FA-12911 / Numerical aggregation / Open access
Clipped contribution sum: Symmetric magnitude clipping is used for asymmetric endpoints. · case 01
The reduction disagrees with its explicit aggregation oracle.
ROOT CAUSE
Symmetric magnitude clipping is used for asymmetric endpoints.
THE FAILURE
Symmetric magnitude clipping is used for asymmetric endpoints.
Unsuccessful approach: Using the lower endpoint magnitude as both bounds also assumes symmetry.
Case contract
For lo<=hi, clip each integer contribution into [lo,hi], then sum. Empty is zero.
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(xs, lo, hi):
return sum(max(-hi,min(hi,x)) for x in xs)
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('regression 1', solve(*([-9, 2, 8], 0, 5)), 7)
check('regression 2', solve(*([], 1, 3)), 0)
check('regression 3', solve(*([1, 2, 3], 0, 5)), 6)
check('regression 4', solve(*([-4, -2], -3, -1)), -5)
check('regression 5', solve(*([0, 0, 9], 2, 4)), 8)
check("variable cap", solve([0,N,N*4],1,N+1),2*N+2)
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 | 7 | Failed |
| regression 2 | 0 | 0 | Passed |
| regression 3 | 6 | 6 | Passed |
| regression 4 | 2 | -5 | Failed |
| regression 5 | 4 | 8 | Failed |
| variable cap | 3 | 4 | Failed |
SHA-256 / fb6c80cfa0abfe86a2580e21b6252752145bfac7a97954e7a5acaa1a386ef9d8
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(xs, lo, hi):
return sum(max(lo,min(-lo,x)) for x in xs)
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('regression 1', solve(*([-9, 2, 8], 0, 5)), 7)
check('regression 2', solve(*([], 1, 3)), 0)
check('regression 3', solve(*([1, 2, 3], 0, 5)), 6)
check('regression 4', solve(*([-4, -2], -3, -1)), -5)
check('regression 5', solve(*([0, 0, 9], 2, 4)), 8)
check("variable cap", solve([0,N,N*4],1,N+1),2*N+2)
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 | 0 | 7 | Failed |
| regression 2 | 0 | 0 | Passed |
| regression 3 | 0 | 6 | Failed |
| regression 4 | -5 | -5 | Passed |
| regression 5 | 6 | 8 | Failed |
| variable cap | 3 | 4 | Failed |
SHA-256 / f866995769e6c129a1f6c05eca9c978c11008c0fb80b518d538755b5bfb7bd30
HELD IN THE MEMBER ARCHIVE
The verified repair and its recorded checks are member-only.
This mechanism has 6 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.
Member access is invitation-based. Sign in with your invited account to inspect the repair.
Sign in to the archive ↗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 2026-09-29T14:39:01.597851+00:00.
Case digest / 81ca238df841bb701ae31d4c18a68040c286c3addbb88f63b83222de0ecb6494