FA-59181 / Payroll withholding rules / Open access

Cumulative wages withholding method: annualization base · case 01

Employees on the cumulative method are withheld as if on the ordinary per-period method.

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

ROOT CAUSE

Only the current paycheck is annualized, ignoring year-to-date wages and elapsed periods.

VERIFIED REPAIR

Restore the contract rule at the annualization base step: use `annual = Fraction(total * x['periods'], k)`.

Unsuccessful approach: The attempt includes year-to-date wages but treats their sum as the annual figure without annualizing.

Case contract

Input {ytd_wages, ytd_wh, wage, period_index k (1-based, includes this period), periods}. Annualized = (ytd_wages + wage)*periods/k exactly. Annual tax: 0% to 10,000.00, 12% to 40,000.00, 24% above. Tax to date = annual tax * k/periods. Withholding = max(0, tax_to_date - ytd_wh) rounded half-up at the end.

Why this case matters

The cumulative method spreads irregular pay across elapsed periods and trues up against prior withholding.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
import math
N = 1
observations = []
def solve(x):
    k = x['period_index']
    total = x['ytd_wages'] + x['wage']
    annual = Fraction(x['wage'] * x['periods'], 1)
    def tax(a):
        t = Fraction(0)
        if a > 1000000:
            t += (min(a, 4000000) - 1000000) * Fraction(12, 100)
        if a > 4000000:
            t += (a - 4000000) * Fraction(24, 100)
        return t
    due = tax(annual) * k / x['periods']
    wh = max(0, due - x['ytd_wh'])
    return math.floor(wh + Fraction(1, 2))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'ytd_wages': 7060061, 'ytd_wh': 424656, 'wage': 491569, 'period_index': 24, 'periods': 52}, 1110812), ('regression', {'ytd_wages': 2182615, 'ytd_wh': 379871, 'wage': 414670, 'period_index': 20, 'periods': 26}, 0), ('partial-repair probe', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('partial-repair probe', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('normal control', {'ytd_wages': 185427, 'ytd_wh': 36091, 'wage': 108159, 'period_index': 2, 'periods': 12}, 0), ('normal control', {'ytd_wages': 141603, 'ytd_wh': 11497, 'wage': 15563, 'period_index': 7, 'periods': 12}, 0), ('normal control', {'ytd_wages': 29165, 'ytd_wh': 3348, 'wage': 5167, 'period_index': 8, 'periods': 52}, 0), ('normal control', {'ytd_wages': 2675147, 'ytd_wh': 522064, 'wage': 171429, 'period_index': 26, 'periods': 26}, 0)], [('regression', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('regression', {'ytd_wages': 3504142, 'ytd_wh': 423680, 'wage': 219362, 'period_index': 26, 'periods': 26}, 0), ('partial-repair probe', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('partial-repair probe', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('normal control', {'ytd_wages': 78474, 'ytd_wh': 12616, 'wage': 2681, 'period_index': 35, 'periods': 52}, 0), ('normal control', {'ytd_wages': 780083, 'ytd_wh': 115357, 'wage': 60520, 'period_index': 12, 'periods': 26}, 0), ('normal control', {'ytd_wages': 30504, 'ytd_wh': 1425, 'wage': 14017, 'period_index': 11, 'periods': 12}, 0), ('normal control', {'ytd_wages': 255421, 'ytd_wh': 47741, 'wage': 37513, 'period_index': 11, 'periods': 12}, 0)], [('regression', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('regression', {'ytd_wages': 7060061, 'ytd_wh': 424656, 'wage': 491569, 'period_index': 24, 'periods': 52}, 1110812), ('partial-repair probe', {'ytd_wages': 0, 'ytd_wh': 1, 'wage': 366567, 'period_index': 1, 'periods': 12}, 37975), ('partial-repair probe', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('normal control', {'ytd_wages': 4101816, 'ytd_wh': 781382, 'wage': 93819, 'period_index': 46, 'periods': 52}, 0), ('normal control', {'ytd_wages': 1734664, 'ytd_wh': 216863, 'wage': 75980, 'period_index': 15, 'periods': 26}, 0), ('normal control', {'ytd_wages': 2864664, 'ytd_wh': 419863, 'wage': 94961, 'period_index': 20, 'periods': 26}, 0), ('normal control', {'ytd_wages': 430759, 'ytd_wh': 55675, 'wage': 54560, 'period_index': 5, 'periods': 12}, 0)], [('regression', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('regression', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('partial-repair probe', {'ytd_wages': 2724662, 'ytd_wh': 86701, 'wage': 332622, 'period_index': 7, 'periods': 52}, 566278), ('partial-repair probe', {'ytd_wages': 1665992, 'ytd_wh': 58323, 'wage': 387492, 'period_index': 4, 'periods': 12}, 234513), ('normal control', {'ytd_wages': 353147, 'ytd_wh': 9536, 'wage': 96030, 'period_index': 6, 'periods': 12}, 0), ('normal control', {'ytd_wages': 659564, 'ytd_wh': 7849, 'wage': 18236, 'period_index': 20, 'periods': 26}, 0), ('normal control', {'ytd_wages': 2446975, 'ytd_wh': 466368, 'wage': 202023, 'period_index': 12, 'periods': 12}, 0), ('normal control', {'ytd_wages': 152088, 'ytd_wh': 11592, 'wage': 17915, 'period_index': 8, 'periods': 26}, 0)], [('regression', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('regression', {'ytd_wages': 953768, 'ytd_wh': 32122, 'wage': 67782, 'period_index': 21, 'periods': 26}, 0), ('partial-repair probe', {'ytd_wages': 17382079, 'ytd_wh': 703861, 'wage': 564605, 'period_index': 25, 'periods': 52}, 3314882), ('partial-repair probe', {'ytd_wages': 7572146, 'ytd_wh': 1352041, 'wage': 228907, 'period_index': 18, 'periods': 52}, 312519), ('normal control', {'ytd_wages': 2303411, 'ytd_wh': 406780, 'wage': 112030, 'period_index': 23, 'periods': 26}, 0), ('normal control', {'ytd_wages': 288287, 'ytd_wh': 41455, 'wage': 26146, 'period_index': 26, 'periods': 26}, 0), ('normal control', {'ytd_wages': 102743, 'ytd_wh': 20507, 'wage': 5626, 'period_index': 12, 'periods': 12}, 0), ('normal control', {'ytd_wages': 1160977, 'ytd_wh': 199709, 'wage': 91537, 'period_index': 11, 'periods': 26}, 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 fixture	Actual	Expected	Outcome
regression 0	`2129858`	`1110812`	Failed
regression 1	`1149007`	`0`	Failed
partial-repair probe 2	`2078481`	`1684840`	Failed
partial-repair probe 3	`0`	`15666`	Failed
normal control 4	`0`	`0`	Passed
normal control 5	`0`	`0`	Passed
normal control 6	`0`	`0`	Passed
normal control 7	`0`	`0`	Passed

SHA-256 / 15d58b90574151d6611b6583614581977923551c4f18d1c7fe7dbd913cbca943

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
import math
N = 1
observations = []
def solve(x):
    k = x['period_index']
    total = x['ytd_wages'] + x['wage']
    annual = Fraction(total * x['periods'], x['periods'])
    def tax(a):
        t = Fraction(0)
        if a > 1000000:
            t += (min(a, 4000000) - 1000000) * Fraction(12, 100)
        if a > 4000000:
            t += (a - 4000000) * Fraction(24, 100)
        return t
    due = tax(annual) * k / x['periods']
    wh = max(0, due - x['ytd_wh'])
    return math.floor(wh + Fraction(1, 2))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'ytd_wages': 7060061, 'ytd_wh': 424656, 'wage': 491569, 'period_index': 24, 'periods': 52}, 1110812), ('regression', {'ytd_wages': 2182615, 'ytd_wh': 379871, 'wage': 414670, 'period_index': 20, 'periods': 26}, 0), ('partial-repair probe', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('partial-repair probe', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('normal control', {'ytd_wages': 185427, 'ytd_wh': 36091, 'wage': 108159, 'period_index': 2, 'periods': 12}, 0), ('normal control', {'ytd_wages': 141603, 'ytd_wh': 11497, 'wage': 15563, 'period_index': 7, 'periods': 12}, 0), ('normal control', {'ytd_wages': 29165, 'ytd_wh': 3348, 'wage': 5167, 'period_index': 8, 'periods': 52}, 0), ('normal control', {'ytd_wages': 2675147, 'ytd_wh': 522064, 'wage': 171429, 'period_index': 26, 'periods': 26}, 0)], [('regression', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('regression', {'ytd_wages': 3504142, 'ytd_wh': 423680, 'wage': 219362, 'period_index': 26, 'periods': 26}, 0), ('partial-repair probe', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('partial-repair probe', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('normal control', {'ytd_wages': 78474, 'ytd_wh': 12616, 'wage': 2681, 'period_index': 35, 'periods': 52}, 0), ('normal control', {'ytd_wages': 780083, 'ytd_wh': 115357, 'wage': 60520, 'period_index': 12, 'periods': 26}, 0), ('normal control', {'ytd_wages': 30504, 'ytd_wh': 1425, 'wage': 14017, 'period_index': 11, 'periods': 12}, 0), ('normal control', {'ytd_wages': 255421, 'ytd_wh': 47741, 'wage': 37513, 'period_index': 11, 'periods': 12}, 0)], [('regression', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('regression', {'ytd_wages': 7060061, 'ytd_wh': 424656, 'wage': 491569, 'period_index': 24, 'periods': 52}, 1110812), ('partial-repair probe', {'ytd_wages': 0, 'ytd_wh': 1, 'wage': 366567, 'period_index': 1, 'periods': 12}, 37975), ('partial-repair probe', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('normal control', {'ytd_wages': 4101816, 'ytd_wh': 781382, 'wage': 93819, 'period_index': 46, 'periods': 52}, 0), ('normal control', {'ytd_wages': 1734664, 'ytd_wh': 216863, 'wage': 75980, 'period_index': 15, 'periods': 26}, 0), ('normal control', {'ytd_wages': 2864664, 'ytd_wh': 419863, 'wage': 94961, 'period_index': 20, 'periods': 26}, 0), ('normal control', {'ytd_wages': 430759, 'ytd_wh': 55675, 'wage': 54560, 'period_index': 5, 'periods': 12}, 0)], [('regression', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('regression', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('partial-repair probe', {'ytd_wages': 2724662, 'ytd_wh': 86701, 'wage': 332622, 'period_index': 7, 'periods': 52}, 566278), ('partial-repair probe', {'ytd_wages': 1665992, 'ytd_wh': 58323, 'wage': 387492, 'period_index': 4, 'periods': 12}, 234513), ('normal control', {'ytd_wages': 353147, 'ytd_wh': 9536, 'wage': 96030, 'period_index': 6, 'periods': 12}, 0), ('normal control', {'ytd_wages': 659564, 'ytd_wh': 7849, 'wage': 18236, 'period_index': 20, 'periods': 26}, 0), ('normal control', {'ytd_wages': 2446975, 'ytd_wh': 466368, 'wage': 202023, 'period_index': 12, 'periods': 12}, 0), ('normal control', {'ytd_wages': 152088, 'ytd_wh': 11592, 'wage': 17915, 'period_index': 8, 'periods': 26}, 0)], [('regression', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('regression', {'ytd_wages': 953768, 'ytd_wh': 32122, 'wage': 67782, 'period_index': 21, 'periods': 26}, 0), ('partial-repair probe', {'ytd_wages': 17382079, 'ytd_wh': 703861, 'wage': 564605, 'period_index': 25, 'periods': 52}, 3314882), ('partial-repair probe', {'ytd_wages': 7572146, 'ytd_wh': 1352041, 'wage': 228907, 'period_index': 18, 'periods': 52}, 312519), ('normal control', {'ytd_wages': 2303411, 'ytd_wh': 406780, 'wage': 112030, 'period_index': 23, 'periods': 26}, 0), ('normal control', {'ytd_wages': 288287, 'ytd_wh': 41455, 'wage': 26146, 'period_index': 26, 'periods': 26}, 0), ('normal control', {'ytd_wages': 102743, 'ytd_wh': 20507, 'wage': 5626, 'period_index': 12, 'periods': 12}, 0), ('normal control', {'ytd_wages': 1160977, 'ytd_wh': 199709, 'wage': 91537, 'period_index': 11, 'periods': 26}, 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 fixture	Actual	Expected	Outcome
regression 0	`134909`	`1110812`	Failed
regression 1	`0`	`0`	Passed
partial-repair probe 2	`1310987`	`1684840`	Failed
partial-repair probe 3	`0`	`15666`	Failed
normal control 4	`0`	`0`	Passed
normal control 5	`0`	`0`	Passed
normal control 6	`0`	`0`	Passed
normal control 7	`0`	`0`	Passed

SHA-256 / 24b976c9fb617c992b292736ea31387dba640aaff4acb09c828b7c0a3d3ceb0e

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
import math
N = 1
observations = []
def solve(x):
    k = x['period_index']
    total = x['ytd_wages'] + x['wage']
    annual = Fraction(total * x['periods'], k)
    def tax(a):
        t = Fraction(0)
        if a > 1000000:
            t += (min(a, 4000000) - 1000000) * Fraction(12, 100)
        if a > 4000000:
            t += (a - 4000000) * Fraction(24, 100)
        return t
    due = tax(annual) * k / x['periods']
    wh = max(0, due - x['ytd_wh'])
    return math.floor(wh + Fraction(1, 2))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'ytd_wages': 7060061, 'ytd_wh': 424656, 'wage': 491569, 'period_index': 24, 'periods': 52}, 1110812), ('regression', {'ytd_wages': 2182615, 'ytd_wh': 379871, 'wage': 414670, 'period_index': 20, 'periods': 26}, 0), ('partial-repair probe', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('partial-repair probe', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('normal control', {'ytd_wages': 185427, 'ytd_wh': 36091, 'wage': 108159, 'period_index': 2, 'periods': 12}, 0), ('normal control', {'ytd_wages': 141603, 'ytd_wh': 11497, 'wage': 15563, 'period_index': 7, 'periods': 12}, 0), ('normal control', {'ytd_wages': 29165, 'ytd_wh': 3348, 'wage': 5167, 'period_index': 8, 'periods': 52}, 0), ('normal control', {'ytd_wages': 2675147, 'ytd_wh': 522064, 'wage': 171429, 'period_index': 26, 'periods': 26}, 0)], [('regression', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('regression', {'ytd_wages': 3504142, 'ytd_wh': 423680, 'wage': 219362, 'period_index': 26, 'periods': 26}, 0), ('partial-repair probe', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('partial-repair probe', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('normal control', {'ytd_wages': 78474, 'ytd_wh': 12616, 'wage': 2681, 'period_index': 35, 'periods': 52}, 0), ('normal control', {'ytd_wages': 780083, 'ytd_wh': 115357, 'wage': 60520, 'period_index': 12, 'periods': 26}, 0), ('normal control', {'ytd_wages': 30504, 'ytd_wh': 1425, 'wage': 14017, 'period_index': 11, 'periods': 12}, 0), ('normal control', {'ytd_wages': 255421, 'ytd_wh': 47741, 'wage': 37513, 'period_index': 11, 'periods': 12}, 0)], [('regression', {'ytd_wages': 8775450, 'ytd_wh': 1616919, 'wage': 334681, 'period_index': 24, 'periods': 26}, 15666), ('regression', {'ytd_wages': 7060061, 'ytd_wh': 424656, 'wage': 491569, 'period_index': 24, 'periods': 52}, 1110812), ('partial-repair probe', {'ytd_wages': 0, 'ytd_wh': 1, 'wage': 366567, 'period_index': 1, 'periods': 12}, 37975), ('partial-repair probe', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('normal control', {'ytd_wages': 4101816, 'ytd_wh': 781382, 'wage': 93819, 'period_index': 46, 'periods': 52}, 0), ('normal control', {'ytd_wages': 1734664, 'ytd_wh': 216863, 'wage': 75980, 'period_index': 15, 'periods': 26}, 0), ('normal control', {'ytd_wages': 2864664, 'ytd_wh': 419863, 'wage': 94961, 'period_index': 20, 'periods': 26}, 0), ('normal control', {'ytd_wages': 430759, 'ytd_wh': 55675, 'wage': 54560, 'period_index': 5, 'periods': 12}, 0)], [('regression', {'ytd_wages': 6145936, 'ytd_wh': 219937, 'wage': 315568, 'period_index': 15, 'periods': 52}, 1157747), ('regression', {'ytd_wages': 9590400, 'ytd_wh': 237513, 'wage': 534789, 'period_index': 22, 'periods': 26}, 1684840), ('partial-repair probe', {'ytd_wages': 2724662, 'ytd_wh': 86701, 'wage': 332622, 'period_index': 7, 'periods': 52}, 566278), ('partial-repair probe', {'ytd_wages': 1665992, 'ytd_wh': 58323, 'wage': 387492, 'period_index': 4, 'periods': 12}, 234513), ('normal control', {'ytd_wages': 353147, 'ytd_wh': 9536, 'wage': 96030, 'period_index': 6, 'periods': 12}, 0), ('normal control', {'ytd_wages': 659564, 'ytd_wh': 7849, 'wage': 18236, 'period_index': 20, 'periods': 26}, 0), ('normal control', {'ytd_wages': 2446975, 'ytd_wh': 466368, 'wage': 202023, 'period_index': 12, 'periods': 12}, 0), ('normal control', {'ytd_wages': 152088, 'ytd_wh': 11592, 'wage': 17915, 'period_index': 8, 'periods': 26}, 0)], [('regression', {'ytd_wages': 4585857, 'ytd_wh': 265867, 'wage': 360821, 'period_index': 12, 'periods': 26}, 644413), ('regression', {'ytd_wages': 953768, 'ytd_wh': 32122, 'wage': 67782, 'period_index': 21, 'periods': 26}, 0), ('partial-repair probe', {'ytd_wages': 17382079, 'ytd_wh': 703861, 'wage': 564605, 'period_index': 25, 'periods': 52}, 3314882), ('partial-repair probe', {'ytd_wages': 7572146, 'ytd_wh': 1352041, 'wage': 228907, 'period_index': 18, 'periods': 52}, 312519), ('normal control', {'ytd_wages': 2303411, 'ytd_wh': 406780, 'wage': 112030, 'period_index': 23, 'periods': 26}, 0), ('normal control', {'ytd_wages': 288287, 'ytd_wh': 41455, 'wage': 26146, 'period_index': 26, 'periods': 26}, 0), ('normal control', {'ytd_wages': 102743, 'ytd_wh': 20507, 'wage': 5626, 'period_index': 12, 'periods': 12}, 0), ('normal control', {'ytd_wages': 1160977, 'ytd_wh': 199709, 'wage': 91537, 'period_index': 11, 'periods': 26}, 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 fixture	Actual	Expected	Outcome
regression 0	`1110812`	`1110812`	Passed
regression 1	`0`	`0`	Passed
partial-repair probe 2	`1684840`	`1684840`	Passed
partial-repair probe 3	`15666`	`15666`	Passed
normal control 4	`0`	`0`	Passed
normal control 5	`0`	`0`	Passed
normal control 6	`0`	`0`	Passed
normal control 7	`0`	`0`	Passed

SHA-256 / d96854553043601d4c7932e5b7206b9ff7b6231d0d6b331bf1c789d65ce7ffb5

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 2026-09-29T14:46:33.814756+00:00.

Case digest / aea40d9a756868f3042e7c1dcb5225076c7b5060d059149636a9ed109020395c