{"abstract":"The reduction disagrees with its explicit aggregation oracle.","category":"Numerical aggregation","checks":8,"contract":"Expand nonnegative integer frequencies. Remove exactly k lowest and k highest observations by multiplicity. Return the exact mean as a Fraction string, or None when no observations survive.","evaluation_group":"s3-na-frequency-symmetric-trim-mean","failed_approach":"Inclusive value filtering instead keeps all boundary ties, exceeding retained multiplicity.","family":"s3-numerical-aggregation-frequency-symmetric-trim-mean-trim-threshold-values","id":"FA-13321","implementations":{"attempt":{"sha256":"bc9d8e638f3f70aabaf6bdf3d3434972cac006dda699b4a4e330f0ab7c153de8","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(rows, k):\n xs=sorted(x for x,w in rows for _ in range(w))\n if not xs or 2*k>=len(xs): return None\n return str(Fraction(sum(x for x in xs if xs[k]<=x<=xs[-k-1]),len(xs)-2*k))\ndef check(label, actual, expected):\n observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([(9, 1), (1, 4), (5, 2)], 2)), '7/3')\ncheck('regression 2', solve(*([(2, 4)], 1)), '2')\ncheck('regression 3', solve(*([], 0)), None)\ncheck('regression 4', solve(*([(1, 2), (4, 2)], 2)), None)\ncheck('regression 5', solve(*([(0, 1), (10, 3), (-5, 2)], 1)), '15/4')\ncheck('regression 6', solve(*([(3, 0), (8, 1)], 0)), '8')\ncheck('regression 7', solve(*([(1, 1), (8, 4), (9, 1)], 1)), '8')\ncheck(\"variable weighted trim\",solve([(N,3),(N+6,2)],1),str(Fraction(3*N+6,3)))\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":"49472d5e53d986eb9bc18b7b72ad2bdb17df610d0817ca16ba50e0ad0fc1bb6a","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(rows, k):\n xs=sorted(x for x,w in rows for _ in range(w))\n if not xs or 2*k>=len(xs): return None\n return str(Fraction(sum(x for x in xs if xs[k]=len(xs): return None\n return str(Fraction(sum(xs[k:len(xs)-k]),len(xs)-2*k))\ndef check(label, actual, expected):\n observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([(9, 1), (1, 4), (5, 2)], 2)), '7/3')\ncheck('regression 2', solve(*([(2, 4)], 1)), '2')\ncheck('regression 3', solve(*([], 0)), None)\ncheck('regression 4', solve(*([(1, 2), (4, 2)], 2)), None)\ncheck('regression 5', solve(*([(0, 1), (10, 3), (-5, 2)], 1)), '15/4')\ncheck('regression 6', solve(*([(3, 0), (8, 1)], 0)), '8')\ncheck('regression 7', solve(*([(1, 1), (8, 4), (9, 1)], 1)), '8')\ncheck(\"variable weighted trim\",solve([(N,3),(N+6,2)],1),str(Fraction(3*N+6,3)))\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-symmetric-trim-mean-trim-threshold-values","generated_at":"2026-09-29T14:39:05.817887+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 symmetric trim mean contract at the identified reduction decision.","root_cause":"All ties at a trim boundary are removed rather than the requested count.","sha256":"a653978ed6106776ff8df4288821b326823309b91f1e9bfad575b4f0f4c5ecdc","title":"Frequency symmetric trim mean: All ties at a trim boundary are removed rather than the requested count. ยท case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":44.86,"exit_code":1,"observations":[{"actual":"14/3","check":"regression 1","expected":"7/3","passed":false},{"actual":"4","check":"regression 2","expected":"2","passed":false},{"actual":null,"check":"regression 3","expected":null,"passed":true},{"actual":null,"check":"regression 4","expected":null,"passed":true},{"actual":"5","check":"regression 5","expected":"15/4","passed":false},{"actual":"8","check":"regression 6","expected":"8","passed":true},{"actual":"8","check":"regression 7","expected":"8","passed":true},{"actual":"17/3","check":"variable weighted trim","expected":"3","passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": \"14/3\", \"expected\": \"7/3\", \"passed\": false}, {\"check\": \"regression 2\", \"actual\": \"4\", \"expected\": \"2\", \"passed\": false}, {\"check\": \"regression 3\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression 4\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression 5\", \"actual\": \"5\", \"expected\": \"15/4\", \"passed\": false}, {\"check\": \"regression 6\", \"actual\": \"8\", \"expected\": \"8\", \"passed\": true}, {\"check\": \"regression 7\", \"actual\": \"8\", \"expected\": \"8\", \"passed\": true}, {\"check\": \"variable weighted trim\", \"actual\": \"17/3\", \"expected\": \"3\", \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":47.337,"exit_code":1,"observations":[{"actual":"0","check":"regression 1","expected":"7/3","passed":false},{"actual":"0","check":"regression 2","expected":"2","passed":false},{"actual":null,"check":"regression 3","expected":null,"passed":true},{"actual":null,"check":"regression 4","expected":null,"passed":true},{"actual":"0","check":"regression 5","expected":"15/4","passed":false},{"actual":"0","check":"regression 6","expected":"8","passed":false},{"actual":"0","check":"regression 7","expected":"8","passed":false},{"actual":"0","check":"variable weighted trim","expected":"3","passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": \"0\", \"expected\": \"7/3\", \"passed\": false}, {\"check\": \"regression 2\", \"actual\": \"0\", \"expected\": \"2\", \"passed\": false}, {\"check\": \"regression 3\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression 4\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression 5\", \"actual\": \"0\", \"expected\": \"15/4\", \"passed\": false}, {\"check\": \"regression 6\", \"actual\": \"0\", \"expected\": \"8\", \"passed\": false}, {\"check\": \"regression 7\", \"actual\": \"0\", \"expected\": \"8\", \"passed\": false}, {\"check\": \"variable weighted trim\", \"actual\": \"0\", \"expected\": \"3\", \"passed\": false}], \"passed\": false}\n"},"fixed":{"elapsed_ms":43.547,"exit_code":0,"observations":[{"actual":"7/3","check":"regression 1","expected":"7/3","passed":true},{"actual":"2","check":"regression 2","expected":"2","passed":true},{"actual":null,"check":"regression 3","expected":null,"passed":true},{"actual":null,"check":"regression 4","expected":null,"passed":true},{"actual":"15/4","check":"regression 5","expected":"15/4","passed":true},{"actual":"8","check":"regression 6","expected":"8","passed":true},{"actual":"8","check":"regression 7","expected":"8","passed":true},{"actual":"3","check":"variable weighted trim","expected":"3","passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": \"7/3\", \"expected\": \"7/3\", \"passed\": true}, {\"check\": \"regression 2\", \"actual\": \"2\", \"expected\": \"2\", \"passed\": true}, {\"check\": \"regression 3\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression 4\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression 5\", \"actual\": \"15/4\", \"expected\": \"15/4\", \"passed\": true}, {\"check\": \"regression 6\", \"actual\": \"8\", \"expected\": \"8\", \"passed\": true}, {\"check\": \"regression 7\", \"actual\": \"8\", \"expected\": \"8\", \"passed\": true}, {\"check\": \"variable weighted trim\", \"actual\": \"3\", \"expected\": \"3\", \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}