FAILURE MAP
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FA-58936 / Payroll withholding rules / Open access

Supplemental flat-rate withholding tiers: year-to-date inclusion · case 01

Bonuses approaching the million-dollar mark move to the 37% tier too early.

Verified by executionVariant 1 · 10 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The room below the limit subtracts the current bonus as though year-to-date already included it.

THE FAILURE

The room below the limit subtracts the current bonus as though year-to-date already included it.

Unsuccessful approach: The attempt removes the double count but also the floor, so once past the limit the low tier becomes negative.

Case contract

Input [ytd_supplemental, bonus] in cents. Supplemental wages up to a cumulative 1,000,000.00 are withheld at 22%; the part of cumulative supplemental wages above that is withheld at 37%. Withholding = (low*22 + high*37) rounded half-up to the cent once. Return [low, high, withholding].

Why this case matters

The mandatory 37% tier depends on cumulative supplemental wages and must split the crossing payment.

1 / The failure

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

N = 1
observations = []
def solve(x):
    ytd, bonus = x
    limit = 100000000
    below_room = max(0, limit - ytd - bonus)
    low = min(bonus, below_room)
    high = bonus - low
    wh = (low * 22 + high * 37 + 50) // 100
    return [low, high, wh]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression (boundary)', [99999950, 101], [50, 51, 30]), ('regression (boundary)', [99000000, 1000000], [1000000, 0, 220000]), ('partial-repair probe (boundary)', [150000000, 33], [0, 33, 12]), ('partial-repair probe', [100849097, 11351881], [0, 11351881, 4200196]), ('boundary control', [100000000, 100], [0, 100, 37]), ('boundary control', [0, 0], [0, 0, 0]), ('normal control', [98022676, 574037], [574037, 0, 126288]), ('normal control', [3276820, 34567110], [34567110, 0, 7604764]), ('normal control', [2185641, 20897159], [20897159, 0, 4597375]), ('normal control', [100000000, 6674805], [0, 6674805, 2469678])], [('regression (boundary)', [99000000, 1000000], [1000000, 0, 220000]), ('regression', [99561826, 3108388], [438174, 2670214, 1084377]), ('partial-repair probe', [107387654, 40335762], [0, 40335762, 14924232]), ('partial-repair probe', [105717987, 5743298], [0, 5743298, 2125020]), ('boundary control', [100000000, 100], [0, 100, 37]), ('boundary control', [0, 0], [0, 0, 0]), ('normal control', [49161621, 69310], [69310, 0, 15248]), ('normal control', [66640759, 96623], [96623, 0, 21257]), ('normal control', [100000000, 52968], [0, 52968, 19598]), ('normal control', [100000000, 2071189], [0, 2071189, 766340])], [('regression', [99561826, 3108388], [438174, 2670214, 1084377]), ('regression', [98276481, 26306241], [1723519, 24582722, 9474781]), ('partial-repair probe', [106100649, 5242825], [0, 5242825, 1939845]), ('partial-repair probe', [108022995, 3827223], [0, 3827223, 1416073]), ('boundary control', [100000000, 100], [0, 100, 37]), ('boundary control', [0, 0], [0, 0, 0]), ('normal control', [2545996, 41743464], [41743464, 0, 9183562]), ('normal control', [100000000, 21687395], [0, 21687395, 8024336]), ('normal control', [97846124, 8522], [8522, 0, 1875]), ('normal control', [3045185, 81939], [81939, 0, 18027])], [('regression', [98276481, 26306241], [1723519, 24582722, 9474781]), ('regression', [95783477, 7789713], [4216523, 3573190, 2249715]), ('partial-repair probe', [107848302, 42942181], [0, 42942181, 15888607]), ('partial-repair probe', [100442126, 20427451], [0, 20427451, 7558157]), ('boundary control', [100000000, 100], [0, 100, 37]), ('boundary control', [0, 0], [0, 0, 0]), ('normal control', [374880, 70400], [70400, 0, 15488]), ('normal control', [688889, 1036750], [1036750, 0, 228085]), ('normal control', [63007595, 8068739], [8068739, 0, 1775123]), ('normal control', [64670743, 3021249], [3021249, 0, 664675])], [('regression', [95783477, 7789713], [4216523, 3573190, 2249715]), ('regression', [80430645, 24514483], [19569355, 4945128, 6134955]), ('partial-repair probe', [105107162, 46320149], [0, 46320149, 17138455]), ('partial-repair probe', [107181794, 22743504], [0, 22743504, 8415096]), ('boundary control', [100000000, 100], [0, 100, 37]), ('boundary control', [0, 0], [0, 0, 0]), ('normal control', [97882960, 95576], [95576, 0, 21027]), ('normal control', [100000000, 75200], [0, 75200, 27824]), ('normal control', [100000000, 1622529], [0, 1622529, 600336]), ('normal control', [63981087, 6343691], [6343691, 0, 1395612])]]
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 (boundary) 0[0, 101, 37][50, 51, 30]Failed
regression (boundary) 1[0, 1000000, 370000][1000000, 0, 220000]Failed
partial-repair probe (boundary) 2[0, 33, 12][0, 33, 12]Passed
partial-repair probe 3[0, 11351881, 4200196][0, 11351881, 4200196]Passed
boundary control 4[0, 100, 37][0, 100, 37]Passed
boundary control 5[0, 0, 0][0, 0, 0]Passed
normal control 6[574037, 0, 126288][574037, 0, 126288]Passed
normal control 7[34567110, 0, 7604764][34567110, 0, 7604764]Passed
normal control 8[20897159, 0, 4597375][20897159, 0, 4597375]Passed
normal control 9[0, 6674805, 2469678][0, 6674805, 2469678]Passed

SHA-256 / b5c01ba8837484d9e6f089980878062f91cd8940016e53c59a3e80a932f3f505

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(x):
    ytd, bonus = x
    limit = 100000000
    below_room = limit - ytd
    low = min(bonus, below_room)
    high = bonus - low
    wh = (low * 22 + high * 37 + 50) // 100
    return [low, high, wh]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression (boundary)', [99999950, 101], [50, 51, 30]), ('regression (boundary)', [99000000, 1000000], [1000000, 0, 220000]), ('partial-repair probe (boundary)', [150000000, 33], [0, 33, 12]), ('partial-repair probe', [100849097, 11351881], [0, 11351881, 4200196]), ('boundary control', [100000000, 100], [0, 100, 37]), ('boundary control', [0, 0], [0, 0, 0]), ('normal control', [98022676, 574037], [574037, 0, 126288]), ('normal control', [3276820, 34567110], [34567110, 0, 7604764]), ('normal control', [2185641, 20897159], [20897159, 0, 4597375]), ('normal control', [100000000, 6674805], [0, 6674805, 2469678])], [('regression (boundary)', [99000000, 1000000], [1000000, 0, 220000]), ('regression', [99561826, 3108388], [438174, 2670214, 1084377]), ('partial-repair probe', [107387654, 40335762], [0, 40335762, 14924232]), ('partial-repair probe', [105717987, 5743298], [0, 5743298, 2125020]), ('boundary control', [100000000, 100], [0, 100, 37]), ('boundary control', [0, 0], [0, 0, 0]), ('normal control', [49161621, 69310], [69310, 0, 15248]), ('normal control', [66640759, 96623], [96623, 0, 21257]), ('normal control', [100000000, 52968], [0, 52968, 19598]), ('normal control', [100000000, 2071189], [0, 2071189, 766340])], [('regression', [99561826, 3108388], [438174, 2670214, 1084377]), ('regression', [98276481, 26306241], [1723519, 24582722, 9474781]), ('partial-repair probe', [106100649, 5242825], [0, 5242825, 1939845]), ('partial-repair probe', [108022995, 3827223], [0, 3827223, 1416073]), ('boundary control', [100000000, 100], [0, 100, 37]), ('boundary control', [0, 0], [0, 0, 0]), ('normal control', [2545996, 41743464], [41743464, 0, 9183562]), ('normal control', [100000000, 21687395], [0, 21687395, 8024336]), ('normal control', [97846124, 8522], [8522, 0, 1875]), ('normal control', [3045185, 81939], [81939, 0, 18027])], [('regression', [98276481, 26306241], [1723519, 24582722, 9474781]), ('regression', [95783477, 7789713], [4216523, 3573190, 2249715]), ('partial-repair probe', [107848302, 42942181], [0, 42942181, 15888607]), ('partial-repair probe', [100442126, 20427451], [0, 20427451, 7558157]), ('boundary control', [100000000, 100], [0, 100, 37]), ('boundary control', [0, 0], [0, 0, 0]), ('normal control', [374880, 70400], [70400, 0, 15488]), ('normal control', [688889, 1036750], [1036750, 0, 228085]), ('normal control', [63007595, 8068739], [8068739, 0, 1775123]), ('normal control', [64670743, 3021249], [3021249, 0, 664675])], [('regression', [95783477, 7789713], [4216523, 3573190, 2249715]), ('regression', [80430645, 24514483], [19569355, 4945128, 6134955]), ('partial-repair probe', [105107162, 46320149], [0, 46320149, 17138455]), ('partial-repair probe', [107181794, 22743504], [0, 22743504, 8415096]), ('boundary control', [100000000, 100], [0, 100, 37]), ('boundary control', [0, 0], [0, 0, 0]), ('normal control', [97882960, 95576], [95576, 0, 21027]), ('normal control', [100000000, 75200], [0, 75200, 27824]), ('normal control', [100000000, 1622529], [0, 1622529, 600336]), ('normal control', [63981087, 6343691], [6343691, 0, 1395612])]]
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 (boundary) 0[50, 51, 30][50, 51, 30]Passed
regression (boundary) 1[1000000, 0, 220000][1000000, 0, 220000]Passed
partial-repair probe (boundary) 2[-50000000, 50000033, 7500012][0, 33, 12]Failed
partial-repair probe 3[-849097, 12200978, 4327561][0, 11351881, 4200196]Failed
boundary control 4[0, 100, 37][0, 100, 37]Passed
boundary control 5[0, 0, 0][0, 0, 0]Passed
normal control 6[574037, 0, 126288][574037, 0, 126288]Passed
normal control 7[34567110, 0, 7604764][34567110, 0, 7604764]Passed
normal control 8[20897159, 0, 4597375][20897159, 0, 4597375]Passed
normal control 9[0, 6674805, 2469678][0, 6674805, 2469678]Passed

SHA-256 / eff41a2fc3bddc87a65b81fd80f622a1e6070b46888517d851426416ac07a7b2

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 10 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

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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 2026-09-29T14:46:31.466966+00:00.

Case digest / 3e05f26a3b382a2b31684c82014099a7b1730188f938b0570e9a71cf56422c80