FAILURE MAP
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FA-62676 / Tax bracket computation / Open access

The deceased spouse's unused exclusion is ignored · case 01

A surviving spouse's estate pays tax as if no exclusion had been ported.

Verified by executionVariant 1 · 7 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The exclusion omits dsue.

VERIFIED REPAIR

Add dsue to the exclusion before computing the credit.

Unsuccessful approach: Adding dsue dollars directly to the credit treats the ported exclusion as tax.

Case contract

solve(estate, gifts, dsue): stipulated unified transfer tax in whole dollars. T is the progressive schedule 18% to 10000 rising to 40% above 1000000. The exclusion is 13610000 + dsue; the unified credit is T(exclusion). Tentative tax = T(estate + gifts). Gift credit = max(0, T(gifts) - unified credit). Tax = max(0, tentative - gift credit - unified credit), returned in integer cents.

Why this case matters

Tax computations hinge on which slice, threshold, ordering and rounding rule applies at each step; a misplaced boundary silently misstates liabilities.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(estate, gifts, dsue):
    def prog(x, br):
        tax, lower = Fraction(0), 0
        for upper, rate in br:
            top = x if upper is None else min(x, upper)
            if top > lower: tax += (top - lower) * Fraction(rate) / 100
            if upper is None or x <= upper: break
            lower = upper
        return tax
    def cents(v):
        v = v * 100
        return int(v + Fraction(1, 2)) if v >= 0 else -int(-v + Fraction(1, 2))
    
    sched = [[10000, '18'], [20000, '20'], [40000, '22'], [60000, '24'], [80000, '26'], [100000, '28'], [150000, '30'], [250000, '32'], [500000, '34'], [750000, '37'], [1000000, '39'], [None, '40']]
    T = lambda x: prog(x, sched)
    credit = T(13610000)
    tent = T(estate + gifts)
    gift_credit = max(0, T(gifts) - credit)
    return cents(max(0, tent - gift_credit - credit))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('regression ported-exclusion 1', (20000000, 0, 5000000), 55600000),
  ('regression ported-exclusion 2', (867406, 15000000, 5000000), 0),
  ('partial repair guard 2', (13610000, 5000000, 2000000), 120000000),
  ('control: large estate', (20000000, 0, 0), 255600000), ('control: under exclusion', (10000000, 0, 0), 0),
  ('control: with prior gifts', (10000000, 5000000, 0), 55600000),
  ('control: gifts above exclusion', (5000000, 15000000, 0), 200000000)],
 [('regression ported-exclusion 1', (13610000, 5000000, 2000000), 120000000),
  ('regression ported-exclusion 2', (27783445, 500000, 5000000), 386937800),
  ('partial repair guard 2', (22327855, 5000000, 2000000), 468714200),
  ('control: ported exclusion', (20000000, 0, 5000000), 55600000),
  ('control: gifts above exclusion', (5000000, 15000000, 0), 200000000), ('control: modest', (1500000, 200000, 0), 0),
  ('control: large estate', (20000000, 0, 0), 255600000)],
 [('regression ported-exclusion 1', (22327855, 5000000, 2000000), 468714200),
  ('regression ported-exclusion 2', (13610000, 5000000, 5000000), 0),
  ('partial repair guard 1', (24200909, 15000000, 2000000), 943636360),
  ('partial repair guard 2', (28075997, 5000000, 5000000), 578639880),
  ('control: large estate', (20000000, 0, 0), 255600000), ('control: under exclusion', (10000000, 0, 0), 0),
  ('control: with prior gifts', (10000000, 5000000, 0), 55600000),
  ('control: ported exclusion', (20000000, 0, 5000000), 55600000)],
 [('regression ported-exclusion 1', (24200909, 15000000, 2000000), 943636360),
  ('regression ported-exclusion 2', (28075997, 5000000, 5000000), 578639880),
  ('partial repair guard 1', (21824496, 5000000, 5000000), 328579840),
  ('partial repair guard 2', (19264731, 0, 2000000), 146189240),
  ('control: ported exclusion', (20000000, 0, 5000000), 55600000),
  ('control: gifts above exclusion', (5000000, 15000000, 0), 200000000), ('control: modest', (1500000, 200000, 0), 0),
  ('control: large estate', (20000000, 0, 0), 255600000)],
 [('regression ported-exclusion 1', (21824496, 5000000, 5000000), 328579840),
  ('regression ported-exclusion 2', (19264731, 0, 2000000), 146189240),
  ('partial repair guard 1', (29904698, 15000000, 2000000), 1171787920),
  ('partial repair guard 2', (9658783, 13906347, 2000000), 318205200),
  ('control: large estate', (20000000, 0, 0), 255600000), ('control: under exclusion', (10000000, 0, 0), 0),
  ('control: with prior gifts', (10000000, 5000000, 0), 55600000),
  ('control: ported exclusion', (20000000, 0, 5000000), 55600000)]]
for label, args, expected in cases[N - 1]:
    check(label, 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 ported-exclusion 125560000055600000Failed
regression ported-exclusion 2346962400Failed
partial repair guard 2200000000120000000Failed
control: large estate255600000255600000Passed
control: under exclusion00Passed
control: with prior gifts5560000055600000Passed
control: gifts above exclusion200000000200000000Passed

SHA-256 / 5beeb384cf27b23ce9f0a429787d5d089a41f8f4af5d191d5d5a2b2fa1c73cc2

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(estate, gifts, dsue):
    def prog(x, br):
        tax, lower = Fraction(0), 0
        for upper, rate in br:
            top = x if upper is None else min(x, upper)
            if top > lower: tax += (top - lower) * Fraction(rate) / 100
            if upper is None or x <= upper: break
            lower = upper
        return tax
    def cents(v):
        v = v * 100
        return int(v + Fraction(1, 2)) if v >= 0 else -int(-v + Fraction(1, 2))
    
    sched = [[10000, '18'], [20000, '20'], [40000, '22'], [60000, '24'], [80000, '26'], [100000, '28'], [150000, '30'], [250000, '32'], [500000, '34'], [750000, '37'], [1000000, '39'], [None, '40']]
    T = lambda x: prog(x, sched)
    credit = T(13610000) + dsue
    tent = T(estate + gifts)
    gift_credit = max(0, T(gifts) - credit)
    return cents(max(0, tent - gift_credit - credit))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('regression ported-exclusion 1', (20000000, 0, 5000000), 55600000),
  ('regression ported-exclusion 2', (867406, 15000000, 5000000), 0),
  ('partial repair guard 2', (13610000, 5000000, 2000000), 120000000),
  ('control: large estate', (20000000, 0, 0), 255600000), ('control: under exclusion', (10000000, 0, 0), 0),
  ('control: with prior gifts', (10000000, 5000000, 0), 55600000),
  ('control: gifts above exclusion', (5000000, 15000000, 0), 200000000)],
 [('regression ported-exclusion 1', (13610000, 5000000, 2000000), 120000000),
  ('regression ported-exclusion 2', (27783445, 500000, 5000000), 386937800),
  ('partial repair guard 2', (22327855, 5000000, 2000000), 468714200),
  ('control: ported exclusion', (20000000, 0, 5000000), 55600000),
  ('control: gifts above exclusion', (5000000, 15000000, 0), 200000000), ('control: modest', (1500000, 200000, 0), 0),
  ('control: large estate', (20000000, 0, 0), 255600000)],
 [('regression ported-exclusion 1', (22327855, 5000000, 2000000), 468714200),
  ('regression ported-exclusion 2', (13610000, 5000000, 5000000), 0),
  ('partial repair guard 1', (24200909, 15000000, 2000000), 943636360),
  ('partial repair guard 2', (28075997, 5000000, 5000000), 578639880),
  ('control: large estate', (20000000, 0, 0), 255600000), ('control: under exclusion', (10000000, 0, 0), 0),
  ('control: with prior gifts', (10000000, 5000000, 0), 55600000),
  ('control: ported exclusion', (20000000, 0, 5000000), 55600000)],
 [('regression ported-exclusion 1', (24200909, 15000000, 2000000), 943636360),
  ('regression ported-exclusion 2', (28075997, 5000000, 5000000), 578639880),
  ('partial repair guard 1', (21824496, 5000000, 5000000), 328579840),
  ('partial repair guard 2', (19264731, 0, 2000000), 146189240),
  ('control: ported exclusion', (20000000, 0, 5000000), 55600000),
  ('control: gifts above exclusion', (5000000, 15000000, 0), 200000000), ('control: modest', (1500000, 200000, 0), 0),
  ('control: large estate', (20000000, 0, 0), 255600000)],
 [('regression ported-exclusion 1', (21824496, 5000000, 5000000), 328579840),
  ('regression ported-exclusion 2', (19264731, 0, 2000000), 146189240),
  ('partial repair guard 1', (29904698, 15000000, 2000000), 1171787920),
  ('partial repair guard 2', (9658783, 13906347, 2000000), 318205200),
  ('control: large estate', (20000000, 0, 0), 255600000), ('control: under exclusion', (10000000, 0, 0), 0),
  ('control: with prior gifts', (10000000, 5000000, 0), 55600000),
  ('control: ported exclusion', (20000000, 0, 5000000), 55600000)]]
for label, args, expected in cases[N - 1]:
    check(label, 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 ported-exclusion 1055600000Failed
regression ported-exclusion 200Passed
partial repair guard 20120000000Failed
control: large estate255600000255600000Passed
control: under exclusion00Passed
control: with prior gifts5560000055600000Passed
control: gifts above exclusion200000000200000000Passed

SHA-256 / 077c1d1ec3b4f7bf0f52ff9f35eb31dd30d6b58df23e71163743cc9b349143c3

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(estate, gifts, dsue):
    def prog(x, br):
        tax, lower = Fraction(0), 0
        for upper, rate in br:
            top = x if upper is None else min(x, upper)
            if top > lower: tax += (top - lower) * Fraction(rate) / 100
            if upper is None or x <= upper: break
            lower = upper
        return tax
    def cents(v):
        v = v * 100
        return int(v + Fraction(1, 2)) if v >= 0 else -int(-v + Fraction(1, 2))
    
    sched = [[10000, '18'], [20000, '20'], [40000, '22'], [60000, '24'], [80000, '26'], [100000, '28'], [150000, '30'], [250000, '32'], [500000, '34'], [750000, '37'], [1000000, '39'], [None, '40']]
    T = lambda x: prog(x, sched)
    credit = T(13610000 + dsue)
    tent = T(estate + gifts)
    gift_credit = max(0, T(gifts) - credit)
    return cents(max(0, tent - gift_credit - credit))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('regression ported-exclusion 1', (20000000, 0, 5000000), 55600000),
  ('regression ported-exclusion 2', (867406, 15000000, 5000000), 0),
  ('partial repair guard 2', (13610000, 5000000, 2000000), 120000000),
  ('control: large estate', (20000000, 0, 0), 255600000), ('control: under exclusion', (10000000, 0, 0), 0),
  ('control: with prior gifts', (10000000, 5000000, 0), 55600000),
  ('control: gifts above exclusion', (5000000, 15000000, 0), 200000000)],
 [('regression ported-exclusion 1', (13610000, 5000000, 2000000), 120000000),
  ('regression ported-exclusion 2', (27783445, 500000, 5000000), 386937800),
  ('partial repair guard 2', (22327855, 5000000, 2000000), 468714200),
  ('control: ported exclusion', (20000000, 0, 5000000), 55600000),
  ('control: gifts above exclusion', (5000000, 15000000, 0), 200000000), ('control: modest', (1500000, 200000, 0), 0),
  ('control: large estate', (20000000, 0, 0), 255600000)],
 [('regression ported-exclusion 1', (22327855, 5000000, 2000000), 468714200),
  ('regression ported-exclusion 2', (13610000, 5000000, 5000000), 0),
  ('partial repair guard 1', (24200909, 15000000, 2000000), 943636360),
  ('partial repair guard 2', (28075997, 5000000, 5000000), 578639880),
  ('control: large estate', (20000000, 0, 0), 255600000), ('control: under exclusion', (10000000, 0, 0), 0),
  ('control: with prior gifts', (10000000, 5000000, 0), 55600000),
  ('control: ported exclusion', (20000000, 0, 5000000), 55600000)],
 [('regression ported-exclusion 1', (24200909, 15000000, 2000000), 943636360),
  ('regression ported-exclusion 2', (28075997, 5000000, 5000000), 578639880),
  ('partial repair guard 1', (21824496, 5000000, 5000000), 328579840),
  ('partial repair guard 2', (19264731, 0, 2000000), 146189240),
  ('control: ported exclusion', (20000000, 0, 5000000), 55600000),
  ('control: gifts above exclusion', (5000000, 15000000, 0), 200000000), ('control: modest', (1500000, 200000, 0), 0),
  ('control: large estate', (20000000, 0, 0), 255600000)],
 [('regression ported-exclusion 1', (21824496, 5000000, 5000000), 328579840),
  ('regression ported-exclusion 2', (19264731, 0, 2000000), 146189240),
  ('partial repair guard 1', (29904698, 15000000, 2000000), 1171787920),
  ('partial repair guard 2', (9658783, 13906347, 2000000), 318205200),
  ('control: large estate', (20000000, 0, 0), 255600000), ('control: under exclusion', (10000000, 0, 0), 0),
  ('control: with prior gifts', (10000000, 5000000, 0), 55600000),
  ('control: ported exclusion', (20000000, 0, 5000000), 55600000)]]
for label, args, expected in cases[N - 1]:
    check(label, 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 ported-exclusion 15560000055600000Passed
regression ported-exclusion 200Passed
partial repair guard 2120000000120000000Passed
control: large estate255600000255600000Passed
control: under exclusion00Passed
control: with prior gifts5560000055600000Passed
control: gifts above exclusion200000000200000000Passed

SHA-256 / 802ec4448976d534fb92df3895bee07388c491a29d965a97108aeb5c1f27857e

Verification & scope

A deterministic, bounded teaching model with a stipulated toy contract; it makes no claim of conformance to any real regulation, standard, or institution's rules. 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 2026-09-29T14:47:06.860061+00:00.

Case digest / 758edc25b4955cb3429a71c3a601fdb55c774bedc0ceada9e22f0d176f1bcc8f