FA-14166 / Numerical aggregation / Open access

Jointly observed pair summary: X subtotal includes rows with missing y. · case 01

The reduction disagrees with its explicit aggregation oracle.

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

ROOT CAUSE

X subtotal includes rows with missing y.

THE FAILURE

X subtotal includes rows with missing y.

Unsuccessful approach: Checking companion truthiness still drops valid x contributions paired with observed zero.

Case contract

Each row holds two integer values or None. Include a row only when both values are observed; zero is observed. Return [paired count, x sum, y sum, cross-product sum, squared-distance sum]. No imputation and no marginal-only contributions.

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(rows):
    pairs=[(x,y) for x,y in rows if x is not None and y is not None]
    return [len(pairs),sum(x for x,y in rows if x is not None),sum(y for x,y in pairs),sum(x*y for x,y in pairs),sum((x-y)**2 for x,y in pairs)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('regression 1', solve(*([(1, 2), (None, 9), (3, None), (0, 4)],)), [2, 1, 6, 2, 17])
check('regression 2', solve(*([],)), [0, 0, 0, 0, 0])
check('regression 3', solve(*([(None, None)],)), [0, 0, 0, 0, 0])
check('regression 4', solve(*([(-2, 3), (4, -1), (0, 0)],)), [3, 2, 2, -10, 50])
check('regression 5', solve(*([(3, 3), (3, 3), (None, 4)],)), [2, 6, 6, 18, 0])
check('regression 6', solve(*([(0, 2), (2, 0)],)), [2, 2, 2, 0, 8])
check('regression 7', solve(*([(5, None), (None, 6)],)), [0, 0, 0, 0, 0])
check("variable complete pair",solve([(N,2*N),(None,N),(0,N)]),[2,N,3*N,2*N*N,2*N*N])
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 fixture	Actual	Expected	Outcome
regression 1	`[2, 4, 6, 2, 17]`	`[2, 1, 6, 2, 17]`	Failed
regression 2	`[0, 0, 0, 0, 0]`	`[0, 0, 0, 0, 0]`	Passed
regression 3	`[0, 0, 0, 0, 0]`	`[0, 0, 0, 0, 0]`	Passed
regression 4	`[3, 2, 2, -10, 50]`	`[3, 2, 2, -10, 50]`	Passed
regression 5	`[2, 6, 6, 18, 0]`	`[2, 6, 6, 18, 0]`	Passed
regression 6	`[2, 2, 2, 0, 8]`	`[2, 2, 2, 0, 8]`	Passed
regression 7	`[0, 5, 0, 0, 0]`	`[0, 0, 0, 0, 0]`	Failed
variable complete pair	`[2, 1, 3, 2, 2]`	`[2, 1, 3, 2, 2]`	Passed

SHA-256 / 00f0939fe58ade695bbb577ddc9448aa40ac49e451040b41d29963c951e12fe7

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(rows):
    pairs=[(x,y) for x,y in rows if x is not None and y is not None]
    return [len(pairs),sum(x for x,y in rows if x is not None and y),sum(y for x,y in pairs),sum(x*y for x,y in pairs),sum((x-y)**2 for x,y in pairs)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('regression 1', solve(*([(1, 2), (None, 9), (3, None), (0, 4)],)), [2, 1, 6, 2, 17])
check('regression 2', solve(*([],)), [0, 0, 0, 0, 0])
check('regression 3', solve(*([(None, None)],)), [0, 0, 0, 0, 0])
check('regression 4', solve(*([(-2, 3), (4, -1), (0, 0)],)), [3, 2, 2, -10, 50])
check('regression 5', solve(*([(3, 3), (3, 3), (None, 4)],)), [2, 6, 6, 18, 0])
check('regression 6', solve(*([(0, 2), (2, 0)],)), [2, 2, 2, 0, 8])
check('regression 7', solve(*([(5, None), (None, 6)],)), [0, 0, 0, 0, 0])
check("variable complete pair",solve([(N,2*N),(None,N),(0,N)]),[2,N,3*N,2*N*N,2*N*N])
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 fixture	Actual	Expected	Outcome
regression 1	`[2, 1, 6, 2, 17]`	`[2, 1, 6, 2, 17]`	Passed
regression 2	`[0, 0, 0, 0, 0]`	`[0, 0, 0, 0, 0]`	Passed
regression 3	`[0, 0, 0, 0, 0]`	`[0, 0, 0, 0, 0]`	Passed
regression 4	`[3, 2, 2, -10, 50]`	`[3, 2, 2, -10, 50]`	Passed
regression 5	`[2, 6, 6, 18, 0]`	`[2, 6, 6, 18, 0]`	Passed
regression 6	`[2, 0, 2, 0, 8]`	`[2, 2, 2, 0, 8]`	Failed
regression 7	`[0, 0, 0, 0, 0]`	`[0, 0, 0, 0, 0]`	Passed
variable complete pair	`[2, 1, 3, 2, 2]`	`[2, 1, 3, 2, 2]`	Passed

SHA-256 / dd769e8f6a56898ba7017cd3ee0731578948933e145db4609787349b31579a7b

HELD IN THE MEMBER ARCHIVE

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

This mechanism has 8 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

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 2026-09-29T14:39:14.395175+00:00.

Case digest / 0006087b60f8660d25f6c91b5abe85c8297cfe92ab9deff64914ce6257152338