FA-13241 / Numerical aggregation / Open access

Positive run block summary: Zero is counted as a positive observation. · case 01

The reduction disagrees with its explicit aggregation oracle.

Verified by executionVariant 1 · 10 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Zero is counted as a positive observation.

THE FAILURE

Zero is counted as a positive observation.

Unsuccessful approach: Treating only zeros as breaks accepts negative observations.

Case contract

Return [item count, positive prefix length, positive suffix length, longest strictly positive contiguous run] over ordered integer blocks. Empty blocks are identity, zero breaks a run.

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(blocks):
    total=0
    previous=None
    prefix=suffix=best=0
    all_positive=True
    for block in blocks:
        k=len(block)
        p=next((i for i,x in enumerate(block) if x<0),k)
        s=next((i for i,x in enumerate(reversed(block)) if x<0),k)
        local=run=0
        for x in block:
            run=run+1 if x>0 else 0
            local=max(local,run)
        best=max(best,local,suffix+p)
        if all_positive: prefix+=p
        suffix=suffix+k if s==k else s
        all_positive=all_positive and p==k
        total+=k
    return [total,prefix,suffix,best]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('regression 1', solve(*([[1, 1, 0, 1, 1, 0]],)), [6, 2, 0, 2])
check('regression 2', solve(*([[1, 0, 1, 0, 1, 1, 0]],)), [7, 1, 0, 2])
check('regression 3', solve(*([[1, 2], [3, 0, 4]],)), [5, 3, 1, 3])
check('regression 4', solve(*([[1, 0, 2], [3, 4, -1, 5]],)), [7, 1, 1, 3])
check('regression 5', solve(*([],)), [0, 0, 0, 0])
check('regression 6', solve(*([[], [0, 0], []],)), [2, 0, 0, 0])
check('regression 7', solve(*([[1, 2], [], [3, 4]],)), [4, 4, 4, 4])
check('regression 8', solve(*([[-1, 2, 3], [4, -1], [-2, 1]],)), [7, 0, 1, 3])
check('regression 9', solve(*([[0], [1, 1, 1], [1, 0]],)), [6, 0, 0, 4])
check("variable positive run",solve([[1]*N,[2]*N+[0]]),[2*N+1,2*N,0,2*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	`[6, 6, 6, 6]`	`[6, 2, 0, 2]`	Failed
regression 2	`[7, 7, 7, 7]`	`[7, 1, 0, 2]`	Failed
regression 3	`[5, 5, 5, 5]`	`[5, 3, 1, 3]`	Failed
regression 4	`[7, 5, 1, 5]`	`[7, 1, 1, 3]`	Failed
regression 5	`[0, 0, 0, 0]`	`[0, 0, 0, 0]`	Passed
regression 6	`[2, 2, 2, 2]`	`[2, 0, 0, 0]`	Failed
regression 7	`[4, 4, 4, 4]`	`[4, 4, 4, 4]`	Passed
regression 8	`[7, 0, 1, 3]`	`[7, 0, 1, 3]`	Passed
regression 9	`[6, 6, 6, 6]`	`[6, 0, 0, 4]`	Failed
variable positive run	`[3, 3, 3, 3]`	`[3, 2, 0, 2]`	Failed

SHA-256 / 10b8bcd6e3513126017f6d6959aed16bc1f88d7815bafdf2a17e67720258dde6

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(blocks):
    total=0
    previous=None
    prefix=suffix=best=0
    all_positive=True
    for block in blocks:
        k=len(block)
        p=next((i for i,x in enumerate(block) if x==0),k)
        s=next((i for i,x in enumerate(reversed(block)) if x==0),k)
        local=run=0
        for x in block:
            run=run+1 if x>0 else 0
            local=max(local,run)
        best=max(best,local,suffix+p)
        if all_positive: prefix+=p
        suffix=suffix+k if s==k else s
        all_positive=all_positive and p==k
        total+=k
    return [total,prefix,suffix,best]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('regression 1', solve(*([[1, 1, 0, 1, 1, 0]],)), [6, 2, 0, 2])
check('regression 2', solve(*([[1, 0, 1, 0, 1, 1, 0]],)), [7, 1, 0, 2])
check('regression 3', solve(*([[1, 2], [3, 0, 4]],)), [5, 3, 1, 3])
check('regression 4', solve(*([[1, 0, 2], [3, 4, -1, 5]],)), [7, 1, 1, 3])
check('regression 5', solve(*([],)), [0, 0, 0, 0])
check('regression 6', solve(*([[], [0, 0], []],)), [2, 0, 0, 0])
check('regression 7', solve(*([[1, 2], [], [3, 4]],)), [4, 4, 4, 4])
check('regression 8', solve(*([[-1, 2, 3], [4, -1], [-2, 1]],)), [7, 0, 1, 3])
check('regression 9', solve(*([[0], [1, 1, 1], [1, 0]],)), [6, 0, 0, 4])
check("variable positive run",solve([[1]*N,[2]*N+[0]]),[2*N+1,2*N,0,2*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	`[6, 2, 0, 2]`	`[6, 2, 0, 2]`	Passed
regression 2	`[7, 1, 0, 2]`	`[7, 1, 0, 2]`	Passed
regression 3	`[5, 3, 1, 3]`	`[5, 3, 1, 3]`	Passed
regression 4	`[7, 1, 5, 5]`	`[7, 1, 1, 3]`	Failed
regression 5	`[0, 0, 0, 0]`	`[0, 0, 0, 0]`	Passed
regression 6	`[2, 0, 0, 0]`	`[2, 0, 0, 0]`	Passed
regression 7	`[4, 4, 4, 4]`	`[4, 4, 4, 4]`	Passed
regression 8	`[7, 7, 7, 7]`	`[7, 0, 1, 3]`	Failed
regression 9	`[6, 0, 0, 4]`	`[6, 0, 0, 4]`	Passed
variable positive run	`[3, 2, 0, 2]`	`[3, 2, 0, 2]`	Passed

SHA-256 / 44c16c5ef3de93dec51ebab5622f01620005b2f84713bd9755347c34f4caba82

HELD IN THE MEMBER ARCHIVE

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

This mechanism has 10 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.

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 2026-09-29T14:39:05.182265+00:00.

Case digest / 6e2e50073664ddea83aec9645021ad9805ebf5a9336f834586bf1d7efc99846b