{"abstract":"diagonal extraction includes off-diagonal values.","category":"Sparse tensor canonicalization","checks":6,"contract":"Return the sum of values at coordinates whose row equals column.","contract_signature":"entries","evaluation_group":"w3-sparse-tensor-canonicalization-coo-diagonal","failed_approach":"The upper triangle is broader than the diagonal and admits off-diagonal entries.","family":"w3-sparse-tensor-canonicalization-coo-diagonal","id":"FA-100496","implementations":{"attempt":{"sha256":"4b9defa85946f6bfc16075f8b973f368ee9a898da97c785fd02e97bd8b1583fa","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(entries):\n    return sum(v for r,c,v in entries if r<=c)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('mixed',solve([[0,0,N],[0,1,5],[2,2,3]]),N+3)\ncheck('empty',solve([]),0)\ncheck('off diagonal',solve([[0,N,8],[N,0,4]]),0)\ncheck('singleton diagonal',solve([[N,N,7]]),7)\ncheck('negative diagonal',solve([[1,1,-N],[1,2,9]]),-N)\ncheck('multiple diagonal entries',solve([[0,0,2],[1,1,3],[2,0,8]]),5)\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":"487591e8c5b62374512adf11e502f2c48a88c1ce9162fde135120938a53ede19","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(entries):\n    return sum(v for r,c,v in entries)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('mixed',solve([[0,0,N],[0,1,5],[2,2,3]]),N+3)\ncheck('empty',solve([]),0)\ncheck('off diagonal',solve([[0,N,8],[N,0,4]]),0)\ncheck('singleton diagonal',solve([[N,N,7]]),7)\ncheck('negative diagonal',solve([[1,1,-N],[1,2,9]]),-N)\ncheck('multiple diagonal entries',solve([[0,0,2],[1,1,3],[2,0,8]]),5)\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":"These are toy integer-valued COO or CSR contracts; they make no claim about a framework API, floating point policy, or large tensor performance. 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-sparse-tensor-canonicalization-coo-diagonal","generated_at":"2026-09-30T16:48:36.272759+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"A small offline sparse-array operation has explicit coordinate and storage semantics.","root_cause":"Only coordinates on the main diagonal contribute to this extraction.","sha256":"3c98bc017a28171a2ec99c5f47e810de6dec3a4e54a37fc88830ed41ca3c9419","title":"Sparse tensor: diagonal extraction includes off-diagonal values · 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":28.626,"exit_code":1,"observations":[{"actual":9,"check":"mixed","expected":4,"passed":false},{"actual":0,"check":"empty","expected":0,"passed":true},{"actual":8,"check":"off diagonal","expected":0,"passed":false},{"actual":7,"check":"singleton diagonal","expected":7,"passed":true},{"actual":8,"check":"negative diagonal","expected":-1,"passed":false},{"actual":5,"check":"multiple diagonal entries","expected":5,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"mixed\", \"actual\": 9, \"expected\": 4, \"passed\": false}, {\"check\": \"empty\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"off diagonal\", \"actual\": 8, \"expected\": 0, \"passed\": false}, {\"check\": \"singleton diagonal\", \"actual\": 7, \"expected\": 7, \"passed\": true}, {\"check\": \"negative diagonal\", \"actual\": 8, \"expected\": -1, \"passed\": false}, {\"check\": \"multiple diagonal entries\", \"actual\": 5, \"expected\": 5, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":28.961,"exit_code":1,"observations":[{"actual":9,"check":"mixed","expected":4,"passed":false},{"actual":0,"check":"empty","expected":0,"passed":true},{"actual":12,"check":"off diagonal","expected":0,"passed":false},{"actual":7,"check":"singleton diagonal","expected":7,"passed":true},{"actual":8,"check":"negative diagonal","expected":-1,"passed":false},{"actual":13,"check":"multiple diagonal entries","expected":5,"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"mixed\", \"actual\": 9, \"expected\": 4, \"passed\": false}, {\"check\": \"empty\", \"actual\": 0, \"expected\": 0, \"passed\": true}, {\"check\": \"off diagonal\", \"actual\": 12, \"expected\": 0, \"passed\": false}, {\"check\": \"singleton diagonal\", \"actual\": 7, \"expected\": 7, \"passed\": true}, {\"check\": \"negative diagonal\", \"actual\": 8, \"expected\": -1, \"passed\": false}, {\"check\": \"multiple diagonal entries\", \"actual\": 13, \"expected\": 5, \"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."}}