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FA-45081 / Data systems / Open access

Constant vector removes repeated gathered rows · case 01

Constant vector removes repeated gathered rows.

Verified by executionVariant 1 · 7 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

constant-vector-materialize: Constant vector removes repeated gathered rows.

THE FAILURE

constant-vector-materialize: Constant vector removes repeated gathered rows.

Unsuccessful approach: Sorting unique row numbers still removes repeated outputs.

Case contract

Materialize a constant vector over a requested logical length, combining scalar validity with row validity. A selection chooses logical rows and may repeat them. Return [row-index,value-or-null] for every selected row; scalar zero is a valid value.

Why this case matters

A bounded deterministic data engine model makes representation and changelog faults reproducible.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(d):
    try:
        value,scalar_valid,row_valid,selection=d
        out=[]
        for row in dict.fromkeys(selection):
            known=scalar_valid and row_valid[row]
            out.append([row,value if known else None])
        return out
    except (IndexError, KeyError, ValueError, StopIteration) as exc:
        return {"representation_error": type(exc).__name__}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
if N == 1:
    check('selected validity', solve([1, True, [True, False, True], [2, 1, 0]]), [[2, 1], [1, None], [0, 1]])
    check('scalar null', solve([1, False, [True, True], [0, 1]]), [[0, None], [1, None]])
    check('mixed row null', solve([1, True, [False, True], [1, 0]]), [[1, 1], [0, None]])
    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])
    check('repeat row', solve([1, True, [True, True], [1, 1]]), [[1, 1], [1, 1]])
    check('empty selection', solve([1, True, [True], []]), [])
    check('single row control', solve([1, True, [True], [0]]), [[0, 1]])
elif N == 2:
    check('selected validity', solve([2, True, [True, False, True], [2, 1, 0]]), [[2, 2], [1, None], [0, 2]])
    check('scalar null', solve([2, False, [True, True], [0, 1]]), [[0, None], [1, None]])
    check('mixed row null', solve([2, True, [False, True], [1, 0]]), [[1, 2], [0, None]])
    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])
    check('repeat row', solve([2, True, [True, True], [1, 1]]), [[1, 2], [1, 2]])
    check('empty selection', solve([2, True, [True], []]), [])
    check('single row control', solve([2, True, [True], [0]]), [[0, 2]])
elif N == 3:
    check('selected validity', solve([3, True, [True, False, True], [2, 1, 0]]), [[2, 3], [1, None], [0, 3]])
    check('scalar null', solve([3, False, [True, True], [0, 1]]), [[0, None], [1, None]])
    check('mixed row null', solve([3, True, [False, True], [1, 0]]), [[1, 3], [0, None]])
    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])
    check('repeat row', solve([3, True, [True, True], [1, 1]]), [[1, 3], [1, 3]])
    check('empty selection', solve([3, True, [True], []]), [])
    check('single row control', solve([3, True, [True], [0]]), [[0, 3]])
elif N == 4:
    check('selected validity', solve([4, True, [True, False, True], [2, 1, 0]]), [[2, 4], [1, None], [0, 4]])
    check('scalar null', solve([4, False, [True, True], [0, 1]]), [[0, None], [1, None]])
    check('mixed row null', solve([4, True, [False, True], [1, 0]]), [[1, 4], [0, None]])
    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])
    check('repeat row', solve([4, True, [True, True], [1, 1]]), [[1, 4], [1, 4]])
    check('empty selection', solve([4, True, [True], []]), [])
    check('single row control', solve([4, True, [True], [0]]), [[0, 4]])
elif N == 5:
    check('selected validity', solve([5, True, [True, False, True], [2, 1, 0]]), [[2, 5], [1, None], [0, 5]])
    check('scalar null', solve([5, False, [True, True], [0, 1]]), [[0, None], [1, None]])
    check('mixed row null', solve([5, True, [False, True], [1, 0]]), [[1, 5], [0, None]])
    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])
    check('repeat row', solve([5, True, [True, True], [1, 1]]), [[1, 5], [1, 5]])
    check('empty selection', solve([5, True, [True], []]), [])
    check('single row control', solve([5, True, [True], [0]]), [[0, 5]])
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
selected validity[[2, 1], [1, None], [0, 1]][[2, 1], [1, None], [0, 1]]Passed
scalar null[[0, None], [1, None]][[0, None], [1, None]]Passed
mixed row null[[1, 1], [0, None]][[1, 1], [0, None]]Passed
zero constant[[0, 0]][[0, 0]]Passed
repeat row[[1, 1]][[1, 1], [1, 1]]Failed
empty selection[][]Passed
single row control[[0, 1]][[0, 1]]Passed

SHA-256 / cf40699369360f7217f4136e7f6314cfa7e503ad625f4af9039eba65df346e59

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(d):
    try:
        value,scalar_valid,row_valid,selection=d
        out=[]
        for row in sorted(set(selection)):
            known=scalar_valid and row_valid[row]
            out.append([row,value if known else None])
        return out
    except (IndexError, KeyError, ValueError, StopIteration) as exc:
        return {"representation_error": type(exc).__name__}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
if N == 1:
    check('selected validity', solve([1, True, [True, False, True], [2, 1, 0]]), [[2, 1], [1, None], [0, 1]])
    check('scalar null', solve([1, False, [True, True], [0, 1]]), [[0, None], [1, None]])
    check('mixed row null', solve([1, True, [False, True], [1, 0]]), [[1, 1], [0, None]])
    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])
    check('repeat row', solve([1, True, [True, True], [1, 1]]), [[1, 1], [1, 1]])
    check('empty selection', solve([1, True, [True], []]), [])
    check('single row control', solve([1, True, [True], [0]]), [[0, 1]])
elif N == 2:
    check('selected validity', solve([2, True, [True, False, True], [2, 1, 0]]), [[2, 2], [1, None], [0, 2]])
    check('scalar null', solve([2, False, [True, True], [0, 1]]), [[0, None], [1, None]])
    check('mixed row null', solve([2, True, [False, True], [1, 0]]), [[1, 2], [0, None]])
    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])
    check('repeat row', solve([2, True, [True, True], [1, 1]]), [[1, 2], [1, 2]])
    check('empty selection', solve([2, True, [True], []]), [])
    check('single row control', solve([2, True, [True], [0]]), [[0, 2]])
elif N == 3:
    check('selected validity', solve([3, True, [True, False, True], [2, 1, 0]]), [[2, 3], [1, None], [0, 3]])
    check('scalar null', solve([3, False, [True, True], [0, 1]]), [[0, None], [1, None]])
    check('mixed row null', solve([3, True, [False, True], [1, 0]]), [[1, 3], [0, None]])
    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])
    check('repeat row', solve([3, True, [True, True], [1, 1]]), [[1, 3], [1, 3]])
    check('empty selection', solve([3, True, [True], []]), [])
    check('single row control', solve([3, True, [True], [0]]), [[0, 3]])
elif N == 4:
    check('selected validity', solve([4, True, [True, False, True], [2, 1, 0]]), [[2, 4], [1, None], [0, 4]])
    check('scalar null', solve([4, False, [True, True], [0, 1]]), [[0, None], [1, None]])
    check('mixed row null', solve([4, True, [False, True], [1, 0]]), [[1, 4], [0, None]])
    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])
    check('repeat row', solve([4, True, [True, True], [1, 1]]), [[1, 4], [1, 4]])
    check('empty selection', solve([4, True, [True], []]), [])
    check('single row control', solve([4, True, [True], [0]]), [[0, 4]])
elif N == 5:
    check('selected validity', solve([5, True, [True, False, True], [2, 1, 0]]), [[2, 5], [1, None], [0, 5]])
    check('scalar null', solve([5, False, [True, True], [0, 1]]), [[0, None], [1, None]])
    check('mixed row null', solve([5, True, [False, True], [1, 0]]), [[1, 5], [0, None]])
    check('zero constant', solve([0, True, [True], [0]]), [[0, 0]])
    check('repeat row', solve([5, True, [True, True], [1, 1]]), [[1, 5], [1, 5]])
    check('empty selection', solve([5, True, [True], []]), [])
    check('single row control', solve([5, True, [True], [0]]), [[0, 5]])
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
selected validity[[0, 1], [1, None], [2, 1]][[2, 1], [1, None], [0, 1]]Failed
scalar null[[0, None], [1, None]][[0, None], [1, None]]Passed
mixed row null[[0, None], [1, 1]][[1, 1], [0, None]]Failed
zero constant[[0, 0]][[0, 0]]Passed
repeat row[[1, 1]][[1, 1], [1, 1]]Failed
empty selection[][]Passed
single row control[[0, 1]][[0, 1]]Passed

SHA-256 / 984c364e4abb4f9b46d6c1d65b02e9dac856a997e628e061f69091d2fb09f9d2

HELD IN THE MEMBER ARCHIVE

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

This mechanism has 7 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

Offline stipulated semantics over valid small inputs; no performance, concurrency, or production-engine 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 2026-09-29T14:44:18.642829+00:00.

Case digest / 4d5f20fa0ab3fc1c2fcb8de0df3cc6bd1ef57e1a6baa57804f2bb8e2e8044c90