FA-77486 / Legal deadline computation / Open access

Limitations period with discovery accrual, tolling and repose cap: toll span · case 01

A one-day tolling interval adds nothing.

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

ROOT CAUSE

The inclusive tolling interval is measured as a half-open difference.

VERIFIED REPAIR

At the toll span decision use `(b - a).days + 1`, leaving the other decision sites of the model unchanged.

Unsuccessful approach: The partial repair `(b - a).days + 2` overcounts by adding two days per interval.

Case contract

Stipulated: the claim accrues on the later of injury and discovery. The base deadline is the anniversary of accrual after years (a February 29 anniversary in a common year becomes February 28). Tolling intervals [start, end] are inclusive of both days, days before accrual do not count, and overlapping intervals count each day once; the tolled day count is added to the base deadline. A repose deadline of repose_years from the injury (same anniversary rule, never tolled) caps the result.

Why this case matters

Docketing systems compute filing and response deadlines from trigger events; a wrong date can forfeit a right.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
import datetime as dt
import calendar
N = 1
observations = []
def solve(injury, discovery, years, tolls, repose_years):
    inj = dt.date.fromisoformat(injury)
    disc = dt.date.fromisoformat(discovery)
    accrual = max(inj, disc)
    def add_years(d, n):
        y = d.year + n
        return dt.date(y, d.month, min(d.day, calendar.monthrange(y, d.month)[1]))
    base = add_years(accrual, years)
    covered = set()
    total = 0
    for a, b in tolls:
        a = dt.date.fromisoformat(a)
        b = dt.date.fromisoformat(b)
        a = max(a, accrual)
        span = max(0, (b - a).days)
        total += span
        for i in range(span):
            covered.add(a + dt.timedelta(days=i))
    tolled = len(covered)
    extended = base + dt.timedelta(days=tolled)
    repose = add_years(inj, repose_years)
    return (min(extended, repose)).isoformat()
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: discovery then overlapping tolls',
   ['2018-03-10', '2019-06-01', 2, [['2019-07-01', '2019-07-10'], ['2019-07-05', '2019-07-20']], 10],
   '2021-06-21'),
  ('leap day accrual', ['2020-02-29', '2020-02-29', 3, [], 10], '2023-02-28'),
  ('leap crossing two years', ['2023-03-01', '2023-03-01', 2, [], 10], '2025-03-01'),
  ('toll before discovery',
   ['2015-01-10', '2016-05-05', 3, [['2015-06-01', '2015-12-31'], ['2017-01-01', '2017-01-01']], 10],
   '2019-05-06'),
  ('one day toll', ['2021-04-04', '2021-04-04', 1, [['2021-05-05', '2021-05-05']], 5], '2022-04-05'),
  ('repose caps', ['2010-01-15', '2019-06-01', 3, [['2020-01-01', '2020-06-30']], 10], '2020-01-15'),
  ('repose with tolling beyond',
   ['2014-07-01', '2015-07-01', 7, [['2016-01-01', '2016-03-31']], 8],
   '2022-07-01')],
 [('leap crossing two years', ['2023-03-01', '2023-03-01', 2, [], 10], '2025-03-01'),
  ('toll before discovery',
   ['2015-01-10', '2016-05-05', 3, [['2015-06-01', '2015-12-31'], ['2017-01-01', '2017-01-01']], 10],
   '2019-05-06'),
  ('one day toll', ['2021-04-04', '2021-04-04', 1, [['2021-05-05', '2021-05-05']], 5], '2022-04-05'),
  ('repose caps', ['2010-01-15', '2019-06-01', 3, [['2020-01-01', '2020-06-30']], 10], '2020-01-15'),
  ('repose with tolling beyond',
   ['2014-07-01', '2015-07-01', 7, [['2016-01-01', '2016-03-31']], 8],
   '2022-07-01'),
  ('toll straddles accrual',
   ['2019-01-01', '2019-02-01', 1, [['2019-01-20', '2019-02-10']], 4],
   '2020-02-11'),
  ('three overlapping tolls',
   ['2022-05-05',
    '2022-05-05',
    2,
    [['2022-06-01', '2022-06-30'], ['2022-06-15', '2022-07-15'], ['2022-07-10', '2022-07-12']],
    6],
   '2024-06-19')],
 [('one day toll', ['2021-04-04', '2021-04-04', 1, [['2021-05-05', '2021-05-05']], 5], '2022-04-05'),
  ('repose caps', ['2010-01-15', '2019-06-01', 3, [['2020-01-01', '2020-06-30']], 10], '2020-01-15'),
  ('repose with tolling beyond',
   ['2014-07-01', '2015-07-01', 7, [['2016-01-01', '2016-03-31']], 8],
   '2022-07-01'),
  ('toll straddles accrual',
   ['2019-01-01', '2019-02-01', 1, [['2019-01-20', '2019-02-10']], 4],
   '2020-02-11'),
  ('three overlapping tolls',
   ['2022-05-05',
    '2022-05-05',
    2,
    [['2022-06-01', '2022-06-30'], ['2022-06-15', '2022-07-15'], ['2022-07-10', '2022-07-12']],
    6],
   '2024-06-19'),
  ('discovery earlier than injury input', ['2021-09-09', '2021-08-01', 2, [], 4], '2023-09-09'),
  ('toll wholly before injury',
   ['2020-10-10', '2020-10-10', 1, [['2019-01-01', '2019-02-01']], 3],
   '2021-10-10')],
 [('repose with tolling beyond',
   ['2014-07-01', '2015-07-01', 7, [['2016-01-01', '2016-03-31']], 8],
   '2022-07-01'),
  ('toll straddles accrual',
   ['2019-01-01', '2019-02-01', 1, [['2019-01-20', '2019-02-10']], 4],
   '2020-02-11'),
  ('three overlapping tolls',
   ['2022-05-05',
    '2022-05-05',
    2,
    [['2022-06-01', '2022-06-30'], ['2022-06-15', '2022-07-15'], ['2022-07-10', '2022-07-12']],
    6],
   '2024-06-19'),
  ('discovery earlier than injury input', ['2021-09-09', '2021-08-01', 2, [], 4], '2023-09-09'),
  ('toll wholly before injury',
   ['2020-10-10', '2020-10-10', 1, [['2019-01-01', '2019-02-01']], 3],
   '2021-10-10'),
  ('regression: discovery then overlapping tolls',
   ['2018-03-10', '2019-06-01', 2, [['2019-07-01', '2019-07-10'], ['2019-07-05', '2019-07-20']], 10],
   '2021-06-21'),
  ('leap day accrual', ['2020-02-29', '2020-02-29', 3, [], 10], '2023-02-28')],
 [('three overlapping tolls',
   ['2022-05-05',
    '2022-05-05',
    2,
    [['2022-06-01', '2022-06-30'], ['2022-06-15', '2022-07-15'], ['2022-07-10', '2022-07-12']],
    6],
   '2024-06-19'),
  ('discovery earlier than injury input', ['2021-09-09', '2021-08-01', 2, [], 4], '2023-09-09'),
  ('toll wholly before injury',
   ['2020-10-10', '2020-10-10', 1, [['2019-01-01', '2019-02-01']], 3],
   '2021-10-10'),
  ('regression: discovery then overlapping tolls',
   ['2018-03-10', '2019-06-01', 2, [['2019-07-01', '2019-07-10'], ['2019-07-05', '2019-07-20']], 10],
   '2021-06-21'),
  ('leap day accrual', ['2020-02-29', '2020-02-29', 3, [], 10], '2023-02-28'),
  ('leap crossing two years', ['2023-03-01', '2023-03-01', 2, [], 10], '2025-03-01'),
  ('toll before discovery',
   ['2015-01-10', '2016-05-05', 3, [['2015-06-01', '2015-12-31'], ['2017-01-01', '2017-01-01']], 10],
   '2019-05-06')]]
for label, args, expected in fixtures[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: discovery then overlapping tolls	`2021-06-20`	`2021-06-21`	Failed
leap day accrual	`2023-02-28`	`2023-02-28`	Passed
leap crossing two years	`2025-03-01`	`2025-03-01`	Passed
toll before discovery	`2019-05-05`	`2019-05-06`	Failed
one day toll	`2022-04-04`	`2022-04-05`	Failed
repose caps	`2020-01-15`	`2020-01-15`	Passed
repose with tolling beyond	`2022-07-01`	`2022-07-01`	Passed

SHA-256 / de282f2b81ebfe63cd96fb8c5727fe5fb1bb7b54eff11a6976db4f069b2ffa94

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
import datetime as dt
import calendar
N = 1
observations = []
def solve(injury, discovery, years, tolls, repose_years):
    inj = dt.date.fromisoformat(injury)
    disc = dt.date.fromisoformat(discovery)
    accrual = max(inj, disc)
    def add_years(d, n):
        y = d.year + n
        return dt.date(y, d.month, min(d.day, calendar.monthrange(y, d.month)[1]))
    base = add_years(accrual, years)
    covered = set()
    total = 0
    for a, b in tolls:
        a = dt.date.fromisoformat(a)
        b = dt.date.fromisoformat(b)
        a = max(a, accrual)
        span = max(0, (b - a).days + 2)
        total += span
        for i in range(span):
            covered.add(a + dt.timedelta(days=i))
    tolled = len(covered)
    extended = base + dt.timedelta(days=tolled)
    repose = add_years(inj, repose_years)
    return (min(extended, repose)).isoformat()
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: discovery then overlapping tolls',
   ['2018-03-10', '2019-06-01', 2, [['2019-07-01', '2019-07-10'], ['2019-07-05', '2019-07-20']], 10],
   '2021-06-21'),
  ('leap day accrual', ['2020-02-29', '2020-02-29', 3, [], 10], '2023-02-28'),
  ('leap crossing two years', ['2023-03-01', '2023-03-01', 2, [], 10], '2025-03-01'),
  ('toll before discovery',
   ['2015-01-10', '2016-05-05', 3, [['2015-06-01', '2015-12-31'], ['2017-01-01', '2017-01-01']], 10],
   '2019-05-06'),
  ('one day toll', ['2021-04-04', '2021-04-04', 1, [['2021-05-05', '2021-05-05']], 5], '2022-04-05'),
  ('repose caps', ['2010-01-15', '2019-06-01', 3, [['2020-01-01', '2020-06-30']], 10], '2020-01-15'),
  ('repose with tolling beyond',
   ['2014-07-01', '2015-07-01', 7, [['2016-01-01', '2016-03-31']], 8],
   '2022-07-01')],
 [('leap crossing two years', ['2023-03-01', '2023-03-01', 2, [], 10], '2025-03-01'),
  ('toll before discovery',
   ['2015-01-10', '2016-05-05', 3, [['2015-06-01', '2015-12-31'], ['2017-01-01', '2017-01-01']], 10],
   '2019-05-06'),
  ('one day toll', ['2021-04-04', '2021-04-04', 1, [['2021-05-05', '2021-05-05']], 5], '2022-04-05'),
  ('repose caps', ['2010-01-15', '2019-06-01', 3, [['2020-01-01', '2020-06-30']], 10], '2020-01-15'),
  ('repose with tolling beyond',
   ['2014-07-01', '2015-07-01', 7, [['2016-01-01', '2016-03-31']], 8],
   '2022-07-01'),
  ('toll straddles accrual',
   ['2019-01-01', '2019-02-01', 1, [['2019-01-20', '2019-02-10']], 4],
   '2020-02-11'),
  ('three overlapping tolls',
   ['2022-05-05',
    '2022-05-05',
    2,
    [['2022-06-01', '2022-06-30'], ['2022-06-15', '2022-07-15'], ['2022-07-10', '2022-07-12']],
    6],
   '2024-06-19')],
 [('one day toll', ['2021-04-04', '2021-04-04', 1, [['2021-05-05', '2021-05-05']], 5], '2022-04-05'),
  ('repose caps', ['2010-01-15', '2019-06-01', 3, [['2020-01-01', '2020-06-30']], 10], '2020-01-15'),
  ('repose with tolling beyond',
   ['2014-07-01', '2015-07-01', 7, [['2016-01-01', '2016-03-31']], 8],
   '2022-07-01'),
  ('toll straddles accrual',
   ['2019-01-01', '2019-02-01', 1, [['2019-01-20', '2019-02-10']], 4],
   '2020-02-11'),
  ('three overlapping tolls',
   ['2022-05-05',
    '2022-05-05',
    2,
    [['2022-06-01', '2022-06-30'], ['2022-06-15', '2022-07-15'], ['2022-07-10', '2022-07-12']],
    6],
   '2024-06-19'),
  ('discovery earlier than injury input', ['2021-09-09', '2021-08-01', 2, [], 4], '2023-09-09'),
  ('toll wholly before injury',
   ['2020-10-10', '2020-10-10', 1, [['2019-01-01', '2019-02-01']], 3],
   '2021-10-10')],
 [('repose with tolling beyond',
   ['2014-07-01', '2015-07-01', 7, [['2016-01-01', '2016-03-31']], 8],
   '2022-07-01'),
  ('toll straddles accrual',
   ['2019-01-01', '2019-02-01', 1, [['2019-01-20', '2019-02-10']], 4],
   '2020-02-11'),
  ('three overlapping tolls',
   ['2022-05-05',
    '2022-05-05',
    2,
    [['2022-06-01', '2022-06-30'], ['2022-06-15', '2022-07-15'], ['2022-07-10', '2022-07-12']],
    6],
   '2024-06-19'),
  ('discovery earlier than injury input', ['2021-09-09', '2021-08-01', 2, [], 4], '2023-09-09'),
  ('toll wholly before injury',
   ['2020-10-10', '2020-10-10', 1, [['2019-01-01', '2019-02-01']], 3],
   '2021-10-10'),
  ('regression: discovery then overlapping tolls',
   ['2018-03-10', '2019-06-01', 2, [['2019-07-01', '2019-07-10'], ['2019-07-05', '2019-07-20']], 10],
   '2021-06-21'),
  ('leap day accrual', ['2020-02-29', '2020-02-29', 3, [], 10], '2023-02-28')],
 [('three overlapping tolls',
   ['2022-05-05',
    '2022-05-05',
    2,
    [['2022-06-01', '2022-06-30'], ['2022-06-15', '2022-07-15'], ['2022-07-10', '2022-07-12']],
    6],
   '2024-06-19'),
  ('discovery earlier than injury input', ['2021-09-09', '2021-08-01', 2, [], 4], '2023-09-09'),
  ('toll wholly before injury',
   ['2020-10-10', '2020-10-10', 1, [['2019-01-01', '2019-02-01']], 3],
   '2021-10-10'),
  ('regression: discovery then overlapping tolls',
   ['2018-03-10', '2019-06-01', 2, [['2019-07-01', '2019-07-10'], ['2019-07-05', '2019-07-20']], 10],
   '2021-06-21'),
  ('leap day accrual', ['2020-02-29', '2020-02-29', 3, [], 10], '2023-02-28'),
  ('leap crossing two years', ['2023-03-01', '2023-03-01', 2, [], 10], '2025-03-01'),
  ('toll before discovery',
   ['2015-01-10', '2016-05-05', 3, [['2015-06-01', '2015-12-31'], ['2017-01-01', '2017-01-01']], 10],
   '2019-05-06')]]
for label, args, expected in fixtures[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: discovery then overlapping tolls	`2021-06-22`	`2021-06-21`	Failed
leap day accrual	`2023-02-28`	`2023-02-28`	Passed
leap crossing two years	`2025-03-01`	`2025-03-01`	Passed
toll before discovery	`2019-05-07`	`2019-05-06`	Failed
one day toll	`2022-04-06`	`2022-04-05`	Failed
repose caps	`2020-01-15`	`2020-01-15`	Passed
repose with tolling beyond	`2022-07-01`	`2022-07-01`	Passed

SHA-256 / 998ccfd2d190b575662c1d53b89fe078c7fe50a6db86f1c8d40235a9765e6875

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json
import datetime as dt
import calendar
N = 1
observations = []
def solve(injury, discovery, years, tolls, repose_years):
    inj = dt.date.fromisoformat(injury)
    disc = dt.date.fromisoformat(discovery)
    accrual = max(inj, disc)
    def add_years(d, n):
        y = d.year + n
        return dt.date(y, d.month, min(d.day, calendar.monthrange(y, d.month)[1]))
    base = add_years(accrual, years)
    covered = set()
    total = 0
    for a, b in tolls:
        a = dt.date.fromisoformat(a)
        b = dt.date.fromisoformat(b)
        a = max(a, accrual)
        span = max(0, (b - a).days + 1)
        total += span
        for i in range(span):
            covered.add(a + dt.timedelta(days=i))
    tolled = len(covered)
    extended = base + dt.timedelta(days=tolled)
    repose = add_years(inj, repose_years)
    return (min(extended, repose)).isoformat()
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: discovery then overlapping tolls',
   ['2018-03-10', '2019-06-01', 2, [['2019-07-01', '2019-07-10'], ['2019-07-05', '2019-07-20']], 10],
   '2021-06-21'),
  ('leap day accrual', ['2020-02-29', '2020-02-29', 3, [], 10], '2023-02-28'),
  ('leap crossing two years', ['2023-03-01', '2023-03-01', 2, [], 10], '2025-03-01'),
  ('toll before discovery',
   ['2015-01-10', '2016-05-05', 3, [['2015-06-01', '2015-12-31'], ['2017-01-01', '2017-01-01']], 10],
   '2019-05-06'),
  ('one day toll', ['2021-04-04', '2021-04-04', 1, [['2021-05-05', '2021-05-05']], 5], '2022-04-05'),
  ('repose caps', ['2010-01-15', '2019-06-01', 3, [['2020-01-01', '2020-06-30']], 10], '2020-01-15'),
  ('repose with tolling beyond',
   ['2014-07-01', '2015-07-01', 7, [['2016-01-01', '2016-03-31']], 8],
   '2022-07-01')],
 [('leap crossing two years', ['2023-03-01', '2023-03-01', 2, [], 10], '2025-03-01'),
  ('toll before discovery',
   ['2015-01-10', '2016-05-05', 3, [['2015-06-01', '2015-12-31'], ['2017-01-01', '2017-01-01']], 10],
   '2019-05-06'),
  ('one day toll', ['2021-04-04', '2021-04-04', 1, [['2021-05-05', '2021-05-05']], 5], '2022-04-05'),
  ('repose caps', ['2010-01-15', '2019-06-01', 3, [['2020-01-01', '2020-06-30']], 10], '2020-01-15'),
  ('repose with tolling beyond',
   ['2014-07-01', '2015-07-01', 7, [['2016-01-01', '2016-03-31']], 8],
   '2022-07-01'),
  ('toll straddles accrual',
   ['2019-01-01', '2019-02-01', 1, [['2019-01-20', '2019-02-10']], 4],
   '2020-02-11'),
  ('three overlapping tolls',
   ['2022-05-05',
    '2022-05-05',
    2,
    [['2022-06-01', '2022-06-30'], ['2022-06-15', '2022-07-15'], ['2022-07-10', '2022-07-12']],
    6],
   '2024-06-19')],
 [('one day toll', ['2021-04-04', '2021-04-04', 1, [['2021-05-05', '2021-05-05']], 5], '2022-04-05'),
  ('repose caps', ['2010-01-15', '2019-06-01', 3, [['2020-01-01', '2020-06-30']], 10], '2020-01-15'),
  ('repose with tolling beyond',
   ['2014-07-01', '2015-07-01', 7, [['2016-01-01', '2016-03-31']], 8],
   '2022-07-01'),
  ('toll straddles accrual',
   ['2019-01-01', '2019-02-01', 1, [['2019-01-20', '2019-02-10']], 4],
   '2020-02-11'),
  ('three overlapping tolls',
   ['2022-05-05',
    '2022-05-05',
    2,
    [['2022-06-01', '2022-06-30'], ['2022-06-15', '2022-07-15'], ['2022-07-10', '2022-07-12']],
    6],
   '2024-06-19'),
  ('discovery earlier than injury input', ['2021-09-09', '2021-08-01', 2, [], 4], '2023-09-09'),
  ('toll wholly before injury',
   ['2020-10-10', '2020-10-10', 1, [['2019-01-01', '2019-02-01']], 3],
   '2021-10-10')],
 [('repose with tolling beyond',
   ['2014-07-01', '2015-07-01', 7, [['2016-01-01', '2016-03-31']], 8],
   '2022-07-01'),
  ('toll straddles accrual',
   ['2019-01-01', '2019-02-01', 1, [['2019-01-20', '2019-02-10']], 4],
   '2020-02-11'),
  ('three overlapping tolls',
   ['2022-05-05',
    '2022-05-05',
    2,
    [['2022-06-01', '2022-06-30'], ['2022-06-15', '2022-07-15'], ['2022-07-10', '2022-07-12']],
    6],
   '2024-06-19'),
  ('discovery earlier than injury input', ['2021-09-09', '2021-08-01', 2, [], 4], '2023-09-09'),
  ('toll wholly before injury',
   ['2020-10-10', '2020-10-10', 1, [['2019-01-01', '2019-02-01']], 3],
   '2021-10-10'),
  ('regression: discovery then overlapping tolls',
   ['2018-03-10', '2019-06-01', 2, [['2019-07-01', '2019-07-10'], ['2019-07-05', '2019-07-20']], 10],
   '2021-06-21'),
  ('leap day accrual', ['2020-02-29', '2020-02-29', 3, [], 10], '2023-02-28')],
 [('three overlapping tolls',
   ['2022-05-05',
    '2022-05-05',
    2,
    [['2022-06-01', '2022-06-30'], ['2022-06-15', '2022-07-15'], ['2022-07-10', '2022-07-12']],
    6],
   '2024-06-19'),
  ('discovery earlier than injury input', ['2021-09-09', '2021-08-01', 2, [], 4], '2023-09-09'),
  ('toll wholly before injury',
   ['2020-10-10', '2020-10-10', 1, [['2019-01-01', '2019-02-01']], 3],
   '2021-10-10'),
  ('regression: discovery then overlapping tolls',
   ['2018-03-10', '2019-06-01', 2, [['2019-07-01', '2019-07-10'], ['2019-07-05', '2019-07-20']], 10],
   '2021-06-21'),
  ('leap day accrual', ['2020-02-29', '2020-02-29', 3, [], 10], '2023-02-28'),
  ('leap crossing two years', ['2023-03-01', '2023-03-01', 2, [], 10], '2025-03-01'),
  ('toll before discovery',
   ['2015-01-10', '2016-05-05', 3, [['2015-06-01', '2015-12-31'], ['2017-01-01', '2017-01-01']], 10],
   '2019-05-06')]]
for label, args, expected in fixtures[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: discovery then overlapping tolls	`2021-06-21`	`2021-06-21`	Passed
leap day accrual	`2023-02-28`	`2023-02-28`	Passed
leap crossing two years	`2025-03-01`	`2025-03-01`	Passed
toll before discovery	`2019-05-06`	`2019-05-06`	Passed
one day toll	`2022-04-05`	`2022-04-05`	Passed
repose caps	`2020-01-15`	`2020-01-15`	Passed
repose with tolling beyond	`2022-07-01`	`2022-07-01`	Passed

SHA-256 / c2414393c47cb015482af313c31cabcb4d7b44b0b6cf891cb757972dc18d72bb

Verification & scope

Stipulated toy deadline rules evaluated offline on explicit fixtures. The contract is inspired by common court-rule structures but is not legal advice and makes no claim to reproduce any jurisdiction'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 2026-09-29T14:49:25.892857+00:00.

Case digest / abc3daa5098de874aa498b0b5ed3887edf6720ef484c10298466f481b213b5f5