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FA-18046 / Time representation / Open access

Logical ownership treats older local physical state as current · case 01

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

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

ROOT CAUSE

Logical ownership treats older local physical state as current.

THE FAILURE

Logical ownership treats older local physical state as current.

Unsuccessful approach: The partial correction still substitutes physical > r['local_p'] 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 // 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, 8], 3, True, True, True][[10, 3], 2, True, True, True]Failed
fixture 4[[11, 6], 5, True, True, False][[11, 0], 4, True, True, False]Failed
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, 201], 3, True, True, True][[2, 1], 2, True, True, True]Failed
fixture 9[[2, 1], 1, True, True, True][[2, 1], 1, True, True, True]Passed
fixture 10[[1, 1], 5, True, True, False][[1, 0], 4, True, True, False]Failed
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 / 18ef7a90752a94a6b82db12e9bead4401e9737afaaffee8388d1a7ecaf8d5479

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 // 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], 2, True, True, True][[10, 5], 3, True, True, True]Failed
fixture 2[[10, 0], 0, False, True, True][[10, 6], 1, True, True, True]Failed
fixture 3[[10, 8], 3, True, True, True][[10, 3], 2, True, True, True]Failed
fixture 4[[11, 6], 5, True, True, False][[11, 0], 4, True, True, False]Failed
fixture 5[[10, 3], 6, False, True, False][[11, 0], 7, True, True, True]Failed
fixture 6[[11, 0], 2, True, True, True][[11, 0], 3, True, True, True]Failed
fixture 7[[10, 1], 6, True, True, False][[10, 1], 7, True, True, False]Failed
fixture 8[[2, 201], 3, True, True, True][[2, 1], 2, True, True, True]Failed
fixture 9[[2, 0], 0, False, True, True][[2, 1], 1, True, True, True]Failed
fixture 10[[1, 1], 5, True, True, False][[1, 0], 4, True, True, False]Failed
fixture 11[[10, 1], 2, False, True, True][[10, 255], 3, True, True, True]Failed
variant capture[[10, 5], 6, True, True, False][[10, 5], 7, True, True, False]Failed

SHA-256 / 324cecaa4c0c5e408ddbde3a3afa98acd2fa83590b65b227d1be6a725098631a

HELD IN THE MEMBER ARCHIVE

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

This mechanism has 12 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 2026-09-29T14:39:54.020599+00:00.

Case digest / 2cce4c68b7d1d84c209d333ce7b75ebf74c66eee9ba59c02dc1c785153f8a5ce