FAILURE MAP
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FA-17741 / Time representation / Open access

Day encoding erases leap metadata or adds a leap to every day · case 01

The decoded time state disagrees with the explicit regression oracle for length.

Verified by executionVariant 1 · 11 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Day encoding erases leap metadata or adds a leap to every day.

THE FAILURE

Day encoding erases leap metadata or adds a leap to every day.

Unsuccessful approach: The partial correction still substitutes 86401 at the same fault site.

Case contract

A stipulated positive-leap day has label 86400 following ordinary 0..86399; a normal day does not. Return validity, atomic origin+label or None, display [hour,minute,second] (23:59:60 for extra label), final-label flag, ticks until next midnight or None, and atomic next-midnight coordinate. No real-world leap table is claimed.

Why this case matters

Clock transfer and timestamp consumers require preserved coordinate, phase, validity and elapsed-time semantics.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(r):
    length = 86400
    valid = 0 <= r['label'] < length
    atomic = r['origin'] + r['label'] if valid else None
    extra = valid and r['label'] == 86400
    hour = None if not valid else (23 if extra else r['label'] // 3600)
    minute = None if not valid else (59 if extra else r['label'] // 60 % 60)
    second = None if not valid else (60 if extra else r['label'] % 60)
    last = valid and r['label'] == length - 1
    remaining = None if not valid else length - r['label']
    midnight = r['origin'] + length
    return [valid,atomic,[hour,minute,second],last,remaining,midnight]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve({'leap': True, 'label': 86400, 'origin': 100000}), [True, 186400, [23, 59, 60], True, 1, 186401])
check('fixture 2', solve({'leap': False, 'label': 86400, 'origin': 100000}), [False, None, [None, None, None], False, None, 186400])
check('fixture 3', solve({'leap': True, 'label': 86399, 'origin': 50}), [True, 86449, [23, 59, 59], False, 2, 86451])
check('fixture 4', solve({'leap': False, 'label': 86399, 'origin': 50}), [True, 86449, [23, 59, 59], True, 1, 86450])
check('fixture 5', solve({'leap': True, 'label': 0, 'origin': -100}), [True, -100, [0, 0, 0], False, 86401, 86301])
check('fixture 6', solve({'leap': False, 'label': 3601, 'origin': 0}), [True, 3601, [1, 0, 1], False, 82799, 86400])
check('fixture 7', solve({'leap': True, 'label': -1, 'origin': 100}), [False, None, [None, None, None], False, None, 86501])
check('fixture 8', solve({'leap': True, 'label': 86401, 'origin': 20}), [False, None, [None, None, None], False, None, 86421])
check('fixture 9', solve({'leap': False, 'label': 0, 'origin': 0}), [True, 0, [0, 0, 0], False, 86400, 86400])
check('fixture 10', solve({'leap': True, 'label': 3661, 'origin': -20}), [True, 3641, [1, 1, 1], False, 82740, 86381])
variant = [({'leap': True, 'label': 86400, 'origin': 100001}, [True, 186401, [23, 59, 60], True, 1, 186402]), ({'leap': True, 'label': 86400, 'origin': 100002}, [True, 186402, [23, 59, 60], True, 1, 186403]), ({'leap': True, 'label': 86400, 'origin': 100003}, [True, 186403, [23, 59, 60], True, 1, 186404]), ({'leap': True, 'label': 86400, 'origin': 100004}, [True, 186404, [23, 59, 60], True, 1, 186405]), ({'leap': True, 'label': 86400, 'origin': 100005}, [True, 186405, [23, 59, 60], True, 1, 186406])]
check("variant capture", solve(variant[N-1][0]), variant[N-1][1])
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
fixture 1[False, None, [None, None, None], False, None, 186400][True, 186400, [23, 59, 60], True, 1, 186401]Failed
fixture 2[False, None, [None, None, None], False, None, 186400][False, None, [None, None, None], False, None, 186400]Passed
fixture 3[True, 86449, [23, 59, 59], True, 1, 86450][True, 86449, [23, 59, 59], False, 2, 86451]Failed
fixture 4[True, 86449, [23, 59, 59], True, 1, 86450][True, 86449, [23, 59, 59], True, 1, 86450]Passed
fixture 5[True, -100, [0, 0, 0], False, 86400, 86300][True, -100, [0, 0, 0], False, 86401, 86301]Failed
fixture 6[True, 3601, [1, 0, 1], False, 82799, 86400][True, 3601, [1, 0, 1], False, 82799, 86400]Passed
fixture 7[False, None, [None, None, None], False, None, 86500][False, None, [None, None, None], False, None, 86501]Failed
fixture 8[False, None, [None, None, None], False, None, 86420][False, None, [None, None, None], False, None, 86421]Failed
fixture 9[True, 0, [0, 0, 0], False, 86400, 86400][True, 0, [0, 0, 0], False, 86400, 86400]Passed
fixture 10[True, 3641, [1, 1, 1], False, 82739, 86380][True, 3641, [1, 1, 1], False, 82740, 86381]Failed
variant capture[False, None, [None, None, None], False, None, 186401][True, 186401, [23, 59, 60], True, 1, 186402]Failed

SHA-256 / d2df3682774d4286f0ac55e22aafb7be0dd61572c93b778374e6d6a1ca56a5c9

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(r):
    length = 86401
    valid = 0 <= r['label'] < length
    atomic = r['origin'] + r['label'] if valid else None
    extra = valid and r['label'] == 86400
    hour = None if not valid else (23 if extra else r['label'] // 3600)
    minute = None if not valid else (59 if extra else r['label'] // 60 % 60)
    second = None if not valid else (60 if extra else r['label'] % 60)
    last = valid and r['label'] == length - 1
    remaining = None if not valid else length - r['label']
    midnight = r['origin'] + length
    return [valid,atomic,[hour,minute,second],last,remaining,midnight]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve({'leap': True, 'label': 86400, 'origin': 100000}), [True, 186400, [23, 59, 60], True, 1, 186401])
check('fixture 2', solve({'leap': False, 'label': 86400, 'origin': 100000}), [False, None, [None, None, None], False, None, 186400])
check('fixture 3', solve({'leap': True, 'label': 86399, 'origin': 50}), [True, 86449, [23, 59, 59], False, 2, 86451])
check('fixture 4', solve({'leap': False, 'label': 86399, 'origin': 50}), [True, 86449, [23, 59, 59], True, 1, 86450])
check('fixture 5', solve({'leap': True, 'label': 0, 'origin': -100}), [True, -100, [0, 0, 0], False, 86401, 86301])
check('fixture 6', solve({'leap': False, 'label': 3601, 'origin': 0}), [True, 3601, [1, 0, 1], False, 82799, 86400])
check('fixture 7', solve({'leap': True, 'label': -1, 'origin': 100}), [False, None, [None, None, None], False, None, 86501])
check('fixture 8', solve({'leap': True, 'label': 86401, 'origin': 20}), [False, None, [None, None, None], False, None, 86421])
check('fixture 9', solve({'leap': False, 'label': 0, 'origin': 0}), [True, 0, [0, 0, 0], False, 86400, 86400])
check('fixture 10', solve({'leap': True, 'label': 3661, 'origin': -20}), [True, 3641, [1, 1, 1], False, 82740, 86381])
variant = [({'leap': True, 'label': 86400, 'origin': 100001}, [True, 186401, [23, 59, 60], True, 1, 186402]), ({'leap': True, 'label': 86400, 'origin': 100002}, [True, 186402, [23, 59, 60], True, 1, 186403]), ({'leap': True, 'label': 86400, 'origin': 100003}, [True, 186403, [23, 59, 60], True, 1, 186404]), ({'leap': True, 'label': 86400, 'origin': 100004}, [True, 186404, [23, 59, 60], True, 1, 186405]), ({'leap': True, 'label': 86400, 'origin': 100005}, [True, 186405, [23, 59, 60], True, 1, 186406])]
check("variant capture", solve(variant[N-1][0]), variant[N-1][1])
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
fixture 1[True, 186400, [23, 59, 60], True, 1, 186401][True, 186400, [23, 59, 60], True, 1, 186401]Passed
fixture 2[True, 186400, [23, 59, 60], True, 1, 186401][False, None, [None, None, None], False, None, 186400]Failed
fixture 3[True, 86449, [23, 59, 59], False, 2, 86451][True, 86449, [23, 59, 59], False, 2, 86451]Passed
fixture 4[True, 86449, [23, 59, 59], False, 2, 86451][True, 86449, [23, 59, 59], True, 1, 86450]Failed
fixture 5[True, -100, [0, 0, 0], False, 86401, 86301][True, -100, [0, 0, 0], False, 86401, 86301]Passed
fixture 6[True, 3601, [1, 0, 1], False, 82800, 86401][True, 3601, [1, 0, 1], False, 82799, 86400]Failed
fixture 7[False, None, [None, None, None], False, None, 86501][False, None, [None, None, None], False, None, 86501]Passed
fixture 8[False, None, [None, None, None], False, None, 86421][False, None, [None, None, None], False, None, 86421]Passed
fixture 9[True, 0, [0, 0, 0], False, 86401, 86401][True, 0, [0, 0, 0], False, 86400, 86400]Failed
fixture 10[True, 3641, [1, 1, 1], False, 82740, 86381][True, 3641, [1, 1, 1], False, 82740, 86381]Passed
variant capture[True, 186401, [23, 59, 60], True, 1, 186402][True, 186401, [23, 59, 60], True, 1, 186402]Passed

SHA-256 / 21f2f889a34102e28aa8e7ba5fafdc8650462550ae1da964e1e8e63452dc3de2

HELD IN THE MEMBER ARCHIVE

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

This mechanism has 11 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

Deterministic integer reference model with stipulated units and policies; not a complete clock, wire standard or platform implementation. 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 2026-09-29T14:39:50.836205+00:00.

Case digest / 1d7644e87da118597c1e0a5b7bea8a3b4dfbc429d7ceb0f4b78dd266bf3b8195