FAILURE MAP
← Case archive

FA-13586 / Numerical aggregation / Open access

Timestamp half decayed mass: Simultaneous contributions overwrite instead of accumulating. · case 01

The reduction disagrees with its explicit aggregation oracle.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Simultaneous contributions overwrite instead of accumulating.

VERIFIED REPAIR

Preserve the timestamp half decayed mass contract at the identified reduction decision.

Unsuccessful approach: Deduplicating identical events also discards their independent mass.

Case contract

Each [integer timestamp, integer contribution] independently contributes value/2**age at integer query now; exclude future events. Same-time events are independent additive contributions. Return exact Fraction string.

Why this case matters

Exact bounded examples isolate a reduction defect without floating-point or external-service effects.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
from collections import Counter, defaultdict
import math
import itertools
N = 1
observations = []
def solve(events, now):
    out=Fraction(0)
    for t,value in dict(events).items():
        if t>now: continue
        out+=Fraction(value,2**(now-t))
    return str(out)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('regression 1', solve(*([(0, 8), (2, 4)], 3)), '3')
check('regression 2', solve(*([], 4)), '0')
check('regression 3', solve(*([(5, 3)], 4)), '0')
check('regression 4', solve(*([(3, -4), (3, 8)], 3)), '4')
check('regression 5', solve(*([(0, 3), (1, 2), (1, 2)], 2)), '11/4')
check('regression 6', solve(*([(-2, 8), (0, -1)], 1)), '1/2')
check('regression 7', solve(*([(4, 9), (0, 16)], 4)), '10')
check("variable age",solve([(0,2**N)],N),"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
regression 133Passed
regression 200Passed
regression 300Passed
regression 484Failed
regression 57/411/4Failed
regression 61/21/2Passed
regression 71010Passed
variable age11Passed

SHA-256 / 92119d20461f116ed8ac7469d8d35674491b08f1393e10f74164ef8e6b0bee53

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
from collections import Counter, defaultdict
import math
import itertools
N = 1
observations = []
def solve(events, now):
    out=Fraction(0)
    for t,value in set(events):
        if t>now: continue
        out+=Fraction(value,2**(now-t))
    return str(out)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('regression 1', solve(*([(0, 8), (2, 4)], 3)), '3')
check('regression 2', solve(*([], 4)), '0')
check('regression 3', solve(*([(5, 3)], 4)), '0')
check('regression 4', solve(*([(3, -4), (3, 8)], 3)), '4')
check('regression 5', solve(*([(0, 3), (1, 2), (1, 2)], 2)), '11/4')
check('regression 6', solve(*([(-2, 8), (0, -1)], 1)), '1/2')
check('regression 7', solve(*([(4, 9), (0, 16)], 4)), '10')
check("variable age",solve([(0,2**N)],N),"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
regression 133Passed
regression 200Passed
regression 300Passed
regression 444Passed
regression 57/411/4Failed
regression 61/21/2Passed
regression 71010Passed
variable age11Passed

SHA-256 / 6ab670c5eeed587bca073d5f2d602b6aad016b7600a01dbe54930171ea8f163e

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
from collections import Counter, defaultdict
import math
import itertools
N = 1
observations = []
def solve(events, now):
    out=Fraction(0)
    for t,value in events:
        if t>now: continue
        out+=Fraction(value,2**(now-t))
    return str(out)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('regression 1', solve(*([(0, 8), (2, 4)], 3)), '3')
check('regression 2', solve(*([], 4)), '0')
check('regression 3', solve(*([(5, 3)], 4)), '0')
check('regression 4', solve(*([(3, -4), (3, 8)], 3)), '4')
check('regression 5', solve(*([(0, 3), (1, 2), (1, 2)], 2)), '11/4')
check('regression 6', solve(*([(-2, 8), (0, -1)], 1)), '1/2')
check('regression 7', solve(*([(4, 9), (0, 16)], 4)), '10')
check("variable age",solve([(0,2**N)],N),"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
regression 133Passed
regression 200Passed
regression 300Passed
regression 444Passed
regression 511/411/4Passed
regression 61/21/2Passed
regression 71010Passed
variable age11Passed

SHA-256 / 1e2dbd9dc2732119fd3166906d82ac3d8c75f01596f3ed96e684181e9366e44f

Verification & scope

Small offline integer/rational inputs only; no performance, statistical inference, or production-library conformance claim. 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 2026-09-29T14:39:08.611803+00:00.

Case digest / 10ce6a4dd93e00004c65c10d9b5d298631cceba7b50a757c4450c4d169569e23