{"abstract":"The reduction disagrees with its explicit aggregation oracle.","category":"Numerical aggregation","checks":6,"contract":"For lo<=hi, clip each integer contribution into [lo,hi], then sum. Empty is zero.","evaluation_group":"s3-na-clipped-contribution-sum","failed_approach":"Applying the upper cap to the final total misses per-item clipping.","family":"s3-numerical-aggregation-clipped-contribution-sum-lower-only","id":"FA-12901","implementations":{"attempt":{"sha256":"457ed6babac7032fc2768d1b982645bc44fa99dfc6669c77cbc78db9b6680684","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(xs, lo, hi):\n    return min(hi,sum(max(lo,x) for x in xs)) if xs else 0\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([-9, 2, 8], 0, 5)), 7)\ncheck('regression 2', solve(*([], 1, 3)), 0)\ncheck('regression 3', solve(*([1, 2, 3], 0, 5)), 6)\ncheck('regression 4', solve(*([-4, -2], -3, -1)), -5)\ncheck('regression 5', solve(*([0, 0, 9], 2, 4)), 8)\ncheck(\"variable cap\", solve([0,N,N*4],1,N+1),2*N+2)\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":"6241a2d65ec6b5e772c7316916ed7ff67111c5e8aa3b8be1953de4b239cf23b7","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(xs, lo, hi):\n    return sum(max(lo,x) for x in xs)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([-9, 2, 8], 0, 5)), 7)\ncheck('regression 2', solve(*([], 1, 3)), 0)\ncheck('regression 3', solve(*([1, 2, 3], 0, 5)), 6)\ncheck('regression 4', solve(*([-4, -2], -3, -1)), -5)\ncheck('regression 5', solve(*([0, 0, 9], 2, 4)), 8)\ncheck(\"variable cap\", solve([0,N,N*4],1,N+1),2*N+2)\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":"292d2346600b360db4ccacda4326c8fc92cbbdeeb13fc7443e3492b695c3e822","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(xs, lo, hi):\n    return sum(min(hi,max(lo,x)) for x in xs)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([-9, 2, 8], 0, 5)), 7)\ncheck('regression 2', solve(*([], 1, 3)), 0)\ncheck('regression 3', solve(*([1, 2, 3], 0, 5)), 6)\ncheck('regression 4', solve(*([-4, -2], -3, -1)), -5)\ncheck('regression 5', solve(*([0, 0, 9], 2, 4)), 8)\ncheck(\"variable cap\", solve([0,N,N*4],1,N+1),2*N+2)\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-clipped-contribution-sum-lower-only","generated_at":"2026-09-29T14:39:01.375903+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 clipped contribution sum contract at the identified reduction decision.","root_cause":"The upper clipping branch is omitted.","sha256":"508b09a7e9edfa6844f737b980f0474138ad472d1f7130ffad3c6ead5b4ac0e2","title":"Clipped contribution sum: The upper clipping branch is omitted. · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":46.574,"exit_code":1,"observations":[{"actual":5,"check":"regression 1","expected":7,"passed":false},{"actual":0,"check":"regression 2","expected":0,"passed":true},{"actual":5,"check":"regression 3","expected":6,"passed":false},{"actual":-5,"check":"regression 4","expected":-5,"passed":true},{"actual":4,"check":"regression 5","expected":8,"passed":false},{"actual":2,"check":"variable cap","expected":4,"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": 5, \"expected\": 7, \"passed\": false}, {\"check\": \"regression 2\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"regression 3\", \"actual\": 5, \"expected\": 6, \"passed\": false}, {\"check\": \"regression 4\", \"actual\": -5, \"expected\": -5, \"passed\": true}, {\"check\": \"regression 5\", \"actual\": 4, \"expected\": 8, \"passed\": false}, {\"check\": \"variable cap\", \"actual\": 2, \"expected\": 4, \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":47.145,"exit_code":1,"observations":[{"actual":10,"check":"regression 1","expected":7,"passed":false},{"actual":0,"check":"regression 2","expected":0,"passed":true},{"actual":6,"check":"regression 3","expected":6,"passed":true},{"actual":-5,"check":"regression 4","expected":-5,"passed":true},{"actual":13,"check":"regression 5","expected":8,"passed":false},{"actual":6,"check":"variable cap","expected":4,"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": 10, \"expected\": 7, \"passed\": false}, {\"check\": \"regression 2\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"regression 3\", \"actual\": 6, \"expected\": 6, \"passed\": true}, {\"check\": \"regression 4\", \"actual\": -5, \"expected\": -5, \"passed\": true}, {\"check\": \"regression 5\", \"actual\": 13, \"expected\": 8, \"passed\": false}, {\"check\": \"variable cap\", \"actual\": 6, \"expected\": 4, \"passed\": false}], \"passed\": false}\n"},"fixed":{"elapsed_ms":50.698,"exit_code":0,"observations":[{"actual":7,"check":"regression 1","expected":7,"passed":true},{"actual":0,"check":"regression 2","expected":0,"passed":true},{"actual":6,"check":"regression 3","expected":6,"passed":true},{"actual":-5,"check":"regression 4","expected":-5,"passed":true},{"actual":8,"check":"regression 5","expected":8,"passed":true},{"actual":4,"check":"variable cap","expected":4,"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": 7, \"expected\": 7, \"passed\": true}, {\"check\": \"regression 2\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"regression 3\", \"actual\": 6, \"expected\": 6, \"passed\": true}, {\"check\": \"regression 4\", \"actual\": -5, \"expected\": -5, \"passed\": true}, {\"check\": \"regression 5\", \"actual\": 8, \"expected\": 8, \"passed\": true}, {\"check\": \"variable cap\", \"actual\": 4, \"expected\": 4, \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}