partial-repair probe', {'claims': [[[2024, 11, 25], False], [[2022, 6, 11], True], [[2024, 3, 25], True]], 'pay_date': [2024, 4, 3]}, 'exempt'), ('normal control', {'claims': [[[2022, 2, 13], True], [[2023, 3, 1], False], [[2022, 3, 22], False]], 'pay_date': [2024, 2, 27]}, 'per-form'), ('normal control', {'claims': [[[2023, 2, 25], False]], 'pay_date': [2025, 12, 24]}, 'per-form'), ('normal control', {'claims': [[[2023, 9, 19], True], [[2023, 8, 5], True], [[2022, 3, 16], False]], 'pay_date': [2023, 9, 19]}, 'exempt'), ('normal control', {'claims': [], 'pay_date': [2025, 2, 4]}, 'single-default')], [('regression', {'claims': [[[2024, 3, 28], False], [[2024, 8, 12], True], [[2022, 9, 27], True]], 'pay_date': [2024, 3, 28]}, 'per-form'), ('regression', {'claims': [[[2023, 9, 14], False], [[2024, 8, 7], True], [[2024, 1, 1], True]], 'pay_date': [2023, 9, 14]}, 'per-form'), ('partial-repair probe', {'claims': [[[2023, 3, 12], True]], 'pay_date': [2023, 2, 16]}, 'single-default'), ('partial-repair probe', {'claims': [[[2024, 8, 16], True], [[2023, 10, 2], True]], 'pay_date': [2023, 2, 18]}, 'single-default'), ('normal control', {'claims': [[[2023, 7, 12], False], [[2023, 2, 5], False], [[2024, 5, 28], False]], 'pay_date': [2024, 2, 16]}, 'per-form'), ('normal control', {'claims': [[[2022, 7, 27], False], [[2022, 1, 5], True], [[2022, 8, 16], True]], 'pay_date': [2024, 5, 27]}, 'single-default'), ('normal control', {'claims': [[[2023, 4, 5], True]], 'pay_date': [2024, 11, 6]}, 'single-default'), ('normal control', {'claims': [], 'pay_date': [2023, 2, 16]}, 'single-default')], [('regression', {'claims': [[[2023, 4, 5], False], [[2023, 8, 19], True]], 'pay_date': [2023, 4, 5]}, 'per-form'), ('regression', {'claims': [[[2022, 5, 10], True]], 'pay_date': [2022, 5, 10]}, 'exempt'), ('partial-repair probe', {'claims': [[[2022, 9, 2], True], [[2024, 9, 25], False], [[2023, 4, 4], True]], 'pay_date': [2024, 2, 26]}, 'single-default'), ('partial-repair probe', {'claims': [[[2024, 8, 28], False]], 'pay_date': [2024, 2, 16]}, 'single-default'), ('normal control', {'claims': [], 'pay_date': [2023, 2, 15]}, 'single-default'), ('normal control', {'claims': [[[2024, 1, 17], False]], 'pay_date': [2025, 2, 16]}, 'per-form'), ('normal control', {'claims': [[[2022, 1, 6], False], [[2024, 2, 7], False]], 'pay_date': [2024, 2, 12]}, 'per-form'), ('normal control', {'claims': [[[2023, 2, 6], True]], 'pay_date': [2024, 2, 13]}, 'exempt')], [('regression', {'claims': [[[2024, 2, 22], True], [[2023, 11, 10], False], [[2024, 4, 24], False]], 'pay_date': [2024, 2, 22]}, 'exempt'), ('regression', {'claims': [[[2023, 4, 26], True], [[2022, 11, 26], False]], 'pay_date': [2023, 4, 26]}, 'exempt'), ('partial-repair probe', {'claims': [[[2024, 4, 16], True], [[2024, 7, 4], True]], 'pay_date': [2024, 2, 15]}, 'single-default'), ('partial-repair probe', {'claims': [[[2023, 12, 28], True], [[2022, 5, 21], False]], 'pay_date': [2023, 2, 16]}, 'per-form'), ('normal control', {'claims': [[[2023, 4, 9], False], [[2024, 8, 26], False], [[2022, 5, 3], False]], 'pay_date': [2024, 7, 21]}, 'per-form'), ('normal control', {'claims': [], 'pay_date': [2023, 2, 27]}, 'single-default'), ('normal control', {'claims': [[[2024, 6, 18], True], [[2024, 1, 24], True], [[2024, 8, 3], True]], 'pay_date': [2025, 2, 16]}, 'single-default'), ('normal control', {'claims': [[[2023, 9, 6], True], [[2023, 1, 17], False], [[2022, 11, 5], True]], 'pay_date': [2025, 2, 16]}, 'single-default')], [('regression', {'claims': [[[2023, 2, 24], False], [[2022, 4, 18], True], [[2023, 7, 8], True]], 'pay_date': [2023, 2, 24]}, 'per-form'), ('regression', {'claims': [[[2023, 2, 17], True], [[2024, 9, 16], False]], 'pay_date': [2023, 2, 17]}, 'exempt'), ('partial-repair probe', {'claims': [[[2024, 3, 6], True], [[2024, 11, 28], True], [[2022, 3, 25], True]], 'pay_date': [2024, 2, 15]}, 'single-default'), ('partial-repair probe', {'claims': [[[2022, 4, 14], True], [[2023, 9, 6], False], [[2023, 3, 2], True]], 'pay_date': [2023, 2, 16]}, 'single-default'), ('normal control', {'claims': [[[2024, 12, 15], False], [[2023, 9, 28], False]], 'pay_date': [2025, 2, 3]}, 'per-form'), ('normal control', {'claims': [], 'pay_date': [2024, 7, 6]}, 'single-default'), ('normal control', {'claims': [[[2022, 7, 16], True], [[2024, 7, 18], True]], 'pay_date': [2025, 2, 15]}, 'exempt'), ('normal control', {'claims': [[[2022, 1, 12], True], [[2022, 3, 16], True]], 'pay_date': [2024, 5, 17]}, 'single-default')]]
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	`exempt`	`exempt`	Passed
regression 1	`exempt`	`exempt`	Passed
partial-repair probe 2	`single-default`	`single-default`	Passed
partial-repair probe 3	`exempt`	`exempt`	Passed
normal control 4	`per-form`	`per-form`	Passed
normal control 5	`per-form`	`per-form`	Passed
normal control 6	`exempt`	`exempt`	Passed
normal control 7	`single-default`	`single-default`	Passed

SHA-256 / 94e6c53630aa0892c2562d9b4d1257d5cd3602d7dfa9750e48ba7e59b07d2ae9

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 2026-09-29T14:46:33.444532+00:00.

Case digest / 93e80137579af1a36e8a1a46f731d8d6ecad9d8897bb1319e805ff1d243c40ee