FAILURE MAP
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FA-18061 / Time representation / Open access

Hybrid logical carry uses the largest label rather than counter capacity · case 01

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

Verified by executionVariant 1 · 12 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Hybrid logical carry uses the largest label rather than counter capacity.

VERIFIED REPAIR

Preserve the declared coordinate and state contract at carry: carry = logical // 256.

Unsuccessful approach: The partial correction still substitutes logical // 257 at the same fault site.

Case contract

A bounded hybrid logical timestamp has physical integer p and logical integer l, cap 255. On receive, p=max(local p,remote p,wall). Increment max logical when both timestamps share p, increment owning side when only one shares p, otherwise reset logical to zero. Carry logical overflow into physical. Return canonical pair, physical source mask, ordering versus inputs and wall-advance flag.

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):
    physical = max(r['local_p'],r['remote_p'],r['wall'])
    local_owns = physical == r['local_p']
    remote_owns = physical == r['remote_p']
    logical = max(r['local_l'],r['remote_l']) + 1 if local_owns and remote_owns else (r['local_l'] + 1 if local_owns else (r['remote_l'] + 1 if remote_owns else 0))
    carry = logical // 255
    normalized_p = physical + carry
    normalized_l = logical % 256
    mask = int(local_owns) + 2 * int(remote_owns) + 4 * int(physical == r['wall'])
    after_local = (normalized_p,normalized_l) > (r['local_p'],r['local_l'])
    after_remote = (normalized_p,normalized_l) > (r['remote_p'],r['remote_l'])
    return [[normalized_p,normalized_l],mask,after_local,after_remote,normalized_p > r['wall']]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 9}), [[10, 5], 3, True, True, True])
check('fixture 2', solve({'local_p': 10, 'local_l': 5, 'remote_p': 9, 'remote_l': 8, 'wall': 8}), [[10, 6], 1, True, True, True])
check('fixture 3', solve({'local_p': 9, 'local_l': 7, 'remote_p': 10, 'remote_l': 2, 'wall': 8}), [[10, 3], 2, True, True, True])
check('fixture 4', solve({'local_p': 10, 'local_l': 5, 'remote_p': 10, 'remote_l': 7, 'wall': 11}), [[11, 0], 4, True, True, False])
check('fixture 5', solve({'local_p': 10, 'local_l': 255, 'remote_p': 10, 'remote_l': 2, 'wall': 10}), [[11, 0], 7, True, True, True])
check('fixture 6', solve({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 255, 'wall': 9}), [[11, 0], 3, True, True, True])
check('fixture 7', solve({'local_p': 10, 'local_l': 0, 'remote_p': 10, 'remote_l': 0, 'wall': 10}), [[10, 1], 7, True, True, False])
check('fixture 8', solve({'local_p': 1, 'local_l': 200, 'remote_p': 2, 'remote_l': 0, 'wall': 1}), [[2, 1], 2, True, True, True])
check('fixture 9', solve({'local_p': 2, 'local_l': 0, 'remote_p': 1, 'remote_l': 200, 'wall': 1}), [[2, 1], 1, True, True, True])
check('fixture 10', solve({'local_p': 0, 'local_l': 0, 'remote_p': 0, 'remote_l': 0, 'wall': 1}), [[1, 0], 4, True, True, False])
check('fixture 11', solve({'local_p': 10, 'local_l': 254, 'remote_p': 10, 'remote_l': 0, 'wall': 9}), [[10, 255], 3, True, True, True])
variant = [({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 10}, [[10, 5], 7, True, True, False]), ({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 11}, [[11, 0], 4, True, True, False]), ({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 12}, [[12, 0], 4, True, True, False]), ({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 13}, [[13, 0], 4, True, True, False]), ({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 14}, [[14, 0], 4, True, True, False])]
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[[10, 5], 3, True, True, True][[10, 5], 3, True, True, True]Passed
fixture 2[[10, 6], 1, True, True, True][[10, 6], 1, True, True, True]Passed
fixture 3[[10, 3], 2, True, True, True][[10, 3], 2, True, True, True]Passed
fixture 4[[11, 0], 4, True, True, False][[11, 0], 4, True, True, False]Passed
fixture 5[[11, 0], 7, True, True, True][[11, 0], 7, True, True, True]Passed
fixture 6[[11, 0], 3, True, True, True][[11, 0], 3, True, True, True]Passed
fixture 7[[10, 1], 7, True, True, False][[10, 1], 7, True, True, False]Passed
fixture 8[[2, 1], 2, True, True, True][[2, 1], 2, True, True, True]Passed
fixture 9[[2, 1], 1, True, True, True][[2, 1], 1, True, True, True]Passed
fixture 10[[1, 0], 4, True, True, False][[1, 0], 4, True, True, False]Passed
fixture 11[[11, 255], 3, True, True, True][[10, 255], 3, True, True, True]Failed
variant capture[[10, 5], 7, True, True, False][[10, 5], 7, True, True, False]Passed

SHA-256 / aaaf28b73ca360239e6f89d496ac5b7e9de407cb31465bb22adce74642deb592

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):
    physical = max(r['local_p'],r['remote_p'],r['wall'])
    local_owns = physical == r['local_p']
    remote_owns = physical == r['remote_p']
    logical = max(r['local_l'],r['remote_l']) + 1 if local_owns and remote_owns else (r['local_l'] + 1 if local_owns else (r['remote_l'] + 1 if remote_owns else 0))
    carry = logical // 257
    normalized_p = physical + carry
    normalized_l = logical % 256
    mask = int(local_owns) + 2 * int(remote_owns) + 4 * int(physical == r['wall'])
    after_local = (normalized_p,normalized_l) > (r['local_p'],r['local_l'])
    after_remote = (normalized_p,normalized_l) > (r['remote_p'],r['remote_l'])
    return [[normalized_p,normalized_l],mask,after_local,after_remote,normalized_p > r['wall']]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 9}), [[10, 5], 3, True, True, True])
check('fixture 2', solve({'local_p': 10, 'local_l': 5, 'remote_p': 9, 'remote_l': 8, 'wall': 8}), [[10, 6], 1, True, True, True])
check('fixture 3', solve({'local_p': 9, 'local_l': 7, 'remote_p': 10, 'remote_l': 2, 'wall': 8}), [[10, 3], 2, True, True, True])
check('fixture 4', solve({'local_p': 10, 'local_l': 5, 'remote_p': 10, 'remote_l': 7, 'wall': 11}), [[11, 0], 4, True, True, False])
check('fixture 5', solve({'local_p': 10, 'local_l': 255, 'remote_p': 10, 'remote_l': 2, 'wall': 10}), [[11, 0], 7, True, True, True])
check('fixture 6', solve({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 255, 'wall': 9}), [[11, 0], 3, True, True, True])
check('fixture 7', solve({'local_p': 10, 'local_l': 0, 'remote_p': 10, 'remote_l': 0, 'wall': 10}), [[10, 1], 7, True, True, False])
check('fixture 8', solve({'local_p': 1, 'local_l': 200, 'remote_p': 2, 'remote_l': 0, 'wall': 1}), [[2, 1], 2, True, True, True])
check('fixture 9', solve({'local_p': 2, 'local_l': 0, 'remote_p': 1, 'remote_l': 200, 'wall': 1}), [[2, 1], 1, True, True, True])
check('fixture 10', solve({'local_p': 0, 'local_l': 0, 'remote_p': 0, 'remote_l': 0, 'wall': 1}), [[1, 0], 4, True, True, False])
check('fixture 11', solve({'local_p': 10, 'local_l': 254, 'remote_p': 10, 'remote_l': 0, 'wall': 9}), [[10, 255], 3, True, True, True])
variant = [({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 10}, [[10, 5], 7, True, True, False]), ({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 11}, [[11, 0], 4, True, True, False]), ({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 12}, [[12, 0], 4, True, True, False]), ({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 13}, [[13, 0], 4, True, True, False]), ({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 14}, [[14, 0], 4, True, True, False])]
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[[10, 5], 3, True, True, True][[10, 5], 3, True, True, True]Passed
fixture 2[[10, 6], 1, True, True, True][[10, 6], 1, True, True, True]Passed
fixture 3[[10, 3], 2, True, True, True][[10, 3], 2, True, True, True]Passed
fixture 4[[11, 0], 4, True, True, False][[11, 0], 4, True, True, False]Passed
fixture 5[[10, 0], 7, False, False, False][[11, 0], 7, True, True, True]Failed
fixture 6[[10, 0], 3, False, False, True][[11, 0], 3, True, True, True]Failed
fixture 7[[10, 1], 7, True, True, False][[10, 1], 7, True, True, False]Passed
fixture 8[[2, 1], 2, True, True, True][[2, 1], 2, True, True, True]Passed
fixture 9[[2, 1], 1, True, True, True][[2, 1], 1, True, True, True]Passed
fixture 10[[1, 0], 4, True, True, False][[1, 0], 4, True, True, False]Passed
fixture 11[[10, 255], 3, True, True, True][[10, 255], 3, True, True, True]Passed
variant capture[[10, 5], 7, True, True, False][[10, 5], 7, True, True, False]Passed

SHA-256 / cfffb115d5e544c52af381d69108b15a8220b5325205114942732db51c449b0a

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):
    physical = max(r['local_p'],r['remote_p'],r['wall'])
    local_owns = physical == r['local_p']
    remote_owns = physical == r['remote_p']
    logical = max(r['local_l'],r['remote_l']) + 1 if local_owns and remote_owns else (r['local_l'] + 1 if local_owns else (r['remote_l'] + 1 if remote_owns else 0))
    carry = logical // 256
    normalized_p = physical + carry
    normalized_l = logical % 256
    mask = int(local_owns) + 2 * int(remote_owns) + 4 * int(physical == r['wall'])
    after_local = (normalized_p,normalized_l) > (r['local_p'],r['local_l'])
    after_remote = (normalized_p,normalized_l) > (r['remote_p'],r['remote_l'])
    return [[normalized_p,normalized_l],mask,after_local,after_remote,normalized_p > r['wall']]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 9}), [[10, 5], 3, True, True, True])
check('fixture 2', solve({'local_p': 10, 'local_l': 5, 'remote_p': 9, 'remote_l': 8, 'wall': 8}), [[10, 6], 1, True, True, True])
check('fixture 3', solve({'local_p': 9, 'local_l': 7, 'remote_p': 10, 'remote_l': 2, 'wall': 8}), [[10, 3], 2, True, True, True])
check('fixture 4', solve({'local_p': 10, 'local_l': 5, 'remote_p': 10, 'remote_l': 7, 'wall': 11}), [[11, 0], 4, True, True, False])
check('fixture 5', solve({'local_p': 10, 'local_l': 255, 'remote_p': 10, 'remote_l': 2, 'wall': 10}), [[11, 0], 7, True, True, True])
check('fixture 6', solve({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 255, 'wall': 9}), [[11, 0], 3, True, True, True])
check('fixture 7', solve({'local_p': 10, 'local_l': 0, 'remote_p': 10, 'remote_l': 0, 'wall': 10}), [[10, 1], 7, True, True, False])
check('fixture 8', solve({'local_p': 1, 'local_l': 200, 'remote_p': 2, 'remote_l': 0, 'wall': 1}), [[2, 1], 2, True, True, True])
check('fixture 9', solve({'local_p': 2, 'local_l': 0, 'remote_p': 1, 'remote_l': 200, 'wall': 1}), [[2, 1], 1, True, True, True])
check('fixture 10', solve({'local_p': 0, 'local_l': 0, 'remote_p': 0, 'remote_l': 0, 'wall': 1}), [[1, 0], 4, True, True, False])
check('fixture 11', solve({'local_p': 10, 'local_l': 254, 'remote_p': 10, 'remote_l': 0, 'wall': 9}), [[10, 255], 3, True, True, True])
variant = [({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 10}, [[10, 5], 7, True, True, False]), ({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 11}, [[11, 0], 4, True, True, False]), ({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 12}, [[12, 0], 4, True, True, False]), ({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 13}, [[13, 0], 4, True, True, False]), ({'local_p': 10, 'local_l': 2, 'remote_p': 10, 'remote_l': 4, 'wall': 14}, [[14, 0], 4, True, True, False])]
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[[10, 5], 3, True, True, True][[10, 5], 3, True, True, True]Passed
fixture 2[[10, 6], 1, True, True, True][[10, 6], 1, True, True, True]Passed
fixture 3[[10, 3], 2, True, True, True][[10, 3], 2, True, True, True]Passed
fixture 4[[11, 0], 4, True, True, False][[11, 0], 4, True, True, False]Passed
fixture 5[[11, 0], 7, True, True, True][[11, 0], 7, True, True, True]Passed
fixture 6[[11, 0], 3, True, True, True][[11, 0], 3, True, True, True]Passed
fixture 7[[10, 1], 7, True, True, False][[10, 1], 7, True, True, False]Passed
fixture 8[[2, 1], 2, True, True, True][[2, 1], 2, True, True, True]Passed
fixture 9[[2, 1], 1, True, True, True][[2, 1], 1, True, True, True]Passed
fixture 10[[1, 0], 4, True, True, False][[1, 0], 4, True, True, False]Passed
fixture 11[[10, 255], 3, True, True, True][[10, 255], 3, True, True, True]Passed
variant capture[[10, 5], 7, True, True, False][[10, 5], 7, True, True, False]Passed

SHA-256 / cd4d4744632e99dd36ab227866c5362b6e92d6517fb14b53867ac83003e5f4a1

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 2026-09-29T14:39:53.837338+00:00.

Case digest / 455af28fea314dcfc5a4c19a9ed4facd88f9c6e95419199b3157c99e4d0e4905