{"abstract":"The reduction disagrees with its explicit aggregation oracle.","category":"Numerical aggregation","checks":8,"contract":"Treat samples as frequency vectors over nominal integer labels. Return squared cosine dot**2/(sum count_a**2 * sum count_b**2) as exact Fraction string. A zero norm on either side returns None.","evaluation_group":"s3-na-frequency-cosine-squared","failed_approach":"Counting support labels also replaces vector squared norm with a different quantity.","family":"s3-numerical-aggregation-frequency-cosine-squared-cosine-linear-norm","id":"FA-14116","implementations":{"attempt":{"sha256":"aa6a3ec824d42388634de8264417cb45b72047cfc7f325fa46df102575254167","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(a, b):\n    ca,cb=Counter(a),Counter(b)\n    keys=set(ca)|set(cb)\n    dot=sum(ca[k]*cb[k] for k in keys)\n    na=len(ca)\n    nb=sum(v*v for v in cb.values())\n    return str(Fraction(dot*dot,na*nb)) if na and nb else None\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([1, 1, 2], [1, 2, 2])), '16/25')\ncheck('regression 2', solve(*([1, 2], [1, 2])), '1')\ncheck('regression 3', solve(*([], [])), None)\ncheck('regression 4', solve(*([0], [1])), '0')\ncheck('regression 5', solve(*([1, 1, 1, 2], [1, 2])), '4/5')\ncheck('regression 6', solve(*([-1, 0, 0], [0, 0, 1, 1])), '2/5')\ncheck('regression 7', solve(*([2, 2], [])), None)\ncheck(\"variable cosine concentration\",solve([0]*N+[1],[0]),str(Fraction(N*N,N*N+1)))\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":"7aacca1552cd6604dd51616ec493df394c81ff9c4f5f2761f4c138777078f7ae","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(a, b):\n    ca,cb=Counter(a),Counter(b)\n    keys=set(ca)|set(cb)\n    dot=sum(ca[k]*cb[k] for k in keys)\n    na=sum(ca.values())\n    nb=sum(v*v for v in cb.values())\n    return str(Fraction(dot*dot,na*nb)) if na and nb else None\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([1, 1, 2], [1, 2, 2])), '16/25')\ncheck('regression 2', solve(*([1, 2], [1, 2])), '1')\ncheck('regression 3', solve(*([], [])), None)\ncheck('regression 4', solve(*([0], [1])), '0')\ncheck('regression 5', solve(*([1, 1, 1, 2], [1, 2])), '4/5')\ncheck('regression 6', solve(*([-1, 0, 0], [0, 0, 1, 1])), '2/5')\ncheck('regression 7', solve(*([2, 2], [])), None)\ncheck(\"variable cosine concentration\",solve([0]*N+[1],[0]),str(Fraction(N*N,N*N+1)))\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":"15037d025e834f16d75cb1853d682a3dbb64d80828dd074813a99eb78afcb9d1","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(a, b):\n    ca,cb=Counter(a),Counter(b)\n    keys=set(ca)|set(cb)\n    dot=sum(ca[k]*cb[k] for k in keys)\n    na=sum(v*v for v in ca.values())\n    nb=sum(v*v for v in cb.values())\n    return str(Fraction(dot*dot,na*nb)) if na and nb else None\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([1, 1, 2], [1, 2, 2])), '16/25')\ncheck('regression 2', solve(*([1, 2], [1, 2])), '1')\ncheck('regression 3', solve(*([], [])), None)\ncheck('regression 4', solve(*([0], [1])), '0')\ncheck('regression 5', solve(*([1, 1, 1, 2], [1, 2])), '4/5')\ncheck('regression 6', solve(*([-1, 0, 0], [0, 0, 1, 1])), '2/5')\ncheck('regression 7', solve(*([2, 2], [])), None)\ncheck(\"variable cosine concentration\",solve([0]*N+[1],[0]),str(Fraction(N*N,N*N+1)))\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-frequency-cosine-squared-cosine-linear-norm","generated_at":"2026-09-29T14:39:13.708352+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 frequency cosine squared contract at the identified reduction decision.","root_cause":"The first squared norm uses total frequency rather than squared counts.","sha256":"435a75a245ba04d8a0578597731fc38161dee143ca46821d7ecfbff8412d87e0","title":"Frequency cosine squared: The first squared norm uses total frequency rather than squared counts. · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":43.307,"exit_code":1,"observations":[{"actual":"8/5","check":"regression 1","expected":"16/25","passed":false},{"actual":"1","check":"regression 2","expected":"1","passed":true},{"actual":null,"check":"regression 3","expected":null,"passed":true},{"actual":"0","check":"regression 4","expected":"0","passed":true},{"actual":"4","check":"regression 5","expected":"4/5","passed":false},{"actual":"1","check":"regression 6","expected":"2/5","passed":false},{"actual":null,"check":"regression 7","expected":null,"passed":true},{"actual":"1/2","check":"variable cosine concentration","expected":"1/2","passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": \"8/5\", \"expected\": \"16/25\", \"passed\": false}, {\"check\": \"regression 2\", \"actual\": \"1\", \"expected\": \"1\", \"passed\": true}, {\"check\": \"regression 3\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression 4\", \"actual\": \"0\", \"expected\": \"0\", \"passed\": true}, {\"check\": \"regression 5\", \"actual\": \"4\", \"expected\": \"4/5\", \"passed\": false}, {\"check\": \"regression 6\", \"actual\": \"1\", \"expected\": \"2/5\", \"passed\": false}, {\"check\": \"regression 7\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"variable cosine concentration\", \"actual\": \"1/2\", \"expected\": \"1/2\", \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":43.368,"exit_code":1,"observations":[{"actual":"16/15","check":"regression 1","expected":"16/25","passed":false},{"actual":"1","check":"regression 2","expected":"1","passed":true},{"actual":null,"check":"regression 3","expected":null,"passed":true},{"actual":"0","check":"regression 4","expected":"0","passed":true},{"actual":"2","check":"regression 5","expected":"4/5","passed":false},{"actual":"2/3","check":"regression 6","expected":"2/5","passed":false},{"actual":null,"check":"regression 7","expected":null,"passed":true},{"actual":"1/2","check":"variable cosine concentration","expected":"1/2","passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": \"16/15\", \"expected\": \"16/25\", \"passed\": false}, {\"check\": \"regression 2\", \"actual\": \"1\", \"expected\": \"1\", \"passed\": true}, {\"check\": \"regression 3\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression 4\", \"actual\": \"0\", \"expected\": \"0\", \"passed\": true}, {\"check\": \"regression 5\", \"actual\": \"2\", \"expected\": \"4/5\", \"passed\": false}, {\"check\": \"regression 6\", \"actual\": \"2/3\", \"expected\": \"2/5\", \"passed\": false}, {\"check\": \"regression 7\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"variable cosine concentration\", \"actual\": \"1/2\", \"expected\": \"1/2\", \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":45.279,"exit_code":0,"observations":[{"actual":"16/25","check":"regression 1","expected":"16/25","passed":true},{"actual":"1","check":"regression 2","expected":"1","passed":true},{"actual":null,"check":"regression 3","expected":null,"passed":true},{"actual":"0","check":"regression 4","expected":"0","passed":true},{"actual":"4/5","check":"regression 5","expected":"4/5","passed":true},{"actual":"2/5","check":"regression 6","expected":"2/5","passed":true},{"actual":null,"check":"regression 7","expected":null,"passed":true},{"actual":"1/2","check":"variable cosine concentration","expected":"1/2","passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": \"16/25\", \"expected\": \"16/25\", \"passed\": true}, {\"check\": \"regression 2\", \"actual\": \"1\", \"expected\": \"1\", \"passed\": true}, {\"check\": \"regression 3\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression 4\", \"actual\": \"0\", \"expected\": \"0\", \"passed\": true}, {\"check\": \"regression 5\", \"actual\": \"4/5\", \"expected\": \"4/5\", \"passed\": true}, {\"check\": \"regression 6\", \"actual\": \"2/5\", \"expected\": \"2/5\", \"passed\": true}, {\"check\": \"regression 7\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"variable cosine concentration\", \"actual\": \"1/2\", \"expected\": \"1/2\", \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}