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FA-18611 / Time representation / Open access

Presentation continuity ignores raw mode or selects backward floor · case 01

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

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

ROOT CAUSE

Presentation continuity ignores raw mode or selects backward floor.

THE FAILURE

Presentation continuity ignores raw mode or selects backward floor.

Unsuccessful approach: The partial correction still substitutes min(r['wall'],floor) if r['continuous'] else r['wall'] 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)
    bias = output - r['wall']
    debt = max(0,floor - r['wall'])
    mode = 'floor' if r['continuous'] and rollback else 'wall'
    checkpoint_output = output 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', [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, 110, 5, 5, 'wall', [110, 20], True][True, 105, 0, 5, 'wall', [105, 20], True]Failed
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', [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 / e6dc8905344c442fa47d28f458f136c7347a3e58358c7ff36fb7e7bbf61d6332

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 = min(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 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, 105, 0, 5, 'floor', [105, 20], True][True, 110, 5, 5, 'floor', [110, 20], True]Failed
fixture 2[True, 110, -10, 0, 'wall', [110, 20], False][True, 120, 0, 0, 'wall', [120, 20], False]Failed
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, 100, -1, 0, 'wall', [100, 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, 90, 0, 20, 'floor', [90, 20], True][True, 110, 20, 20, 'floor', [110, 20], True]Failed
fixture 9[True, 105, 0, 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, 106, 0, 4, 'floor', [106, 20], True][True, 110, 4, 4, 'floor', [110, 20], True]Failed

SHA-256 / 810f65c39c461d1a00c8535322eeadf41c5b349332867fd8b89b9f7f4cb5993c

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 / 043448aa58430eb0b1051a8aacdcfcb0138caa0fa2223dd5c28f3257adabfef8