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FA-18631 / Time representation / Open access

Invalid or raw wall value contaminates continuity checkpoint · case 01

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

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

ROOT CAUSE

Invalid or raw wall value contaminates continuity checkpoint.

THE FAILURE

Invalid or raw wall value contaminates continuity checkpoint.

Unsuccessful approach: The partial correction still substitutes r['wall'] if valid else r['last_output'] at the same fault site.

Case contract

A presentation clock preserves monotonic output while exposing raw wall coordinate and rollback debt. It advances a floor by monotonic elapsed since previous sample; publish max(raw wall,floor) if continuity mode enabled, otherwise raw wall. On explicit reset, discard old floor. Output adjusted coordinate, added bias, rollback marker, debt, source mode and next checkpoint. Negative elapsed invalidates sample.

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):
    elapsed = r['mono'] - r['last_mono']
    valid = elapsed >= 0
    floor = r['wall'] if r['reset'] else r['last_output'] + max(0,elapsed)
    rollback = r['wall'] < floor
    output = max(r['wall'],floor) if r['continuous'] else r['wall']
    bias = output - r['wall']
    debt = max(0,floor - r['wall'])
    mode = 'floor' if r['continuous'] and rollback else 'wall'
    checkpoint_output = output
    checkpoint_mono = r['mono'] if valid else r['last_mono']
    return [valid,output,bias,debt,mode,[checkpoint_output,checkpoint_mono],rollback]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve({'mono': 20, 'last_mono': 10, 'wall': 105, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}), [True, 110, 5, 5, 'floor', [110, 20], True])
check('fixture 2', solve({'mono': 20, 'last_mono': 10, 'wall': 120, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}), [True, 120, 0, 0, 'wall', [120, 20], False])
check('fixture 3', solve({'mono': 20, 'last_mono': 10, 'wall': 105, 'last_output': 100, 'reset': False, 'continuous': False, 'last_bias': 2}), [True, 105, 0, 5, 'wall', [105, 20], True])
check('fixture 4', solve({'mono': 20, 'last_mono': 10, 'wall': 90, 'last_output': 100, 'reset': True, 'continuous': True, 'last_bias': 2}), [True, 90, 0, 0, 'wall', [90, 20], False])
check('fixture 5', solve({'mono': 10, 'last_mono': 10, 'wall': 100, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}), [True, 100, 0, 0, 'wall', [100, 10], False])
check('fixture 6', solve({'mono': 9, 'last_mono': 10, 'wall': 101, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}), [False, 101, 0, 0, 'wall', [100, 10], False])
check('fixture 7', solve({'mono': 20, 'last_mono': 10, 'wall': 110, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}), [True, 110, 0, 0, 'wall', [110, 20], False])
check('fixture 8', solve({'mono': 20, 'last_mono': 10, 'wall': 90, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}), [True, 110, 20, 20, 'floor', [110, 20], True])
check('fixture 9', solve({'mono': 30, 'last_mono': 10, 'wall': 105, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 5}), [True, 120, 15, 15, 'floor', [120, 30], True])
check('fixture 10', solve({'mono': 10, 'last_mono': 10, 'wall': 120, 'last_output': 100, 'reset': False, 'continuous': False, 'last_bias': 0}), [True, 120, 0, 0, 'wall', [120, 10], False])
variant = [({'mono': 20, 'last_mono': 10, 'wall': 106, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}, [True, 110, 4, 4, 'floor', [110, 20], True]), ({'mono': 20, 'last_mono': 10, 'wall': 107, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}, [True, 110, 3, 3, 'floor', [110, 20], True]), ({'mono': 20, 'last_mono': 10, 'wall': 108, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}, [True, 110, 2, 2, 'floor', [110, 20], True]), ({'mono': 20, 'last_mono': 10, 'wall': 109, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}, [True, 110, 1, 1, 'floor', [110, 20], True]), ({'mono': 20, 'last_mono': 10, 'wall': 110, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}, [True, 110, 0, 0, 'wall', [110, 20], 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[True, 110, 5, 5, 'floor', [110, 20], True][True, 110, 5, 5, 'floor', [110, 20], True]Passed
fixture 2[True, 120, 0, 0, 'wall', [120, 20], False][True, 120, 0, 0, 'wall', [120, 20], False]Passed
fixture 3[True, 105, 0, 5, 'wall', [105, 20], True][True, 105, 0, 5, 'wall', [105, 20], True]Passed
fixture 4[True, 90, 0, 0, 'wall', [90, 20], False][True, 90, 0, 0, 'wall', [90, 20], False]Passed
fixture 5[True, 100, 0, 0, 'wall', [100, 10], False][True, 100, 0, 0, 'wall', [100, 10], False]Passed
fixture 6[False, 101, 0, 0, 'wall', [101, 10], False][False, 101, 0, 0, 'wall', [100, 10], False]Failed
fixture 7[True, 110, 0, 0, 'wall', [110, 20], False][True, 110, 0, 0, 'wall', [110, 20], False]Passed
fixture 8[True, 110, 20, 20, 'floor', [110, 20], True][True, 110, 20, 20, 'floor', [110, 20], True]Passed
fixture 9[True, 120, 15, 15, 'floor', [120, 30], True][True, 120, 15, 15, 'floor', [120, 30], True]Passed
fixture 10[True, 120, 0, 0, 'wall', [120, 10], False][True, 120, 0, 0, 'wall', [120, 10], False]Passed
variant capture[True, 110, 4, 4, 'floor', [110, 20], True][True, 110, 4, 4, 'floor', [110, 20], True]Passed

SHA-256 / d5d5c8d11c709b0d3ffc6cbd08935890ae97b5ad369dd8f538647986af24b48f

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):
    elapsed = r['mono'] - r['last_mono']
    valid = elapsed >= 0
    floor = r['wall'] if r['reset'] else r['last_output'] + max(0,elapsed)
    rollback = r['wall'] < floor
    output = max(r['wall'],floor) if r['continuous'] else r['wall']
    bias = output - r['wall']
    debt = max(0,floor - r['wall'])
    mode = 'floor' if r['continuous'] and rollback else 'wall'
    checkpoint_output = r['wall'] if valid else r['last_output']
    checkpoint_mono = r['mono'] if valid else r['last_mono']
    return [valid,output,bias,debt,mode,[checkpoint_output,checkpoint_mono],rollback]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve({'mono': 20, 'last_mono': 10, 'wall': 105, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}), [True, 110, 5, 5, 'floor', [110, 20], True])
check('fixture 2', solve({'mono': 20, 'last_mono': 10, 'wall': 120, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}), [True, 120, 0, 0, 'wall', [120, 20], False])
check('fixture 3', solve({'mono': 20, 'last_mono': 10, 'wall': 105, 'last_output': 100, 'reset': False, 'continuous': False, 'last_bias': 2}), [True, 105, 0, 5, 'wall', [105, 20], True])
check('fixture 4', solve({'mono': 20, 'last_mono': 10, 'wall': 90, 'last_output': 100, 'reset': True, 'continuous': True, 'last_bias': 2}), [True, 90, 0, 0, 'wall', [90, 20], False])
check('fixture 5', solve({'mono': 10, 'last_mono': 10, 'wall': 100, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}), [True, 100, 0, 0, 'wall', [100, 10], False])
check('fixture 6', solve({'mono': 9, 'last_mono': 10, 'wall': 101, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}), [False, 101, 0, 0, 'wall', [100, 10], False])
check('fixture 7', solve({'mono': 20, 'last_mono': 10, 'wall': 110, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}), [True, 110, 0, 0, 'wall', [110, 20], False])
check('fixture 8', solve({'mono': 20, 'last_mono': 10, 'wall': 90, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}), [True, 110, 20, 20, 'floor', [110, 20], True])
check('fixture 9', solve({'mono': 30, 'last_mono': 10, 'wall': 105, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 5}), [True, 120, 15, 15, 'floor', [120, 30], True])
check('fixture 10', solve({'mono': 10, 'last_mono': 10, 'wall': 120, 'last_output': 100, 'reset': False, 'continuous': False, 'last_bias': 0}), [True, 120, 0, 0, 'wall', [120, 10], False])
variant = [({'mono': 20, 'last_mono': 10, 'wall': 106, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}, [True, 110, 4, 4, 'floor', [110, 20], True]), ({'mono': 20, 'last_mono': 10, 'wall': 107, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}, [True, 110, 3, 3, 'floor', [110, 20], True]), ({'mono': 20, 'last_mono': 10, 'wall': 108, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}, [True, 110, 2, 2, 'floor', [110, 20], True]), ({'mono': 20, 'last_mono': 10, 'wall': 109, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}, [True, 110, 1, 1, 'floor', [110, 20], True]), ({'mono': 20, 'last_mono': 10, 'wall': 110, 'last_output': 100, 'reset': False, 'continuous': True, 'last_bias': 2}, [True, 110, 0, 0, 'wall', [110, 20], 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[True, 110, 5, 5, 'floor', [105, 20], True][True, 110, 5, 5, 'floor', [110, 20], True]Failed
fixture 2[True, 120, 0, 0, 'wall', [120, 20], False][True, 120, 0, 0, 'wall', [120, 20], False]Passed
fixture 3[True, 105, 0, 5, 'wall', [105, 20], True][True, 105, 0, 5, 'wall', [105, 20], True]Passed
fixture 4[True, 90, 0, 0, 'wall', [90, 20], False][True, 90, 0, 0, 'wall', [90, 20], False]Passed
fixture 5[True, 100, 0, 0, 'wall', [100, 10], False][True, 100, 0, 0, 'wall', [100, 10], False]Passed
fixture 6[False, 101, 0, 0, 'wall', [100, 10], False][False, 101, 0, 0, 'wall', [100, 10], False]Passed
fixture 7[True, 110, 0, 0, 'wall', [110, 20], False][True, 110, 0, 0, 'wall', [110, 20], False]Passed
fixture 8[True, 110, 20, 20, 'floor', [90, 20], True][True, 110, 20, 20, 'floor', [110, 20], True]Failed
fixture 9[True, 120, 15, 15, 'floor', [105, 30], True][True, 120, 15, 15, 'floor', [120, 30], True]Failed
fixture 10[True, 120, 0, 0, 'wall', [120, 10], False][True, 120, 0, 0, 'wall', [120, 10], False]Passed
variant capture[True, 110, 4, 4, 'floor', [106, 20], True][True, 110, 4, 4, 'floor', [110, 20], True]Failed

SHA-256 / efa6e8f01b26668c523be0b03d1d918f8790136014cd54f7bc2346bb6b64b15e

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 2026-09-29T14:39:59.623694+00:00.

Case digest / 3d04b6a33f104679f1d1ea047befbb19b60aa5640a54d6528b5b11898a8b5ed2