FA-62726 / Tax bracket computation / Open access

Unearned income above the first deduction tier is taxed at the parent's rate · case 01

Unearned income between 1300 and 2600 is taxed at the parent's rate instead of the child's.

Verified by executionVariant 1 · 6 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Net unearned income is measured over 1300 instead of 2600.

VERIFIED REPAIR

Only unearned income above 2600 is net unearned income.

Unsuccessful approach: Updating the threshold to a stale prior-year figure of 2500 still pulls 100 of unearned income into the parent's rate.

Case contract

solve(earned, unearned, parent_rate): stipulated child unearned-income rule, whole dollars. Standard deduction = min(14600, max(1300, earned + 450)); taxable = max(0, earned + unearned - std). Net unearned income = max(0, unearned - 2600); the part of taxable income taxed at the parent's rate (percent string) is min(net unearned, taxable); the rest is taxed at 10%. Return integer cents half-up.

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(earned, unearned, parent_rate):
    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))
    
    std = min(14600, max(1300, earned + 450))
    taxable = max(0, earned + unearned - std)
    net_unearned = max(0, unearned - 1300)
    at_parent = min(net_unearned, taxable)
    tax = at_parent * Fraction(parent_rate) / 100 + (taxable - at_parent) * Fraction(10, 100)
    return cents(tax)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('regression parent-rate-threshold 1', (0, 5000, '32'), 89800),
  ('regression parent-rate-threshold 2', (20000, 5000, '24'), 137600),
  ('control: below threshold', (0, 2000, '32'), 7000), ('control: mixed small', (3000, 3000, '35'), 35500),
  ('control: large trust income', (0, 60000, '37'), 2136800), ('control: earned only', (8000, 0, '22'), 0)],
 [('regression parent-rate-threshold 1', (0, 2000, '32'), 7000),
  ('regression parent-rate-threshold 2', (3000, 3000, '35'), 35500),
  ('partial repair guard 2', (0, 60000, '37'), 2136800), ('control: earned only', (8000, 0, '22'), 0),
  ('control: unearned only', (0, 5000, '32'), 89800), ('control: working teen', (20000, 5000, '24'), 137600)],
 [('regression parent-rate-threshold 1', (0, 60000, '37'), 2136800),
  ('regression parent-rate-threshold 2', (0, 2600, '22'), 13000), ('partial repair guard 2', (8000, 5662, '22'), 88864),
  ('control: unearned only', (0, 5000, '32'), 89800), ('control: below threshold', (0, 2000, '32'), 7000),
  ('control: working teen', (20000, 5000, '24'), 137600), ('control: mixed small', (3000, 3000, '35'), 35500)],
 [('regression parent-rate-threshold 1', (8000, 5662, '22'), 88864),
  ('regression parent-rate-threshold 2', (1000, 60000, '22'), 1284300),
  ('partial repair guard 2', (3000, 5000, '32'), 98300), ('control: mixed small', (3000, 3000, '35'), 35500),
  ('control: large trust income', (0, 60000, '37'), 2136800), ('control: earned only', (8000, 0, '22'), 0),
  ('control: unearned only', (0, 5000, '32'), 89800)],
 [('regression parent-rate-threshold 1', (3000, 5000, '32'), 98300),
  ('regression parent-rate-threshold 2', (8000, 5000, '24'), 79100),
  ('partial repair guard 2', (20000, 5000, '35'), 164000), ('control: unearned only', (0, 5000, '32'), 89800),
  ('control: below threshold', (0, 2000, '32'), 7000), ('control: working teen', (20000, 5000, '24'), 137600),
  ('control: mixed small', (3000, 3000, '35'), 35500)]]
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 fixture	Actual	Expected	Outcome
regression parent-rate-threshold 1	`118400`	`89800`	Failed
regression parent-rate-threshold 2	`155800`	`137600`	Failed
control: below threshold	`22400`	`7000`	Failed
control: mixed small	`68000`	`35500`	Failed
control: large trust income	`2171900`	`2136800`	Failed
control: earned only	`0`	`0`	Passed

SHA-256 / 86aece53923644c4200b8f2345b2add31c44628103498320a87e3b465c4f6393

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(earned, unearned, parent_rate):
    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))
    
    std = min(14600, max(1300, earned + 450))
    taxable = max(0, earned + unearned - std)
    net_unearned = max(0, unearned - 2500)
    at_parent = min(net_unearned, taxable)
    tax = at_parent * Fraction(parent_rate) / 100 + (taxable - at_parent) * Fraction(10, 100)
    return cents(tax)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('regression parent-rate-threshold 1', (0, 5000, '32'), 89800),
  ('regression parent-rate-threshold 2', (20000, 5000, '24'), 137600),
  ('control: below threshold', (0, 2000, '32'), 7000), ('control: mixed small', (3000, 3000, '35'), 35500),
  ('control: large trust income', (0, 60000, '37'), 2136800), ('control: earned only', (8000, 0, '22'), 0)],
 [('regression parent-rate-threshold 1', (0, 2000, '32'), 7000),
  ('regression parent-rate-threshold 2', (3000, 3000, '35'), 35500),
  ('partial repair guard 2', (0, 60000, '37'), 2136800), ('control: earned only', (8000, 0, '22'), 0),
  ('control: unearned only', (0, 5000, '32'), 89800), ('control: working teen', (20000, 5000, '24'), 137600)],
 [('regression parent-rate-threshold 1', (0, 60000, '37'), 2136800),
  ('regression parent-rate-threshold 2', (0, 2600, '22'), 13000), ('partial repair guard 2', (8000, 5662, '22'), 88864),
  ('control: unearned only', (0, 5000, '32'), 89800), ('control: below threshold', (0, 2000, '32'), 7000),
  ('control: working teen', (20000, 5000, '24'), 137600), ('control: mixed small', (3000, 3000, '35'), 35500)],
 [('regression parent-rate-threshold 1', (8000, 5662, '22'), 88864),
  ('regression parent-rate-threshold 2', (1000, 60000, '22'), 1284300),
  ('partial repair guard 2', (3000, 5000, '32'), 98300), ('control: mixed small', (3000, 3000, '35'), 35500),
  ('control: large trust income', (0, 60000, '37'), 2136800), ('control: earned only', (8000, 0, '22'), 0),
  ('control: unearned only', (0, 5000, '32'), 89800)],
 [('regression parent-rate-threshold 1', (3000, 5000, '32'), 98300),
  ('regression parent-rate-threshold 2', (8000, 5000, '24'), 79100),
  ('partial repair guard 2', (20000, 5000, '35'), 164000), ('control: unearned only', (0, 5000, '32'), 89800),
  ('control: below threshold', (0, 2000, '32'), 7000), ('control: working teen', (20000, 5000, '24'), 137600),
  ('control: mixed small', (3000, 3000, '35'), 35500)]]
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 fixture	Actual	Expected	Outcome
regression parent-rate-threshold 1	`92000`	`89800`	Failed
regression parent-rate-threshold 2	`139000`	`137600`	Failed
control: below threshold	`7000`	`7000`	Passed
control: mixed small	`38000`	`35500`	Failed
control: large trust income	`2139500`	`2136800`	Failed
control: earned only	`0`	`0`	Passed

SHA-256 / b1055b3988a3fa20d29b5845f9dd6d2c76b0d22bc6513594d976cb8aaf5a1872

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(earned, unearned, parent_rate):
    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))
    
    std = min(14600, max(1300, earned + 450))
    taxable = max(0, earned + unearned - std)
    net_unearned = max(0, unearned - 2600)
    at_parent = min(net_unearned, taxable)
    tax = at_parent * Fraction(parent_rate) / 100 + (taxable - at_parent) * Fraction(10, 100)
    return cents(tax)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('regression parent-rate-threshold 1', (0, 5000, '32'), 89800),
  ('regression parent-rate-threshold 2', (20000, 5000, '24'), 137600),
  ('control: below threshold', (0, 2000, '32'), 7000), ('control: mixed small', (3000, 3000, '35'), 35500),
  ('control: large trust income', (0, 60000, '37'), 2136800), ('control: earned only', (8000, 0, '22'), 0)],
 [('regression parent-rate-threshold 1', (0, 2000, '32'), 7000),
  ('regression parent-rate-threshold 2', (3000, 3000, '35'), 35500),
  ('partial repair guard 2', (0, 60000, '37'), 2136800), ('control: earned only', (8000, 0, '22'), 0),
  ('control: unearned only', (0, 5000, '32'), 89800), ('control: working teen', (20000, 5000, '24'), 137600)],
 [('regression parent-rate-threshold 1', (0, 60000, '37'), 2136800),
  ('regression parent-rate-threshold 2', (0, 2600, '22'), 13000), ('partial repair guard 2', (8000, 5662, '22'), 88864),
  ('control: unearned only', (0, 5000, '32'), 89800), ('control: below threshold', (0, 2000, '32'), 7000),
  ('control: working teen', (20000, 5000, '24'), 137600), ('control: mixed small', (3000, 3000, '35'), 35500)],
 [('regression parent-rate-threshold 1', (8000, 5662, '22'), 88864),
  ('regression parent-rate-threshold 2', (1000, 60000, '22'), 1284300),
  ('partial repair guard 2', (3000, 5000, '32'), 98300), ('control: mixed small', (3000, 3000, '35'), 35500),
  ('control: large trust income', (0, 60000, '37'), 2136800), ('control: earned only', (8000, 0, '22'), 0),
  ('control: unearned only', (0, 5000, '32'), 89800)],
 [('regression parent-rate-threshold 1', (3000, 5000, '32'), 98300),
  ('regression parent-rate-threshold 2', (8000, 5000, '24'), 79100),
  ('partial repair guard 2', (20000, 5000, '35'), 164000), ('control: unearned only', (0, 5000, '32'), 89800),
  ('control: below threshold', (0, 2000, '32'), 7000), ('control: working teen', (20000, 5000, '24'), 137600),
  ('control: mixed small', (3000, 3000, '35'), 35500)]]
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 fixture	Actual	Expected	Outcome
regression parent-rate-threshold 1	`89800`	`89800`	Passed
regression parent-rate-threshold 2	`137600`	`137600`	Passed
control: below threshold	`7000`	`7000`	Passed
control: mixed small	`35500`	`35500`	Passed
control: large trust income	`2136800`	`2136800`	Passed
control: earned only	`0`	`0`	Passed

SHA-256 / 98e032614e8d9f75d0fc12df5ec7a60feafdcec0dbc7d370b8626a45771f1721

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 2026-09-29T14:47:07.328365+00:00.

Case digest / 62618c2159952be63a8953b0d524912000c2d714b002c7fc0fca369b0b040d26