{"abstract":"Soft targets are renormalized over only nonzero entries.","category":"Training label alignment","checks":7,"contract":"For each row of nonnegative weights, return probabilities summing to one; an all-zero row stays all zero.","contract_signature":"rows","evaluation_group":"training-label-alignment-soft-target-row-normalization","failed_approach":"Normalizing only positive entries handles sparse vectors but still biases unequal weights.","family":"w3-training-label-alignment-soft-target-normalization","id":"FA-97966","implementations":{"attempt":{"sha256":"2f510b40ac18749606d969b4e1bdf273ab5f1fb0cd2cdd0c81093bb10eb5406a","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(rows):\n    return [[int(x>0)/(sum(int(v>0) for v in row) or 1) for x in row] for row in rows]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('unequal weights',solve([[N,2*N]]),[[1/3,2/3]])\ncheck('already normalized',solve([[0.25,0.75]]),[[0.25,0.75]])\ncheck('all zero',solve([[0,0]]),[[0,0]])\ncheck('empty batch',solve([]),[])\ncheck('singleton',solve([[N]]),[[1.0]])\ncheck('sparse unequal',solve([[0,N,3*N]]),[[0,0.25,0.75]])\ncheck('independent rows',solve([[N,0],[0,2*N]]),[[1.0,0],[0,1.0]])\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":"8a8923104fde3ecbe03d6c59e9b63e240063f0c48a94307b921cc786921d7f9a","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(rows):\n    return [[x/sum(1 for v in row if v>0) if x else 0 for x in row] for row in rows]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('unequal weights',solve([[N,2*N]]),[[1/3,2/3]])\ncheck('already normalized',solve([[0.25,0.75]]),[[0.25,0.75]])\ncheck('all zero',solve([[0,0]]),[[0,0]])\ncheck('empty batch',solve([]),[])\ncheck('singleton',solve([[N]]),[[1.0]])\ncheck('sparse unequal',solve([[0,N,3*N]]),[[0,0.25,0.75]])\ncheck('independent rows',solve([[N,0],[0,2*N]]),[[1.0,0],[0,1.0]])\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":"Weights are finite nonnegative numbers; output uses ordinary Python division. 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":"w3-training-label-alignment-soft-target-normalization","generated_at":"2026-09-30T16:48:07.975849+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"A small deterministic training-data operation makes label ownership and ordering explicit.","root_cause":"The normalizer uses the number of positive classes rather than total probability mass.","sha256":"d915d8f42966663d957dcea914773cb8aff2e6eee761aeab1d93fddd2078fd6f","title":"Soft targets are renormalized over only nonzero entries · case 01","variant":1,"variant_policy":"The fixture code references N, supplied as 1 through 5. Fixed boundary checks may repeat. These records share one mechanism and must remain together in evaluation splits.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":27.543,"exit_code":1,"observations":[{"actual":[[0.5,0.5]],"check":"unequal weights","expected":[[0.3333333333333333,0.6666666666666666]],"passed":false},{"actual":[[0.5,0.5]],"check":"already normalized","expected":[[0.25,0.75]],"passed":false},{"actual":[[0.0,0.0]],"check":"all zero","expected":[[0,0]],"passed":true},{"actual":[],"check":"empty batch","expected":[],"passed":true},{"actual":[[1.0]],"check":"singleton","expected":[[1.0]],"passed":true},{"actual":[[0.0,0.5,0.5]],"check":"sparse unequal","expected":[[0,0.25,0.75]],"passed":false},{"actual":[[1.0,0.0],[0.0,1.0]],"check":"independent rows","expected":[[1.0,0],[0,1.0]],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"unequal weights\", \"actual\": [[0.5, 0.5]], \"expected\": [[0.3333333333333333, 0.6666666666666666]], \"passed\": false}, {\"check\": \"already normalized\", \"actual\": [[0.5, 0.5]], \"expected\": [[0.25, 0.75]], \"passed\": false}, {\"check\": \"all zero\", \"actual\": [[0.0, 0.0]], \"expected\": [[0, 0]], \"passed\": true}, {\"check\": \"empty batch\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"singleton\", \"actual\": [[1.0]], \"expected\": [[1.0]], \"passed\": true}, {\"check\": \"sparse unequal\", \"actual\": [[0.0, 0.5, 0.5]], \"expected\": [[0, 0.25, 0.75]], \"passed\": false}, {\"check\": \"independent rows\", \"actual\": [[1.0, 0.0], [0.0, 1.0]], \"expected\": [[1.0, 0], [0, 1.0]], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":25.605,"exit_code":1,"observations":[{"actual":[[0.5,1.0]],"check":"unequal weights","expected":[[0.3333333333333333,0.6666666666666666]],"passed":false},{"actual":[[0.125,0.375]],"check":"already normalized","expected":[[0.25,0.75]],"passed":false},{"actual":[[0,0]],"check":"all zero","expected":[[0,0]],"passed":true},{"actual":[],"check":"empty batch","expected":[],"passed":true},{"actual":[[1.0]],"check":"singleton","expected":[[1.0]],"passed":true},{"actual":[[0,0.5,1.5]],"check":"sparse unequal","expected":[[0,0.25,0.75]],"passed":false},{"actual":[[1.0,0],[0,2.0]],"check":"independent rows","expected":[[1.0,0],[0,1.0]],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"unequal weights\", \"actual\": [[0.5, 1.0]], \"expected\": [[0.3333333333333333, 0.6666666666666666]], \"passed\": false}, {\"check\": \"already normalized\", \"actual\": [[0.125, 0.375]], \"expected\": [[0.25, 0.75]], \"passed\": false}, {\"check\": \"all zero\", \"actual\": [[0, 0]], \"expected\": [[0, 0]], \"passed\": true}, {\"check\": \"empty batch\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"singleton\", \"actual\": [[1.0]], \"expected\": [[1.0]], \"passed\": true}, {\"check\": \"sparse unequal\", \"actual\": [[0, 0.5, 1.5]], \"expected\": [[0, 0.25, 0.75]], \"passed\": false}, {\"check\": \"independent rows\", \"actual\": [[1.0, 0], [0, 2.0]], \"expected\": [[1.0, 0], [0, 1.0]], \"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."}}