{"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.","contract_signature":"blocks, width, queries","evaluation_group":"s3-na-three-row-count-sketch","failed_approach":"Skipping the first row instead still creates an unupdated zero counter.","family":"s3-numerical-aggregation-three-row-count-sketch-sketch-third-row-skipped","id":"FA-14271","implementations":{"attempt":{"sha256":"5e8730a681d4fa77907a1547b2c52e8b1d7e85f71e66e4bee7391e41e8e3ab78","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 list(enumerate(buckets))[1:]:\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"},"broken":{"sha256":"309fde7607afd7fa3f72ec1c38cdee358c59b85e4cc814018b2a93e236779ad7","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 list(enumerate(buckets))[:2]:\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-third-row-skipped","generated_at":"2026-09-29T14:39:15.451416+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.","root_cause":"Updates omit the third independent hash row.","sha256":"6f39af912220930674156a9d1d7e38314cd42882b522f68df5f93523f7fd4991","title":"Three row count sketch: Updates omit the third independent hash row. · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":42.024,"exit_code":1,"observations":[{"actual":[[[0,0,0,0],[3,3,0,0],[5,1,0,0]],[0,0,0,0]],"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":[[[0,0,0],[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":[[[0],[2],[2]],[0,0]],"check":"regression 4","expected":[[[2],[2],[2]],[2,2]],"passed":false},{"actual":[[[0,0,0,0],[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":[[[0,0,0,0,0],[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":[[[0,0],[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\": [[[0, 0, 0, 0], [3, 3, 0, 0], [5, 1, 0, 0]], [0, 0, 0, 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, 0], [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\": [[[0], [2], [2]], [0, 0]], \"expected\": [[[2], [2], [2]], [2, 2]], \"passed\": false}, {\"check\": \"regression 5\", \"actual\": [[[0, 0, 0, 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, 0, 0, 0, 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\": [[[0, 0], [0, 1], [1, 0]], [0]], \"expected\": [[[1, 0], [0, 1], [1, 0]], [1]], \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":42.625,"exit_code":1,"observations":[{"actual":[[[3,3,0,0],[3,3,0,0],[0,0,0,0]],[0,0,0,0]],"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],[0,0,0]],[0,0]],"check":"regression 3","expected":[[[0,0,7],[0,7,0],[7,0,0]],[7,0]],"passed":false},{"actual":[[[2],[2],[0]],[0,0]],"check":"regression 4","expected":[[[2],[2],[2]],[2,2]],"passed":false},{"actual":[[[0,1,4,0],[1,0,0,4],[0,0,0,0]],[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],[0,0,0,0,0]],[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],[0,0]],[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], [0, 0, 0, 0]], [0, 0, 0, 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], [0, 0, 0]], [0, 0]], \"expected\": [[[0, 0, 7], [0, 7, 0], [7, 0, 0]], [7, 0]], \"passed\": false}, {\"check\": \"regression 4\", \"actual\": [[[2], [2], [0]], [0, 0]], \"expected\": [[[2], [2], [2]], [2, 2]], \"passed\": false}, {\"check\": \"regression 5\", \"actual\": [[[0, 1, 4, 0], [1, 0, 0, 4], [0, 0, 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], [0, 0, 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], [0, 0]], [0]], \"expected\": [[[1, 0], [0, 1], [1, 0]], [1]], \"passed\": false}], \"passed\": false}\n"}},"member_only":{"stages":["fixed"],"fields":["implementations.fixed","verification.fixed","harness","repair"],"note":"The verified repair, its recorded checks, the repair description, and the scoring harness are available to members."}}