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FA-18586 / Time representation / Open access

Transferred deadline uncertainty uses coordinate sum or selected policy offset · case 01

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

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

ROOT CAUSE

Transferred deadline uncertainty uses coordinate sum or selected policy offset.

VERIFIED REPAIR

Preserve the declared coordinate and state contract at slack: slack = high - low.

Unsuccessful approach: The partial correction still substitutes chosen - low at the same fault site.

Case contract

A deadline in source clock D maps to target clock with uncertain target-minus-source offset [lo,hi]. Use earliest target deadline for no-late policy, latest for no-early. Account for known transport time in target units and clamp remaining budget at zero. Relative waits are capped by platform maximum and flag rearming. Return target bounds, chosen deadline, wait, rearm and expired status.

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):
    low = r['deadline'] + r['offset_low']
    high = r['deadline'] + r['offset_high']
    chosen = low if r['policy'] == 'no-late' else high
    arrival = r['target_now'] + r['transport']
    remaining = max(0,chosen - arrival)
    wait = min(remaining,r['max_wait'])
    rearm = remaining > r['max_wait']
    expired = chosen <= arrival
    absolute_wake = arrival + wait
    slack = high + low
    return [[low,high],chosen,wait,rearm,expired,absolute_wake,slack]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve({'deadline': 20, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}), [[22, 25], 22, 10, False, False, 22, 3])
check('fixture 2', solve({'deadline': 20, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-early', 'target_now': 10, 'transport': 2, 'max_wait': 100}), [[22, 25], 25, 13, False, False, 25, 3])
check('fixture 3', solve({'deadline': 20, 'offset_low': -5, 'offset_high': -2, 'policy': 'no-late', 'target_now': 15, 'transport': 0, 'max_wait': 20}), [[15, 18], 15, 0, False, True, 15, 3])
check('fixture 4', solve({'deadline': 20, 'offset_low': -5, 'offset_high': -2, 'policy': 'no-early', 'target_now': 20, 'transport': 1, 'max_wait': 20}), [[15, 18], 18, 0, False, True, 21, 3])
check('fixture 5', solve({'deadline': 100, 'offset_low': 0, 'offset_high': 0, 'policy': 'no-late', 'target_now': 0, 'transport': 0, 'max_wait': 10}), [[100, 100], 100, 10, True, False, 10, 0])
check('fixture 6', solve({'deadline': 20, 'offset_low': 0, 'offset_high': 0, 'policy': 'no-late', 'target_now': 10, 'transport': 0, 'max_wait': 10}), [[20, 20], 20, 10, False, False, 20, 0])
check('fixture 7', solve({'deadline': 0, 'offset_low': 0, 'offset_high': 0, 'policy': 'no-late', 'target_now': 0, 'transport': 0, 'max_wait': 1}), [[0, 0], 0, 0, False, True, 0, 0])
check('fixture 8', solve({'deadline': -10, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-early', 'target_now': -20, 'transport': 2, 'max_wait': 4}), [[-8, -5], -5, 4, True, False, -14, 3])
check('fixture 9', solve({'deadline': 20, 'offset_low': 0, 'offset_high': 0, 'policy': 'no-early', 'target_now': 19, 'transport': 0, 'max_wait': 2}), [[20, 20], 20, 1, False, False, 20, 0])
check('fixture 10', solve({'deadline': 10, 'offset_low': 2, 'offset_high': 8, 'policy': 'no-late', 'target_now': 10, 'transport': 4, 'max_wait': 10}), [[12, 18], 12, 0, False, True, 14, 6])
variant = [({'deadline': 21, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[23, 26], 23, 11, False, False, 23, 3]), ({'deadline': 22, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[24, 27], 24, 12, False, False, 24, 3]), ({'deadline': 23, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[25, 28], 25, 13, False, False, 25, 3]), ({'deadline': 24, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[26, 29], 26, 14, False, False, 26, 3]), ({'deadline': 25, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[27, 30], 27, 15, False, False, 27, 3])]
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[[22, 25], 22, 10, False, False, 22, 47][[22, 25], 22, 10, False, False, 22, 3]Failed
fixture 2[[22, 25], 25, 13, False, False, 25, 47][[22, 25], 25, 13, False, False, 25, 3]Failed
fixture 3[[15, 18], 15, 0, False, True, 15, 33][[15, 18], 15, 0, False, True, 15, 3]Failed
fixture 4[[15, 18], 18, 0, False, True, 21, 33][[15, 18], 18, 0, False, True, 21, 3]Failed
fixture 5[[100, 100], 100, 10, True, False, 10, 200][[100, 100], 100, 10, True, False, 10, 0]Failed
fixture 6[[20, 20], 20, 10, False, False, 20, 40][[20, 20], 20, 10, False, False, 20, 0]Failed
fixture 7[[0, 0], 0, 0, False, True, 0, 0][[0, 0], 0, 0, False, True, 0, 0]Passed
fixture 8[[-8, -5], -5, 4, True, False, -14, -13][[-8, -5], -5, 4, True, False, -14, 3]Failed
fixture 9[[20, 20], 20, 1, False, False, 20, 40][[20, 20], 20, 1, False, False, 20, 0]Failed
fixture 10[[12, 18], 12, 0, False, True, 14, 30][[12, 18], 12, 0, False, True, 14, 6]Failed
variant capture[[23, 26], 23, 11, False, False, 23, 49][[23, 26], 23, 11, False, False, 23, 3]Failed

SHA-256 / 7105dd7db50ae6a61967ceb158bf453978d02914145da65200b9db7403fe3816

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):
    low = r['deadline'] + r['offset_low']
    high = r['deadline'] + r['offset_high']
    chosen = low if r['policy'] == 'no-late' else high
    arrival = r['target_now'] + r['transport']
    remaining = max(0,chosen - arrival)
    wait = min(remaining,r['max_wait'])
    rearm = remaining > r['max_wait']
    expired = chosen <= arrival
    absolute_wake = arrival + wait
    slack = chosen - low
    return [[low,high],chosen,wait,rearm,expired,absolute_wake,slack]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve({'deadline': 20, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}), [[22, 25], 22, 10, False, False, 22, 3])
check('fixture 2', solve({'deadline': 20, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-early', 'target_now': 10, 'transport': 2, 'max_wait': 100}), [[22, 25], 25, 13, False, False, 25, 3])
check('fixture 3', solve({'deadline': 20, 'offset_low': -5, 'offset_high': -2, 'policy': 'no-late', 'target_now': 15, 'transport': 0, 'max_wait': 20}), [[15, 18], 15, 0, False, True, 15, 3])
check('fixture 4', solve({'deadline': 20, 'offset_low': -5, 'offset_high': -2, 'policy': 'no-early', 'target_now': 20, 'transport': 1, 'max_wait': 20}), [[15, 18], 18, 0, False, True, 21, 3])
check('fixture 5', solve({'deadline': 100, 'offset_low': 0, 'offset_high': 0, 'policy': 'no-late', 'target_now': 0, 'transport': 0, 'max_wait': 10}), [[100, 100], 100, 10, True, False, 10, 0])
check('fixture 6', solve({'deadline': 20, 'offset_low': 0, 'offset_high': 0, 'policy': 'no-late', 'target_now': 10, 'transport': 0, 'max_wait': 10}), [[20, 20], 20, 10, False, False, 20, 0])
check('fixture 7', solve({'deadline': 0, 'offset_low': 0, 'offset_high': 0, 'policy': 'no-late', 'target_now': 0, 'transport': 0, 'max_wait': 1}), [[0, 0], 0, 0, False, True, 0, 0])
check('fixture 8', solve({'deadline': -10, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-early', 'target_now': -20, 'transport': 2, 'max_wait': 4}), [[-8, -5], -5, 4, True, False, -14, 3])
check('fixture 9', solve({'deadline': 20, 'offset_low': 0, 'offset_high': 0, 'policy': 'no-early', 'target_now': 19, 'transport': 0, 'max_wait': 2}), [[20, 20], 20, 1, False, False, 20, 0])
check('fixture 10', solve({'deadline': 10, 'offset_low': 2, 'offset_high': 8, 'policy': 'no-late', 'target_now': 10, 'transport': 4, 'max_wait': 10}), [[12, 18], 12, 0, False, True, 14, 6])
variant = [({'deadline': 21, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[23, 26], 23, 11, False, False, 23, 3]), ({'deadline': 22, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[24, 27], 24, 12, False, False, 24, 3]), ({'deadline': 23, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[25, 28], 25, 13, False, False, 25, 3]), ({'deadline': 24, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[26, 29], 26, 14, False, False, 26, 3]), ({'deadline': 25, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[27, 30], 27, 15, False, False, 27, 3])]
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[[22, 25], 22, 10, False, False, 22, 0][[22, 25], 22, 10, False, False, 22, 3]Failed
fixture 2[[22, 25], 25, 13, False, False, 25, 3][[22, 25], 25, 13, False, False, 25, 3]Passed
fixture 3[[15, 18], 15, 0, False, True, 15, 0][[15, 18], 15, 0, False, True, 15, 3]Failed
fixture 4[[15, 18], 18, 0, False, True, 21, 3][[15, 18], 18, 0, False, True, 21, 3]Passed
fixture 5[[100, 100], 100, 10, True, False, 10, 0][[100, 100], 100, 10, True, False, 10, 0]Passed
fixture 6[[20, 20], 20, 10, False, False, 20, 0][[20, 20], 20, 10, False, False, 20, 0]Passed
fixture 7[[0, 0], 0, 0, False, True, 0, 0][[0, 0], 0, 0, False, True, 0, 0]Passed
fixture 8[[-8, -5], -5, 4, True, False, -14, 3][[-8, -5], -5, 4, True, False, -14, 3]Passed
fixture 9[[20, 20], 20, 1, False, False, 20, 0][[20, 20], 20, 1, False, False, 20, 0]Passed
fixture 10[[12, 18], 12, 0, False, True, 14, 0][[12, 18], 12, 0, False, True, 14, 6]Failed
variant capture[[23, 26], 23, 11, False, False, 23, 0][[23, 26], 23, 11, False, False, 23, 3]Failed

SHA-256 / 78177f8464fb26f2cb5b249a7c2bb488782b7e21cbc5ead8936b272d80cd9d67

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(r):
    low = r['deadline'] + r['offset_low']
    high = r['deadline'] + r['offset_high']
    chosen = low if r['policy'] == 'no-late' else high
    arrival = r['target_now'] + r['transport']
    remaining = max(0,chosen - arrival)
    wait = min(remaining,r['max_wait'])
    rearm = remaining > r['max_wait']
    expired = chosen <= arrival
    absolute_wake = arrival + wait
    slack = high - low
    return [[low,high],chosen,wait,rearm,expired,absolute_wake,slack]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve({'deadline': 20, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}), [[22, 25], 22, 10, False, False, 22, 3])
check('fixture 2', solve({'deadline': 20, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-early', 'target_now': 10, 'transport': 2, 'max_wait': 100}), [[22, 25], 25, 13, False, False, 25, 3])
check('fixture 3', solve({'deadline': 20, 'offset_low': -5, 'offset_high': -2, 'policy': 'no-late', 'target_now': 15, 'transport': 0, 'max_wait': 20}), [[15, 18], 15, 0, False, True, 15, 3])
check('fixture 4', solve({'deadline': 20, 'offset_low': -5, 'offset_high': -2, 'policy': 'no-early', 'target_now': 20, 'transport': 1, 'max_wait': 20}), [[15, 18], 18, 0, False, True, 21, 3])
check('fixture 5', solve({'deadline': 100, 'offset_low': 0, 'offset_high': 0, 'policy': 'no-late', 'target_now': 0, 'transport': 0, 'max_wait': 10}), [[100, 100], 100, 10, True, False, 10, 0])
check('fixture 6', solve({'deadline': 20, 'offset_low': 0, 'offset_high': 0, 'policy': 'no-late', 'target_now': 10, 'transport': 0, 'max_wait': 10}), [[20, 20], 20, 10, False, False, 20, 0])
check('fixture 7', solve({'deadline': 0, 'offset_low': 0, 'offset_high': 0, 'policy': 'no-late', 'target_now': 0, 'transport': 0, 'max_wait': 1}), [[0, 0], 0, 0, False, True, 0, 0])
check('fixture 8', solve({'deadline': -10, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-early', 'target_now': -20, 'transport': 2, 'max_wait': 4}), [[-8, -5], -5, 4, True, False, -14, 3])
check('fixture 9', solve({'deadline': 20, 'offset_low': 0, 'offset_high': 0, 'policy': 'no-early', 'target_now': 19, 'transport': 0, 'max_wait': 2}), [[20, 20], 20, 1, False, False, 20, 0])
check('fixture 10', solve({'deadline': 10, 'offset_low': 2, 'offset_high': 8, 'policy': 'no-late', 'target_now': 10, 'transport': 4, 'max_wait': 10}), [[12, 18], 12, 0, False, True, 14, 6])
variant = [({'deadline': 21, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[23, 26], 23, 11, False, False, 23, 3]), ({'deadline': 22, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[24, 27], 24, 12, False, False, 24, 3]), ({'deadline': 23, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[25, 28], 25, 13, False, False, 25, 3]), ({'deadline': 24, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[26, 29], 26, 14, False, False, 26, 3]), ({'deadline': 25, 'offset_low': 2, 'offset_high': 5, 'policy': 'no-late', 'target_now': 10, 'transport': 2, 'max_wait': 100}, [[27, 30], 27, 15, False, False, 27, 3])]
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[[22, 25], 22, 10, False, False, 22, 3][[22, 25], 22, 10, False, False, 22, 3]Passed
fixture 2[[22, 25], 25, 13, False, False, 25, 3][[22, 25], 25, 13, False, False, 25, 3]Passed
fixture 3[[15, 18], 15, 0, False, True, 15, 3][[15, 18], 15, 0, False, True, 15, 3]Passed
fixture 4[[15, 18], 18, 0, False, True, 21, 3][[15, 18], 18, 0, False, True, 21, 3]Passed
fixture 5[[100, 100], 100, 10, True, False, 10, 0][[100, 100], 100, 10, True, False, 10, 0]Passed
fixture 6[[20, 20], 20, 10, False, False, 20, 0][[20, 20], 20, 10, False, False, 20, 0]Passed
fixture 7[[0, 0], 0, 0, False, True, 0, 0][[0, 0], 0, 0, False, True, 0, 0]Passed
fixture 8[[-8, -5], -5, 4, True, False, -14, 3][[-8, -5], -5, 4, True, False, -14, 3]Passed
fixture 9[[20, 20], 20, 1, False, False, 20, 0][[20, 20], 20, 1, False, False, 20, 0]Passed
fixture 10[[12, 18], 12, 0, False, True, 14, 6][[12, 18], 12, 0, False, True, 14, 6]Passed
variant capture[[23, 26], 23, 11, False, False, 23, 3][[23, 26], 23, 11, False, False, 23, 3]Passed

SHA-256 / 2592163eb3bacca10797f3ca81ebe861ad76856219bff0e1894d1a117186fbe5

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 2026-09-29T14:39:59.035494+00:00.

Case digest / 2dd00dadd3d044e0a8d53c73ee1d7807ac533eb84b5e007839a099a9ee449d72