{"abstract":"The reduction disagrees with its explicit aggregation oracle.","category":"Numerical aggregation","checks":7,"contract":"A stipulated nonnegative weighted count-min table has exactly three independent rows of positive width, with bucket functions k%w,(3*k+1)%w,(k//w)%w for nonnegative integer keys. Add all block contributions, then estimate each query by the minimum of its three cells. Return [table,estimates]; no probabilistic error guarantee is claimed.","evaluation_group":"s3-na-three-row-count-sketch","failed_approach":"Using the first row bucket in every row also misaligns hash coordinates.","family":"s3-numerical-aggregation-three-row-count-sketch-sketch-query-row-misalignment","id":"FA-14276","implementations":{"attempt":{"sha256":"2b9ca727a626c62ccbc89f5b307649c2e805f98f18aa022bf8324fbb22c810bc","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nfrom collections import Counter, defaultdict\nimport math\nimport itertools\nN = 1\nobservations = []\ndef solve(blocks, width, queries):\n    table=[[0]*width for _ in range(3)]\n    for block in blocks:\n        for key,weight in block:\n            buckets=[key%width,(3*key+1)%width,(key//width)%width]\n            for row,col in enumerate(buckets):\n                table[row][col]+=weight\n    estimates=[]\n    for key in queries:\n        buckets=[key%width,(3*key+1)%width,(key//width)%width]\n        estimates.append(min(table[row][buckets[0]] for row,col in enumerate(buckets)))\n    return [table,estimates]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([[(0, 2), (1, 3)], [(4, 1)]], 4, [0, 1, 4, 8])), [[[3, 3, 0, 0], [3, 3, 0, 0], [5, 1, 0, 0]], [3, 3, 1, 0]])\ncheck('regression 2', solve(*([], 3, [0, 1])), [[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [0, 0]])\ncheck('regression 3', solve(*([[(2, 5)], [], [(2, 2)]], 3, [2, 5])), [[[0, 0, 7], [0, 7, 0], [7, 0, 0]], [7, 0]])\ncheck('regression 4', solve(*([[(0, 1), (0, 1)]], 1, [0, 3])), [[[2], [2], [2]], [2, 2]])\ncheck('regression 5', solve(*([[(7, 0), (2, 4), (5, 1)]], 4, [2, 5, 7])), [[[0, 1, 4, 0], [1, 0, 0, 4], [4, 1, 0, 0]], [4, 1, 0]])\ncheck('regression 6', solve(*([[(1, 2)], [(8, 5), (3, 4)]], 5, [1, 8, 3, 13])), [[[0, 2, 0, 9, 0], [9, 0, 0, 0, 2], [6, 5, 0, 0, 0]], [2, 5, 6, 0]])\ncheck(\"variable sketch contribution\",solve([[(0,N)]],2,[0]),[[[N,0],[0,N],[N,0]],[N]])\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"a24281088c55c790eece0c12b13771f7e2b018fdef1ef08ca098062886528591","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nfrom collections import Counter, defaultdict\nimport math\nimport itertools\nN = 1\nobservations = []\ndef solve(blocks, width, queries):\n    table=[[0]*width for _ in range(3)]\n    for block in blocks:\n        for key,weight in block:\n            buckets=[key%width,(3*key+1)%width,(key//width)%width]\n            for row,col in enumerate(buckets):\n                table[row][col]+=weight\n    estimates=[]\n    for key in queries:\n        buckets=[key%width,(3*key+1)%width,(key//width)%width]\n        estimates.append(min(table[(row+1)%3][col] for row,col in enumerate(buckets)))\n    return [table,estimates]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([[(0, 2), (1, 3)], [(4, 1)]], 4, [0, 1, 4, 8])), [[[3, 3, 0, 0], [3, 3, 0, 0], [5, 1, 0, 0]], [3, 3, 1, 0]])\ncheck('regression 2', solve(*([], 3, [0, 1])), [[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [0, 0]])\ncheck('regression 3', solve(*([[(2, 5)], [], [(2, 2)]], 3, [2, 5])), [[[0, 0, 7], [0, 7, 0], [7, 0, 0]], [7, 0]])\ncheck('regression 4', solve(*([[(0, 1), (0, 1)]], 1, [0, 3])), [[[2], [2], [2]], [2, 2]])\ncheck('regression 5', solve(*([[(7, 0), (2, 4), (5, 1)]], 4, [2, 5, 7])), [[[0, 1, 4, 0], [1, 0, 0, 4], [4, 1, 0, 0]], [4, 1, 0]])\ncheck('regression 6', solve(*([[(1, 2)], [(8, 5), (3, 4)]], 5, [1, 8, 3, 13])), [[[0, 2, 0, 9, 0], [9, 0, 0, 0, 2], [6, 5, 0, 0, 0]], [2, 5, 6, 0]])\ncheck(\"variable sketch contribution\",solve([[(0,N)]],2,[0]),[[[N,0],[0,N],[N,0]],[N]])\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"fixed":{"sha256":"c6d9eeecd646bee9d90bdbd7c194e3a43e64dc71d005356f968d60d9e621c585","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nfrom collections import Counter, defaultdict\nimport math\nimport itertools\nN = 1\nobservations = []\ndef solve(blocks, width, queries):\n    table=[[0]*width for _ in range(3)]\n    for block in blocks:\n        for key,weight in block:\n            buckets=[key%width,(3*key+1)%width,(key//width)%width]\n            for row,col in enumerate(buckets):\n                table[row][col]+=weight\n    estimates=[]\n    for key in queries:\n        buckets=[key%width,(3*key+1)%width,(key//width)%width]\n        estimates.append(min(table[row][col] for row,col in enumerate(buckets)))\n    return [table,estimates]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([[(0, 2), (1, 3)], [(4, 1)]], 4, [0, 1, 4, 8])), [[[3, 3, 0, 0], [3, 3, 0, 0], [5, 1, 0, 0]], [3, 3, 1, 0]])\ncheck('regression 2', solve(*([], 3, [0, 1])), [[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [0, 0]])\ncheck('regression 3', solve(*([[(2, 5)], [], [(2, 2)]], 3, [2, 5])), [[[0, 0, 7], [0, 7, 0], [7, 0, 0]], [7, 0]])\ncheck('regression 4', solve(*([[(0, 1), (0, 1)]], 1, [0, 3])), [[[2], [2], [2]], [2, 2]])\ncheck('regression 5', solve(*([[(7, 0), (2, 4), (5, 1)]], 4, [2, 5, 7])), [[[0, 1, 4, 0], [1, 0, 0, 4], [4, 1, 0, 0]], [4, 1, 0]])\ncheck('regression 6', solve(*([[(1, 2)], [(8, 5), (3, 4)]], 5, [1, 8, 3, 13])), [[[0, 2, 0, 9, 0], [9, 0, 0, 0, 2], [6, 5, 0, 0, 0]], [2, 5, 6, 0]])\ncheck(\"variable sketch contribution\",solve([[(0,N)]],2,[0]),[[[N,0],[0,N],[N,0]],[N]])\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"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.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"s3-numerical-aggregation-three-row-count-sketch-sketch-query-row-misalignment","generated_at":"2026-09-29T14:39:15.220485+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Exact bounded examples isolate a reduction defect without floating-point or external-service effects.","repair":"Preserve the three row count sketch contract at the identified reduction decision.","root_cause":"Query buckets are read from the next hash row.","sha256":"9fc2ebdf6379b55d7afa2a77294f0c65149e3f8b18d7ea5d2178d5fb0bb8ee6a","title":"Three row count sketch: Query buckets are read from the next hash row. · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":45.115,"exit_code":1,"observations":[{"actual":[[[3,3,0,0],[3,3,0,0],[5,1,0,0]],[3,1,3,3]],"check":"regression 1","expected":[[[3,3,0,0],[3,3,0,0],[5,1,0,0]],[3,3,1,0]],"passed":false},{"actual":[[[0,0,0],[0,0,0],[0,0,0]],[0,0]],"check":"regression 2","expected":[[[0,0,0],[0,0,0],[0,0,0]],[0,0]],"passed":true},{"actual":[[[0,0,7],[0,7,0],[7,0,0]],[0,0]],"check":"regression 3","expected":[[[0,0,7],[0,7,0],[7,0,0]],[7,0]],"passed":false},{"actual":[[[2],[2],[2]],[2,2]],"check":"regression 4","expected":[[[2],[2],[2]],[2,2]],"passed":true},{"actual":[[[0,1,4,0],[1,0,0,4],[4,1,0,0]],[0,0,0]],"check":"regression 5","expected":[[[0,1,4,0],[1,0,0,4],[4,1,0,0]],[4,1,0]],"passed":false},{"actual":[[[0,2,0,9,0],[9,0,0,0,2],[6,5,0,0,0]],[0,0,0,0]],"check":"regression 6","expected":[[[0,2,0,9,0],[9,0,0,0,2],[6,5,0,0,0]],[2,5,6,0]],"passed":false},{"actual":[[[1,0],[0,1],[1,0]],[0]],"check":"variable sketch contribution","expected":[[[1,0],[0,1],[1,0]],[1]],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": [[[3, 3, 0, 0], [3, 3, 0, 0], [5, 1, 0, 0]], [3, 1, 3, 3]], \"expected\": [[[3, 3, 0, 0], [3, 3, 0, 0], [5, 1, 0, 0]], [3, 3, 1, 0]], \"passed\": false}, {\"check\": \"regression 2\", \"actual\": [[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [0, 0]], \"expected\": [[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [0, 0]], \"passed\": true}, {\"check\": \"regression 3\", \"actual\": [[[0, 0, 7], [0, 7, 0], [7, 0, 0]], [0, 0]], \"expected\": [[[0, 0, 7], [0, 7, 0], [7, 0, 0]], [7, 0]], \"passed\": false}, {\"check\": \"regression 4\", \"actual\": [[[2], [2], [2]], [2, 2]], \"expected\": [[[2], [2], [2]], [2, 2]], \"passed\": true}, {\"check\": \"regression 5\", \"actual\": [[[0, 1, 4, 0], [1, 0, 0, 4], [4, 1, 0, 0]], [0, 0, 0]], \"expected\": [[[0, 1, 4, 0], [1, 0, 0, 4], [4, 1, 0, 0]], [4, 1, 0]], \"passed\": false}, {\"check\": \"regression 6\", \"actual\": [[[0, 2, 0, 9, 0], [9, 0, 0, 0, 2], [6, 5, 0, 0, 0]], [0, 0, 0, 0]], \"expected\": [[[0, 2, 0, 9, 0], [9, 0, 0, 0, 2], [6, 5, 0, 0, 0]], [2, 5, 6, 0]], \"passed\": false}, {\"check\": \"variable sketch contribution\", \"actual\": [[[1, 0], [0, 1], [1, 0]], [0]], \"expected\": [[[1, 0], [0, 1], [1, 0]], [1]], \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":43.865,"exit_code":1,"observations":[{"actual":[[[3,3,0,0],[3,3,0,0],[5,1,0,0]],[1,3,1,0]],"check":"regression 1","expected":[[[3,3,0,0],[3,3,0,0],[5,1,0,0]],[3,3,1,0]],"passed":false},{"actual":[[[0,0,0],[0,0,0],[0,0,0]],[0,0]],"check":"regression 2","expected":[[[0,0,0],[0,0,0],[0,0,0]],[0,0]],"passed":true},{"actual":[[[0,0,7],[0,7,0],[7,0,0]],[0,0]],"check":"regression 3","expected":[[[0,0,7],[0,7,0],[7,0,0]],[7,0]],"passed":false},{"actual":[[[2],[2],[2]],[2,2]],"check":"regression 4","expected":[[[2],[2],[2]],[2,2]],"passed":true},{"actual":[[[0,1,4,0],[1,0,0,4],[4,1,0,0]],[0,0,0]],"check":"regression 5","expected":[[[0,1,4,0],[1,0,0,4],[4,1,0,0]],[4,1,0]],"passed":false},{"actual":[[[0,2,0,9,0],[9,0,0,0,2],[6,5,0,0,0]],[0,0,0,0]],"check":"regression 6","expected":[[[0,2,0,9,0],[9,0,0,0,2],[6,5,0,0,0]],[2,5,6,0]],"passed":false},{"actual":[[[1,0],[0,1],[1,0]],[0]],"check":"variable sketch contribution","expected":[[[1,0],[0,1],[1,0]],[1]],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": [[[3, 3, 0, 0], [3, 3, 0, 0], [5, 1, 0, 0]], [1, 3, 1, 0]], \"expected\": [[[3, 3, 0, 0], [3, 3, 0, 0], [5, 1, 0, 0]], [3, 3, 1, 0]], \"passed\": false}, {\"check\": \"regression 2\", \"actual\": [[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [0, 0]], \"expected\": [[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [0, 0]], \"passed\": true}, {\"check\": \"regression 3\", \"actual\": [[[0, 0, 7], [0, 7, 0], [7, 0, 0]], [0, 0]], \"expected\": [[[0, 0, 7], [0, 7, 0], [7, 0, 0]], [7, 0]], \"passed\": false}, {\"check\": \"regression 4\", \"actual\": [[[2], [2], [2]], [2, 2]], \"expected\": [[[2], [2], [2]], [2, 2]], \"passed\": true}, {\"check\": \"regression 5\", \"actual\": [[[0, 1, 4, 0], [1, 0, 0, 4], [4, 1, 0, 0]], [0, 0, 0]], \"expected\": [[[0, 1, 4, 0], [1, 0, 0, 4], [4, 1, 0, 0]], [4, 1, 0]], \"passed\": false}, {\"check\": \"regression 6\", \"actual\": [[[0, 2, 0, 9, 0], [9, 0, 0, 0, 2], [6, 5, 0, 0, 0]], [0, 0, 0, 0]], \"expected\": [[[0, 2, 0, 9, 0], [9, 0, 0, 0, 2], [6, 5, 0, 0, 0]], [2, 5, 6, 0]], \"passed\": false}, {\"check\": \"variable sketch contribution\", \"actual\": [[[1, 0], [0, 1], [1, 0]], [0]], \"expected\": [[[1, 0], [0, 1], [1, 0]], [1]], \"passed\": false}], \"passed\": false}\n"},"fixed":{"elapsed_ms":41.795,"exit_code":0,"observations":[{"actual":[[[3,3,0,0],[3,3,0,0],[5,1,0,0]],[3,3,1,0]],"check":"regression 1","expected":[[[3,3,0,0],[3,3,0,0],[5,1,0,0]],[3,3,1,0]],"passed":true},{"actual":[[[0,0,0],[0,0,0],[0,0,0]],[0,0]],"check":"regression 2","expected":[[[0,0,0],[0,0,0],[0,0,0]],[0,0]],"passed":true},{"actual":[[[0,0,7],[0,7,0],[7,0,0]],[7,0]],"check":"regression 3","expected":[[[0,0,7],[0,7,0],[7,0,0]],[7,0]],"passed":true},{"actual":[[[2],[2],[2]],[2,2]],"check":"regression 4","expected":[[[2],[2],[2]],[2,2]],"passed":true},{"actual":[[[0,1,4,0],[1,0,0,4],[4,1,0,0]],[4,1,0]],"check":"regression 5","expected":[[[0,1,4,0],[1,0,0,4],[4,1,0,0]],[4,1,0]],"passed":true},{"actual":[[[0,2,0,9,0],[9,0,0,0,2],[6,5,0,0,0]],[2,5,6,0]],"check":"regression 6","expected":[[[0,2,0,9,0],[9,0,0,0,2],[6,5,0,0,0]],[2,5,6,0]],"passed":true},{"actual":[[[1,0],[0,1],[1,0]],[1]],"check":"variable sketch contribution","expected":[[[1,0],[0,1],[1,0]],[1]],"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": [[[3, 3, 0, 0], [3, 3, 0, 0], [5, 1, 0, 0]], [3, 3, 1, 0]], \"expected\": [[[3, 3, 0, 0], [3, 3, 0, 0], [5, 1, 0, 0]], [3, 3, 1, 0]], \"passed\": true}, {\"check\": \"regression 2\", \"actual\": [[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [0, 0]], \"expected\": [[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [0, 0]], \"passed\": true}, {\"check\": \"regression 3\", \"actual\": [[[0, 0, 7], [0, 7, 0], [7, 0, 0]], [7, 0]], \"expected\": [[[0, 0, 7], [0, 7, 0], [7, 0, 0]], [7, 0]], \"passed\": true}, {\"check\": \"regression 4\", \"actual\": [[[2], [2], [2]], [2, 2]], \"expected\": [[[2], [2], [2]], [2, 2]], \"passed\": true}, {\"check\": \"regression 5\", \"actual\": [[[0, 1, 4, 0], [1, 0, 0, 4], [4, 1, 0, 0]], [4, 1, 0]], \"expected\": [[[0, 1, 4, 0], [1, 0, 0, 4], [4, 1, 0, 0]], [4, 1, 0]], \"passed\": true}, {\"check\": \"regression 6\", \"actual\": [[[0, 2, 0, 9, 0], [9, 0, 0, 0, 2], [6, 5, 0, 0, 0]], [2, 5, 6, 0]], \"expected\": [[[0, 2, 0, 9, 0], [9, 0, 0, 0, 2], [6, 5, 0, 0, 0]], [2, 5, 6, 0]], \"passed\": true}, {\"check\": \"variable sketch contribution\", \"actual\": [[[1, 0], [0, 1], [1, 0]], [1]], \"expected\": [[[1, 0], [0, 1], [1, 0]], [1]], \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}