FA-58896 / Payroll withholding rules / Open access
Annualized percentage-method withholding: de-annualization rounding stage · case 01
Per-period withholding drifts by several cents from the exact annualized result.
ROOT CAUSE
The annual tax is rounded to whole dollars before dividing by periods, rounding at the wrong stage.
VERIFIED REPAIR
Restore the contract rule at the de-annualization rounding stage step: use `wh = (tax * 2 + 100 * periods) // (200 * periods)`.
Unsuccessful approach: The attempt drops the intermediate truncation but then truncates the per-period result instead of rounding half-up.
Case contract
Input {wage, freq, allow, status}. Annualize: wage*periods (weekly 52, biweekly 26, semimonthly 24, monthly 12); subtract 4,300.00 per allowance, floor 0. Marginal brackets over 4,000/15,000/50,000 dollars at 10/12/22% (married thresholds doubled). Annual tax (in cent-percent units) is divided by periods and rounded half-up to cents once.
Why this case matters
Percentage-method withholding depends on marginal bracket slices and on rounding only after de-annualizing.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
periods = {'weekly': 52, 'biweekly': 26, 'semimonthly': 24, 'monthly': 12}[x['freq']]
annual = x['wage'] * periods
adjusted = max(0, annual - x['allow'] * 430000)
mult = 2 if x['status'] == 'married' else 1
brackets = [(400000 * mult, 10), (1500000 * mult, 12), (5000000 * mult, 22)]
tax = 0
for i, (lo, rate) in enumerate(brackets):
hi = brackets[i + 1][0] if i + 1 < len(brackets) else None
if adjusted > lo:
top = adjusted if hi is None else min(adjusted, hi)
tax += (top - lo) * rate
wh = ((tax + 5000) // 10000 * 200 + periods) // (2 * periods)
return wh
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'wage': 998152, 'freq': 'semimonthly', 'allow': 1, 'status': 'married'}, 168152), ('regression', {'wage': 36754, 'freq': 'biweekly', 'allow': 1, 'status': 'single'}, 483), ('partial-repair probe', {'wage': 992194, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 190902), ('partial-repair probe', {'wage': 124657, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 1966), ('boundary control', {'wage': 0, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 0), ('boundary control', {'wage': 57693, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 5577), ('normal control', {'wage': 22359, 'freq': 'semimonthly', 'allow': 5, 'status': 'married'}, 0), ('normal control', {'wage': 89742, 'freq': 'monthly', 'allow': 1, 'status': 'married'}, 0), ('normal control', {'wage': 17607, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 0), ('normal control', {'wage': 6134, 'freq': 'semimonthly', 'allow': 0, 'status': 'single'}, 0)], [('regression', {'wage': 124657, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 1966), ('regression', {'wage': 36754, 'freq': 'biweekly', 'allow': 1, 'status': 'single'}, 483), ('partial-repair probe', {'wage': 157996, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 5300), ('partial-repair probe', {'wage': 1223812, 'freq': 'monthly', 'allow': 5, 'status': 'married'}, 134822), ('boundary control', {'wage': 125000, 'freq': 'semimonthly', 'allow': 1, 'status': 'married'}, 7375), ('boundary control', {'wage': 0, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 0), ('normal control', {'wage': 52312, 'freq': 'semimonthly', 'allow': 5, 'status': 'single'}, 0), ('normal control', {'wage': 28087, 'freq': 'biweekly', 'allow': 1, 'status': 'single'}, 0), ('normal control', {'wage': 97334, 'freq': 'monthly', 'allow': 6, 'status': 'single'}, 0), ('normal control', {'wage': 18119, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 0)], [('regression', {'wage': 1223812, 'freq': 'monthly', 'allow': 5, 'status': 'married'}, 134822), ('regression', {'wage': 124657, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 1966), ('partial-repair probe', {'wage': 475908, 'freq': 'monthly', 'allow': 6, 'status': 'single'}, 25476), ('partial-repair probe', {'wage': 872994, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 164678), ('boundary control', {'wage': 57693, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 5577), ('boundary control', {'wage': 125000, 'freq': 'semimonthly', 'allow': 1, 'status': 'married'}, 7375), ('normal control', {'wage': 10871, 'freq': 'weekly', 'allow': 6, 'status': 'single'}, 0), ('normal control', {'wage': 836, 'freq': 'semimonthly', 'allow': 1, 'status': 'single'}, 0), ('normal control', {'wage': 30453, 'freq': 'biweekly', 'allow': 2, 'status': 'married'}, 0), ('normal control', {'wage': 27188, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 0)], [('regression', {'wage': 475908, 'freq': 'monthly', 'allow': 6, 'status': 'single'}, 25476), ('regression', {'wage': 1223812, 'freq': 'monthly', 'allow': 5, 'status': 'married'}, 134822), ('partial-repair probe', {'wage': 872994, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 164678), ('partial-repair probe', {'wage': 166201, 'freq': 'weekly', 'allow': 0, 'status': 'married'}, 17252), ('boundary control', {'wage': 0, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 0), ('boundary control', {'wage': 57693, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 5577), ('normal control', {'wage': 23859, 'freq': 'semimonthly', 'allow': 2, 'status': 'married'}, 0), ('normal control', {'wage': 50426, 'freq': 'semimonthly', 'allow': 5, 'status': 'married'}, 0), ('normal control', {'wage': 128102, 'freq': 'monthly', 'allow': 5, 'status': 'married'}, 0), ('normal control', {'wage': 6608, 'freq': 'semimonthly', 'allow': 0, 'status': 'married'}, 0)], [('regression', {'wage': 872994, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 164678), ('regression', {'wage': 143245, 'freq': 'biweekly', 'allow': 5, 'status': 'married'}, 2978), ('partial-repair probe', {'wage': 1203384, 'freq': 'biweekly', 'allow': 6, 'status': 'single'}, 220991), ('partial-repair probe', {'wage': 1136572, 'freq': 'weekly', 'allow': 1, 'status': 'married'}, 226304), ('boundary control', {'wage': 125000, 'freq': 'semimonthly', 'allow': 1, 'status': 'married'}, 7375), ('boundary control', {'wage': 0, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 0), ('normal control', {'wage': 49359, 'freq': 'weekly', 'allow': 6, 'status': 'single'}, 0), ('normal control', {'wage': 56499, 'freq': 'monthly', 'allow': 4, 'status': 'married'}, 0), ('normal control', {'wage': 6762, 'freq': 'weekly', 'allow': 6, 'status': 'single'}, 0), ('normal control', {'wage': 30428, 'freq': 'monthly', 'allow': 4, 'status': 'married'}, 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 | 168150 | 168152 | Failed |
| regression 1 | 485 | 483 | Failed |
| partial-repair probe 2 | 190902 | 190902 | Passed |
| partial-repair probe 3 | 1967 | 1966 | Failed |
| boundary control 4 | 0 | 0 | Passed |
| boundary control 5 | 5577 | 5577 | Passed |
| normal control 6 | 0 | 0 | Passed |
| normal control 7 | 0 | 0 | Passed |
| normal control 8 | 0 | 0 | Passed |
| normal control 9 | 0 | 0 | Passed |
SHA-256 / 90e8810deda46a98a4a3288ab640acd398f7a8b4866faa2ef710f7a4140d552d
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
periods = {'weekly': 52, 'biweekly': 26, 'semimonthly': 24, 'monthly': 12}[x['freq']]
annual = x['wage'] * periods
adjusted = max(0, annual - x['allow'] * 430000)
mult = 2 if x['status'] == 'married' else 1
brackets = [(400000 * mult, 10), (1500000 * mult, 12), (5000000 * mult, 22)]
tax = 0
for i, (lo, rate) in enumerate(brackets):
hi = brackets[i + 1][0] if i + 1 < len(brackets) else None
if adjusted > lo:
top = adjusted if hi is None else min(adjusted, hi)
tax += (top - lo) * rate
wh = tax // (100 * periods)
return wh
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'wage': 998152, 'freq': 'semimonthly', 'allow': 1, 'status': 'married'}, 168152), ('regression', {'wage': 36754, 'freq': 'biweekly', 'allow': 1, 'status': 'single'}, 483), ('partial-repair probe', {'wage': 992194, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 190902), ('partial-repair probe', {'wage': 124657, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 1966), ('boundary control', {'wage': 0, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 0), ('boundary control', {'wage': 57693, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 5577), ('normal control', {'wage': 22359, 'freq': 'semimonthly', 'allow': 5, 'status': 'married'}, 0), ('normal control', {'wage': 89742, 'freq': 'monthly', 'allow': 1, 'status': 'married'}, 0), ('normal control', {'wage': 17607, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 0), ('normal control', {'wage': 6134, 'freq': 'semimonthly', 'allow': 0, 'status': 'single'}, 0)], [('regression', {'wage': 124657, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 1966), ('regression', {'wage': 36754, 'freq': 'biweekly', 'allow': 1, 'status': 'single'}, 483), ('partial-repair probe', {'wage': 157996, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 5300), ('partial-repair probe', {'wage': 1223812, 'freq': 'monthly', 'allow': 5, 'status': 'married'}, 134822), ('boundary control', {'wage': 125000, 'freq': 'semimonthly', 'allow': 1, 'status': 'married'}, 7375), ('boundary control', {'wage': 0, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 0), ('normal control', {'wage': 52312, 'freq': 'semimonthly', 'allow': 5, 'status': 'single'}, 0), ('normal control', {'wage': 28087, 'freq': 'biweekly', 'allow': 1, 'status': 'single'}, 0), ('normal control', {'wage': 97334, 'freq': 'monthly', 'allow': 6, 'status': 'single'}, 0), ('normal control', {'wage': 18119, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 0)], [('regression', {'wage': 1223812, 'freq': 'monthly', 'allow': 5, 'status': 'married'}, 134822), ('regression', {'wage': 124657, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 1966), ('partial-repair probe', {'wage': 475908, 'freq': 'monthly', 'allow': 6, 'status': 'single'}, 25476), ('partial-repair probe', {'wage': 872994, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 164678), ('boundary control', {'wage': 57693, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 5577), ('boundary control', {'wage': 125000, 'freq': 'semimonthly', 'allow': 1, 'status': 'married'}, 7375), ('normal control', {'wage': 10871, 'freq': 'weekly', 'allow': 6, 'status': 'single'}, 0), ('normal control', {'wage': 836, 'freq': 'semimonthly', 'allow': 1, 'status': 'single'}, 0), ('normal control', {'wage': 30453, 'freq': 'biweekly', 'allow': 2, 'status': 'married'}, 0), ('normal control', {'wage': 27188, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 0)], [('regression', {'wage': 475908, 'freq': 'monthly', 'allow': 6, 'status': 'single'}, 25476), ('regression', {'wage': 1223812, 'freq': 'monthly', 'allow': 5, 'status': 'married'}, 134822), ('partial-repair probe', {'wage': 872994, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 164678), ('partial-repair probe', {'wage': 166201, 'freq': 'weekly', 'allow': 0, 'status': 'married'}, 17252), ('boundary control', {'wage': 0, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 0), ('boundary control', {'wage': 57693, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 5577), ('normal control', {'wage': 23859, 'freq': 'semimonthly', 'allow': 2, 'status': 'married'}, 0), ('normal control', {'wage': 50426, 'freq': 'semimonthly', 'allow': 5, 'status': 'married'}, 0), ('normal control', {'wage': 128102, 'freq': 'monthly', 'allow': 5, 'status': 'married'}, 0), ('normal control', {'wage': 6608, 'freq': 'semimonthly', 'allow': 0, 'status': 'married'}, 0)], [('regression', {'wage': 872994, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 164678), ('regression', {'wage': 143245, 'freq': 'biweekly', 'allow': 5, 'status': 'married'}, 2978), ('partial-repair probe', {'wage': 1203384, 'freq': 'biweekly', 'allow': 6, 'status': 'single'}, 220991), ('partial-repair probe', {'wage': 1136572, 'freq': 'weekly', 'allow': 1, 'status': 'married'}, 226304), ('boundary control', {'wage': 125000, 'freq': 'semimonthly', 'allow': 1, 'status': 'married'}, 7375), ('boundary control', {'wage': 0, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 0), ('normal control', {'wage': 49359, 'freq': 'weekly', 'allow': 6, 'status': 'single'}, 0), ('normal control', {'wage': 56499, 'freq': 'monthly', 'allow': 4, 'status': 'married'}, 0), ('normal control', {'wage': 6762, 'freq': 'weekly', 'allow': 6, 'status': 'single'}, 0), ('normal control', {'wage': 30428, 'freq': 'monthly', 'allow': 4, 'status': 'married'}, 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 | 168151 | 168152 | Failed |
| regression 1 | 483 | 483 | Passed |
| partial-repair probe 2 | 190901 | 190902 | Failed |
| partial-repair probe 3 | 1965 | 1966 | Failed |
| boundary control 4 | 0 | 0 | Passed |
| boundary control 5 | 5577 | 5577 | Passed |
| normal control 6 | 0 | 0 | Passed |
| normal control 7 | 0 | 0 | Passed |
| normal control 8 | 0 | 0 | Passed |
| normal control 9 | 0 | 0 | Passed |
SHA-256 / 9c3d031dba41ec5812ee503620d83bf73edc500833ca8a92f326e365cb915d6f
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
periods = {'weekly': 52, 'biweekly': 26, 'semimonthly': 24, 'monthly': 12}[x['freq']]
annual = x['wage'] * periods
adjusted = max(0, annual - x['allow'] * 430000)
mult = 2 if x['status'] == 'married' else 1
brackets = [(400000 * mult, 10), (1500000 * mult, 12), (5000000 * mult, 22)]
tax = 0
for i, (lo, rate) in enumerate(brackets):
hi = brackets[i + 1][0] if i + 1 < len(brackets) else None
if adjusted > lo:
top = adjusted if hi is None else min(adjusted, hi)
tax += (top - lo) * rate
wh = (tax * 2 + 100 * periods) // (200 * periods)
return wh
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'wage': 998152, 'freq': 'semimonthly', 'allow': 1, 'status': 'married'}, 168152), ('regression', {'wage': 36754, 'freq': 'biweekly', 'allow': 1, 'status': 'single'}, 483), ('partial-repair probe', {'wage': 992194, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 190902), ('partial-repair probe', {'wage': 124657, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 1966), ('boundary control', {'wage': 0, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 0), ('boundary control', {'wage': 57693, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 5577), ('normal control', {'wage': 22359, 'freq': 'semimonthly', 'allow': 5, 'status': 'married'}, 0), ('normal control', {'wage': 89742, 'freq': 'monthly', 'allow': 1, 'status': 'married'}, 0), ('normal control', {'wage': 17607, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 0), ('normal control', {'wage': 6134, 'freq': 'semimonthly', 'allow': 0, 'status': 'single'}, 0)], [('regression', {'wage': 124657, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 1966), ('regression', {'wage': 36754, 'freq': 'biweekly', 'allow': 1, 'status': 'single'}, 483), ('partial-repair probe', {'wage': 157996, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 5300), ('partial-repair probe', {'wage': 1223812, 'freq': 'monthly', 'allow': 5, 'status': 'married'}, 134822), ('boundary control', {'wage': 125000, 'freq': 'semimonthly', 'allow': 1, 'status': 'married'}, 7375), ('boundary control', {'wage': 0, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 0), ('normal control', {'wage': 52312, 'freq': 'semimonthly', 'allow': 5, 'status': 'single'}, 0), ('normal control', {'wage': 28087, 'freq': 'biweekly', 'allow': 1, 'status': 'single'}, 0), ('normal control', {'wage': 97334, 'freq': 'monthly', 'allow': 6, 'status': 'single'}, 0), ('normal control', {'wage': 18119, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 0)], [('regression', {'wage': 1223812, 'freq': 'monthly', 'allow': 5, 'status': 'married'}, 134822), ('regression', {'wage': 124657, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 1966), ('partial-repair probe', {'wage': 475908, 'freq': 'monthly', 'allow': 6, 'status': 'single'}, 25476), ('partial-repair probe', {'wage': 872994, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 164678), ('boundary control', {'wage': 57693, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 5577), ('boundary control', {'wage': 125000, 'freq': 'semimonthly', 'allow': 1, 'status': 'married'}, 7375), ('normal control', {'wage': 10871, 'freq': 'weekly', 'allow': 6, 'status': 'single'}, 0), ('normal control', {'wage': 836, 'freq': 'semimonthly', 'allow': 1, 'status': 'single'}, 0), ('normal control', {'wage': 30453, 'freq': 'biweekly', 'allow': 2, 'status': 'married'}, 0), ('normal control', {'wage': 27188, 'freq': 'semimonthly', 'allow': 4, 'status': 'married'}, 0)], [('regression', {'wage': 475908, 'freq': 'monthly', 'allow': 6, 'status': 'single'}, 25476), ('regression', {'wage': 1223812, 'freq': 'monthly', 'allow': 5, 'status': 'married'}, 134822), ('partial-repair probe', {'wage': 872994, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 164678), ('partial-repair probe', {'wage': 166201, 'freq': 'weekly', 'allow': 0, 'status': 'married'}, 17252), ('boundary control', {'wage': 0, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 0), ('boundary control', {'wage': 57693, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 5577), ('normal control', {'wage': 23859, 'freq': 'semimonthly', 'allow': 2, 'status': 'married'}, 0), ('normal control', {'wage': 50426, 'freq': 'semimonthly', 'allow': 5, 'status': 'married'}, 0), ('normal control', {'wage': 128102, 'freq': 'monthly', 'allow': 5, 'status': 'married'}, 0), ('normal control', {'wage': 6608, 'freq': 'semimonthly', 'allow': 0, 'status': 'married'}, 0)], [('regression', {'wage': 872994, 'freq': 'weekly', 'allow': 3, 'status': 'married'}, 164678), ('regression', {'wage': 143245, 'freq': 'biweekly', 'allow': 5, 'status': 'married'}, 2978), ('partial-repair probe', {'wage': 1203384, 'freq': 'biweekly', 'allow': 6, 'status': 'single'}, 220991), ('partial-repair probe', {'wage': 1136572, 'freq': 'weekly', 'allow': 1, 'status': 'married'}, 226304), ('boundary control', {'wage': 125000, 'freq': 'semimonthly', 'allow': 1, 'status': 'married'}, 7375), ('boundary control', {'wage': 0, 'freq': 'weekly', 'allow': 0, 'status': 'single'}, 0), ('normal control', {'wage': 49359, 'freq': 'weekly', 'allow': 6, 'status': 'single'}, 0), ('normal control', {'wage': 56499, 'freq': 'monthly', 'allow': 4, 'status': 'married'}, 0), ('normal control', {'wage': 6762, 'freq': 'weekly', 'allow': 6, 'status': 'single'}, 0), ('normal control', {'wage': 30428, 'freq': 'monthly', 'allow': 4, 'status': 'married'}, 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 | 168152 | 168152 | Passed |
| regression 1 | 483 | 483 | Passed |
| partial-repair probe 2 | 190902 | 190902 | Passed |
| partial-repair probe 3 | 1966 | 1966 | Passed |
| boundary control 4 | 0 | 0 | Passed |
| boundary control 5 | 5577 | 5577 | Passed |
| normal control 6 | 0 | 0 | Passed |
| normal control 7 | 0 | 0 | Passed |
| normal control 8 | 0 | 0 | Passed |
| normal control 9 | 0 | 0 | Passed |
SHA-256 / 4f6d1306290a2ad43af19ff58fa0dde71b0b48011d6c88324a3c8781ca13313a
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 2026-09-29T14:46:31.121445+00:00.
Case digest / db3e5f3e716163d886443fba1fdfebfd833754c919f351e78bec5cac88448b7f