{"abstract":"The reduction disagrees with its explicit aggregation oracle.","category":"Numerical aggregation","checks":8,"contract":"Each [integer timestamp, integer contribution] independently contributes value/2**age at integer query now; exclude future events. Same-time events are independent additive contributions. Return exact Fraction string.","evaluation_group":"s3-na-timestamp-half-decayed-mass","failed_approach":"Rounding each contribution loses fractional residual mass.","family":"s3-numerical-aggregation-timestamp-half-decayed-mass-decay-fraction-truncate","id":"FA-13591","implementations":{"attempt":{"sha256":"c77addd8c62c3d5ea93134c01a7d73ebf31af7ad08e6482af142427339dfe5b2","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(events, now):\n    out=Fraction(0)\n    for t,value in events:\n        if t>now: continue\n        out+=Fraction(round(Fraction(value,2**(now-t))))\n    return str(out)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([(0, 8), (2, 4)], 3)), '3')\ncheck('regression 2', solve(*([], 4)), '0')\ncheck('regression 3', solve(*([(5, 3)], 4)), '0')\ncheck('regression 4', solve(*([(3, -4), (3, 8)], 3)), '4')\ncheck('regression 5', solve(*([(0, 3), (1, 2), (1, 2)], 2)), '11/4')\ncheck('regression 6', solve(*([(-2, 8), (0, -1)], 1)), '1/2')\ncheck('regression 7', solve(*([(4, 9), (0, 16)], 4)), '10')\ncheck(\"variable age\",solve([(0,2**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":"623f9a32d3fbcc189212a7e0a62aba10b90f442c37677acd8e21dd44e64cb36b","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(events, now):\n    out=Fraction(0)\n    for t,value in events:\n        if t>now: continue\n        out+=Fraction(value//(2**(now-t)))\n    return str(out)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([(0, 8), (2, 4)], 3)), '3')\ncheck('regression 2', solve(*([], 4)), '0')\ncheck('regression 3', solve(*([(5, 3)], 4)), '0')\ncheck('regression 4', solve(*([(3, -4), (3, 8)], 3)), '4')\ncheck('regression 5', solve(*([(0, 3), (1, 2), (1, 2)], 2)), '11/4')\ncheck('regression 6', solve(*([(-2, 8), (0, -1)], 1)), '1/2')\ncheck('regression 7', solve(*([(4, 9), (0, 16)], 4)), '10')\ncheck(\"variable age\",solve([(0,2**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":"1e2dbd9dc2732119fd3166906d82ac3d8c75f01596f3ed96e684181e9366e44f","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(events, now):\n    out=Fraction(0)\n    for t,value in events:\n        if t>now: continue\n        out+=Fraction(value,2**(now-t))\n    return str(out)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([(0, 8), (2, 4)], 3)), '3')\ncheck('regression 2', solve(*([], 4)), '0')\ncheck('regression 3', solve(*([(5, 3)], 4)), '0')\ncheck('regression 4', solve(*([(3, -4), (3, 8)], 3)), '4')\ncheck('regression 5', solve(*([(0, 3), (1, 2), (1, 2)], 2)), '11/4')\ncheck('regression 6', solve(*([(-2, 8), (0, -1)], 1)), '1/2')\ncheck('regression 7', solve(*([(4, 9), (0, 16)], 4)), '10')\ncheck(\"variable age\",solve([(0,2**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-timestamp-half-decayed-mass-decay-fraction-truncate","generated_at":"2026-09-29T14:39:08.698796+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 timestamp half decayed mass contract at the identified reduction decision.","root_cause":"Each decayed contribution is integer-truncated.","sha256":"c10ddb6728ebd0cb03c2174163cad5191cb9b707497ae67e919c7170d3452dc4","title":"Timestamp half decayed mass: Each decayed contribution is integer-truncated. · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":46.677,"exit_code":1,"observations":[{"actual":"3","check":"regression 1","expected":"3","passed":true},{"actual":"0","check":"regression 2","expected":"0","passed":true},{"actual":"0","check":"regression 3","expected":"0","passed":true},{"actual":"4","check":"regression 4","expected":"4","passed":true},{"actual":"3","check":"regression 5","expected":"11/4","passed":false},{"actual":"1","check":"regression 6","expected":"1/2","passed":false},{"actual":"10","check":"regression 7","expected":"10","passed":true},{"actual":"1","check":"variable age","expected":"1","passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": \"3\", \"expected\": \"3\", \"passed\": true}, {\"check\": \"regression 2\", \"actual\": \"0\", \"expected\": \"0\", \"passed\": true}, {\"check\": \"regression 3\", \"actual\": \"0\", \"expected\": \"0\", \"passed\": true}, {\"check\": \"regression 4\", \"actual\": \"4\", \"expected\": \"4\", \"passed\": true}, {\"check\": \"regression 5\", \"actual\": \"3\", \"expected\": \"11/4\", \"passed\": false}, {\"check\": \"regression 6\", \"actual\": \"1\", \"expected\": \"1/2\", \"passed\": false}, {\"check\": \"regression 7\", \"actual\": \"10\", \"expected\": \"10\", \"passed\": true}, {\"check\": \"variable age\", \"actual\": \"1\", \"expected\": \"1\", \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":44.892,"exit_code":1,"observations":[{"actual":"3","check":"regression 1","expected":"3","passed":true},{"actual":"0","check":"regression 2","expected":"0","passed":true},{"actual":"0","check":"regression 3","expected":"0","passed":true},{"actual":"4","check":"regression 4","expected":"4","passed":true},{"actual":"2","check":"regression 5","expected":"11/4","passed":false},{"actual":"0","check":"regression 6","expected":"1/2","passed":false},{"actual":"10","check":"regression 7","expected":"10","passed":true},{"actual":"1","check":"variable age","expected":"1","passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": \"3\", \"expected\": \"3\", \"passed\": true}, {\"check\": \"regression 2\", \"actual\": \"0\", \"expected\": \"0\", \"passed\": true}, {\"check\": \"regression 3\", \"actual\": \"0\", \"expected\": \"0\", \"passed\": true}, {\"check\": \"regression 4\", \"actual\": \"4\", \"expected\": \"4\", \"passed\": true}, {\"check\": \"regression 5\", \"actual\": \"2\", \"expected\": \"11/4\", \"passed\": false}, {\"check\": \"regression 6\", \"actual\": \"0\", \"expected\": \"1/2\", \"passed\": false}, {\"check\": \"regression 7\", \"actual\": \"10\", \"expected\": \"10\", \"passed\": true}, {\"check\": \"variable age\", \"actual\": \"1\", \"expected\": \"1\", \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":44.07,"exit_code":0,"observations":[{"actual":"3","check":"regression 1","expected":"3","passed":true},{"actual":"0","check":"regression 2","expected":"0","passed":true},{"actual":"0","check":"regression 3","expected":"0","passed":true},{"actual":"4","check":"regression 4","expected":"4","passed":true},{"actual":"11/4","check":"regression 5","expected":"11/4","passed":true},{"actual":"1/2","check":"regression 6","expected":"1/2","passed":true},{"actual":"10","check":"regression 7","expected":"10","passed":true},{"actual":"1","check":"variable age","expected":"1","passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": \"3\", \"expected\": \"3\", \"passed\": true}, {\"check\": \"regression 2\", \"actual\": \"0\", \"expected\": \"0\", \"passed\": true}, {\"check\": \"regression 3\", \"actual\": \"0\", \"expected\": \"0\", \"passed\": true}, {\"check\": \"regression 4\", \"actual\": \"4\", \"expected\": \"4\", \"passed\": true}, {\"check\": \"regression 5\", \"actual\": \"11/4\", \"expected\": \"11/4\", \"passed\": true}, {\"check\": \"regression 6\", \"actual\": \"1/2\", \"expected\": \"1/2\", \"passed\": true}, {\"check\": \"regression 7\", \"actual\": \"10\", \"expected\": \"10\", \"passed\": true}, {\"check\": \"variable age\", \"actual\": \"1\", \"expected\": \"1\", \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}