{"abstract":"row selection matches columns instead of rows.","category":"Sparse tensor canonicalization","checks":6,"contract":"Return entries from exactly the requested row, preserving their column/value pairs in column order.","contract_signature":"entries,row","evaluation_group":"w3-sparse-tensor-canonicalization-coo-row-filter","failed_approach":"A threshold includes unrelated earlier rows, even though the requested row match is exact.","family":"w3-sparse-tensor-canonicalization-coo-row-filter","id":"FA-100501","implementations":{"attempt":{"sha256":"c95a7567b7805af7c5e5626ab9d42755f174bfaa9ec222d7838e32fb0c8c9338","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(entries,row):\n    return [[r,c,v] for r,c,v in entries if r<=row]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('requested row',solve([[0,2,3],[1,0,4],[2,1,5]],1),[[1,0,4]])\ncheck('different column same label',solve([[0,1,3],[1,2,4]],1),[[1,2,4]])\ncheck('empty',solve([],N),[])\ncheck('no match',solve([[0,0,1]],N+1),[])\ncheck('multiple sorted',solve([[N,2,1],[N,0,3],[N,1,5]],N),[[N,0,3],[N,1,5],[N,2,1]])\ncheck('single row',solve([[0,N,8]],0),[[0,N,8]])\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":"2134f0aeb54ea8af39be88dd8689359eb38f6ada2fbe7ba9c1083676c33656a2","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(entries,row):\n    return [[r,c,v] for r,c,v in entries if c==row]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('requested row',solve([[0,2,3],[1,0,4],[2,1,5]],1),[[1,0,4]])\ncheck('different column same label',solve([[0,1,3],[1,2,4]],1),[[1,2,4]])\ncheck('empty',solve([],N),[])\ncheck('no match',solve([[0,0,1]],N+1),[])\ncheck('multiple sorted',solve([[N,2,1],[N,0,3],[N,1,5]],N),[[N,0,3],[N,1,5],[N,2,1]])\ncheck('single row',solve([[0,N,8]],0),[[0,N,8]])\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-row-filter","generated_at":"2026-09-30T16:48:36.272893+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":"Row filtering must inspect the row coordinate and canonicalize the selected columns.","sha256":"d03f93b62ce60d6765d08e143524ae860aed34bec32c9a917fb77bc7c87ad5be","title":"Sparse tensor: row selection matches columns instead of rows · 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":28.035,"exit_code":1,"observations":[{"actual":[[0,2,3],[1,0,4]],"check":"requested row","expected":[[1,0,4]],"passed":false},{"actual":[[0,1,3],[1,2,4]],"check":"different column same label","expected":[[1,2,4]],"passed":false},{"actual":[],"check":"empty","expected":[],"passed":true},{"actual":[[0,0,1]],"check":"no match","expected":[],"passed":false},{"actual":[[1,2,1],[1,0,3],[1,1,5]],"check":"multiple sorted","expected":[[1,0,3],[1,1,5],[1,2,1]],"passed":false},{"actual":[[0,1,8]],"check":"single row","expected":[[0,1,8]],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"requested row\", \"actual\": [[0, 2, 3], [1, 0, 4]], \"expected\": [[1, 0, 4]], \"passed\": false}, {\"check\": \"different column same label\", \"actual\": [[0, 1, 3], [1, 2, 4]], \"expected\": [[1, 2, 4]], \"passed\": false}, {\"check\": \"empty\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"no match\", \"actual\": [[0, 0, 1]], \"expected\": [], \"passed\": false}, {\"check\": \"multiple sorted\", \"actual\": [[1, 2, 1], [1, 0, 3], [1, 1, 5]], \"expected\": [[1, 0, 3], [1, 1, 5], [1, 2, 1]], \"passed\": false}, {\"check\": \"single row\", \"actual\": [[0, 1, 8]], \"expected\": [[0, 1, 8]], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":29.173,"exit_code":1,"observations":[{"actual":[[2,1,5]],"check":"requested row","expected":[[1,0,4]],"passed":false},{"actual":[[0,1,3]],"check":"different column same label","expected":[[1,2,4]],"passed":false},{"actual":[],"check":"empty","expected":[],"passed":true},{"actual":[],"check":"no match","expected":[],"passed":true},{"actual":[[1,1,5]],"check":"multiple sorted","expected":[[1,0,3],[1,1,5],[1,2,1]],"passed":false},{"actual":[],"check":"single row","expected":[[0,1,8]],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"requested row\", \"actual\": [[2, 1, 5]], \"expected\": [[1, 0, 4]], \"passed\": false}, {\"check\": \"different column same label\", \"actual\": [[0, 1, 3]], \"expected\": [[1, 2, 4]], \"passed\": false}, {\"check\": \"empty\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"no match\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"multiple sorted\", \"actual\": [[1, 1, 5]], \"expected\": [[1, 0, 3], [1, 1, 5], [1, 2, 1]], \"passed\": false}, {\"check\": \"single row\", \"actual\": [], \"expected\": [[0, 1, 8]], \"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."}}