{"abstract":"Corridors bend on the wrong corner, cutting through rooms.","category":"Procedural level generation constraints","checks":8,"contract":"Carve from centre a [x, y] to b. Horizontal-first walks x from ax to bx on row ay, then y from ay to by on column bx; otherwise y first on column ax, then x on row by. Both legs include their endpoints; returns cells [x, y] in walk order without duplicates.","contract_signature":"a, b, horizontal_first","evaluation_group":"w2-procedural-level-generation-constraints-l-corridor","failed_approach":"Deriving the order from direction ignores the flag.","family":"w2-procedural-level-generation-constraints-l-corridor-leg-order-flag","id":"FA-86596","implementations":{"attempt":{"sha256":"ba9087be480c17a665e0d5a2fce5230cdf9a987bbca7931ebe5b2fdb3de3d30e","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(a, b, horizontal_first):\n    ax, ay = a\n    bx, by = b\n    path = []\n    def add(x, y):\n        if [x, y] not in path:\n            path.append([x, y])\n    sx = 1 if bx >= ax else -1\n    sy = 1 if by >= ay else -1\n    if horizontal_first == (ax < bx):\n        for x in range(ax, bx + sx, sx):\n            add(x, ay)\n        for y in range(ay, by + sy, sy):\n            add(bx, y)\n    else:\n        for y in range(ay, by + sy, sy):\n            add(ax, y)\n        for x in range(ax, bx + sx, sx):\n            add(x, by)\n    return path\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncases = [[('leftward corridor #1', [[5, 1], [2, 3], True], [[5, 1], [4, 1], [3, 1], [2, 1], [2, 2], [2, 3]]),\n  ('regression leg order flag #1', [[8, 6], [4, 5], True], [[8, 6], [7, 6], [6, 6], [5, 6], [4, 6], [4, 5]]),\n  ('regression leg order flag #2',\n   [[8, 4], [1, 0], True],\n   [[8, 4], [7, 4], [6, 4], [5, 4], [4, 4], [3, 4], [2, 4], [1, 4], [1, 3], [1, 2], [1, 1], [1, 0]]),\n  ('regression leg order flag #3',\n   [[7, 2], [6, 8], False],\n   [[7, 2], [7, 3], [7, 4], [7, 5], [7, 6], [7, 7], [7, 8], [6, 8]]),\n  ('regression leg order flag #4', [[5, 1], [4, 5], True], [[5, 1], [4, 1], [4, 2], [4, 3], [4, 4], [4, 5]]),\n  ('same column #1', [[3, 0], [3, 2], False], [[3, 0], [3, 1], [3, 2]]),\n  ('single cell #1', [[4, 4], [4, 4], True], [[4, 4]]),\n  ('control #1', [[8, 1], [8, 3], True], [[8, 1], [8, 2], [8, 3]])],\n [('leftward corridor #1', [[5, 1], [2, 3], True], [[5, 1], [4, 1], [3, 1], [2, 1], [2, 2], [2, 3]]),\n  ('regression leg order flag #1',\n   [[8, 4], [1, 0], True],\n   [[8, 4], [7, 4], [6, 4], [5, 4], [4, 4], [3, 4], [2, 4], [1, 4], [1, 3], [1, 2], [1, 1], [1, 0]]),\n  ('fault site leg order flag #1', [[6, 4], [8, 5], False], [[6, 4], [6, 5], [7, 5], [8, 5]]),\n  ('regression leg order flag #2',\n   [[7, 2], [6, 8], False],\n   [[7, 2], [7, 3], [7, 4], [7, 5], [7, 6], [7, 7], [7, 8], [6, 8]]),\n  ('regression leg order flag #3', [[5, 1], [4, 5], True], [[5, 1], [4, 1], [4, 2], [4, 3], [4, 4], [4, 5]]),\n  ('same column #1', [[3, 0], [3, 2], False], [[3, 0], [3, 1], [3, 2]]),\n  ('single cell #1', [[4, 4], [4, 4], True], [[4, 4]]),\n  ('control #1', [[8, 1], [8, 3], True], [[8, 1], [8, 2], [8, 3]])],\n [('leftward corridor #1', [[5, 1], [2, 3], True], [[5, 1], [4, 1], [3, 1], [2, 1], [2, 2], [2, 3]]),\n  ('fault site leg order flag #1', [[6, 2], [8, 0], True], [[6, 2], [7, 2], [8, 2], [8, 1], [8, 0]]),\n  ('fault site leg order flag #2',\n   [[3, 2], [8, 8], False],\n   [[3, 2], [3, 3], [3, 4], [3, 5], [3, 6], [3, 7], [3, 8], [4, 8], [5, 8], [6, 8], [7, 8], [8, 8]]),\n  ('regression leg order flag #1', [[5, 1], [4, 5], True], [[5, 1], [4, 1], [4, 2], [4, 3], [4, 4], [4, 5]]),\n  ('regression leg order flag #2', [[6, 2], [3, 4], False], [[6, 2], [6, 3], [6, 4], [5, 4], [4, 4], [3, 4]]),\n  ('same column #1', [[3, 0], [3, 2], False], [[3, 0], [3, 1], [3, 2]]),\n  ('single cell #1', [[4, 4], [4, 4], True], [[4, 4]]),\n  ('control #1', [[8, 1], [8, 3], True], [[8, 1], [8, 2], [8, 3]])],\n [('leftward corridor #1', [[5, 1], [2, 3], True], [[5, 1], [4, 1], [3, 1], [2, 1], [2, 2], [2, 3]]),\n  ('fault site leg order flag #1',\n   [[3, 5], [8, 1], False],\n   [[3, 5], [3, 4], [3, 3], [3, 2], [3, 1], [4, 1], [5, 1], [6, 1], [7, 1], [8, 1]]),\n  ('fault site leg order flag #2',\n   [[1, 4], [6, 1], False],\n   [[1, 4], [1, 3], [1, 2], [1, 1], [2, 1], [3, 1], [4, 1], [5, 1], [6, 1]]),\n  ('regression leg order flag #1',\n   [[1, 3], [0, 8], True],\n   [[1, 3], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 8]]),\n  ('regression leg order flag #2',\n   [[5, 5], [0, 2], False],\n   [[5, 5], [5, 4], [5, 3], [5, 2], [4, 2], [3, 2], [2, 2], [1, 2], [0, 2]]),\n  ('same column #1', [[3, 0], [3, 2], False], [[3, 0], [3, 1], [3, 2]]),\n  ('single cell #1', [[4, 4], [4, 4], True], [[4, 4]]),\n  ('control #1', [[4, 2], [4, 0], False], [[4, 2], [4, 1], [4, 0]])],\n [('leftward corridor #1', [[5, 1], [2, 3], True], [[5, 1], [4, 1], [3, 1], [2, 1], [2, 2], [2, 3]]),\n  ('regression leg order flag #1',\n   [[7, 2], [6, 8], False],\n   [[7, 2], [7, 3], [7, 4], [7, 5], [7, 6], [7, 7], [7, 8], [6, 8]]),\n  ('fault site leg order flag #1',\n   [[0, 6], [5, 0], False],\n   [[0, 6], [0, 5], [0, 4], [0, 3], [0, 2], [0, 1], [0, 0], [1, 0], [2, 0], [3, 0], [4, 0], [5, 0]]),\n  ('regression leg order flag #2', [[5, 2], [3, 4], True], [[5, 2], [4, 2], [3, 2], [3, 3], [3, 4]]),\n  ('regression leg order flag #3', [[3, 8], [2, 7], True], [[3, 8], [2, 8], [2, 7]]),\n  ('same column #1', [[3, 0], [3, 2], False], [[3, 0], [3, 1], [3, 2]]),\n  ('single cell #1', [[4, 4], [4, 4], True], [[4, 4]]),\n  ('control #1', [[7, 6], [7, 3], False], [[7, 6], [7, 5], [7, 4], [7, 3]])]]\nfor label, args, expected in cases[N-1]:\n    check(label, solve(*args), expected)\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":"ad7c90607b5c3eb269baf0c2e3b4da45bbc5811430a4f27ba0a803922f12b25c","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(a, b, horizontal_first):\n    ax, ay = a\n    bx, by = b\n    path = []\n    def add(x, y):\n        if [x, y] not in path:\n            path.append([x, y])\n    sx = 1 if bx >= ax else -1\n    sy = 1 if by >= ay else -1\n    if not horizontal_first:\n        for x in range(ax, bx + sx, sx):\n            add(x, ay)\n        for y in range(ay, by + sy, sy):\n            add(bx, y)\n    else:\n        for y in range(ay, by + sy, sy):\n            add(ax, y)\n        for x in range(ax, bx + sx, sx):\n            add(x, by)\n    return path\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncases = [[('leftward corridor #1', [[5, 1], [2, 3], True], [[5, 1], [4, 1], [3, 1], [2, 1], [2, 2], [2, 3]]),\n  ('regression leg order flag #1', [[8, 6], [4, 5], True], [[8, 6], [7, 6], [6, 6], [5, 6], [4, 6], [4, 5]]),\n  ('regression leg order flag #2',\n   [[8, 4], [1, 0], True],\n   [[8, 4], [7, 4], [6, 4], [5, 4], [4, 4], [3, 4], [2, 4], [1, 4], [1, 3], [1, 2], [1, 1], [1, 0]]),\n  ('regression leg order flag #3',\n   [[7, 2], [6, 8], False],\n   [[7, 2], [7, 3], [7, 4], [7, 5], [7, 6], [7, 7], [7, 8], [6, 8]]),\n  ('regression leg order flag #4', [[5, 1], [4, 5], True], [[5, 1], [4, 1], [4, 2], [4, 3], [4, 4], [4, 5]]),\n  ('same column #1', [[3, 0], [3, 2], False], [[3, 0], [3, 1], [3, 2]]),\n  ('single cell #1', [[4, 4], [4, 4], True], [[4, 4]]),\n  ('control #1', [[8, 1], [8, 3], True], [[8, 1], [8, 2], [8, 3]])],\n [('leftward corridor #1', [[5, 1], [2, 3], True], [[5, 1], [4, 1], [3, 1], [2, 1], [2, 2], [2, 3]]),\n  ('regression leg order flag #1',\n   [[8, 4], [1, 0], True],\n   [[8, 4], [7, 4], [6, 4], [5, 4], [4, 4], [3, 4], [2, 4], [1, 4], [1, 3], [1, 2], [1, 1], [1, 0]]),\n  ('fault site leg order flag #1', [[6, 4], [8, 5], False], [[6, 4], [6, 5], [7, 5], [8, 5]]),\n  ('regression leg order flag #2',\n   [[7, 2], [6, 8], False],\n   [[7, 2], [7, 3], [7, 4], [7, 5], [7, 6], [7, 7], [7, 8], [6, 8]]),\n  ('regression leg order flag #3', [[5, 1], [4, 5], True], [[5, 1], [4, 1], [4, 2], [4, 3], [4, 4], [4, 5]]),\n  ('same column #1', [[3, 0], [3, 2], False], [[3, 0], [3, 1], [3, 2]]),\n  ('single cell #1', [[4, 4], [4, 4], True], [[4, 4]]),\n  ('control #1', [[8, 1], [8, 3], True], [[8, 1], [8, 2], [8, 3]])],\n [('leftward corridor #1', [[5, 1], [2, 3], True], [[5, 1], [4, 1], [3, 1], [2, 1], [2, 2], [2, 3]]),\n  ('fault site leg order flag #1', [[6, 2], [8, 0], True], [[6, 2], [7, 2], [8, 2], [8, 1], [8, 0]]),\n  ('fault site leg order flag #2',\n   [[3, 2], [8, 8], False],\n   [[3, 2], [3, 3], [3, 4], [3, 5], [3, 6], [3, 7], [3, 8], [4, 8], [5, 8], [6, 8], [7, 8], [8, 8]]),\n  ('regression leg order flag #1', [[5, 1], [4, 5], True], [[5, 1], [4, 1], [4, 2], [4, 3], [4, 4], [4, 5]]),\n  ('regression leg order flag #2', [[6, 2], [3, 4], False], [[6, 2], [6, 3], [6, 4], [5, 4], [4, 4], [3, 4]]),\n  ('same column #1', [[3, 0], [3, 2], False], [[3, 0], [3, 1], [3, 2]]),\n  ('single cell #1', [[4, 4], [4, 4], True], [[4, 4]]),\n  ('control #1', [[8, 1], [8, 3], True], [[8, 1], [8, 2], [8, 3]])],\n [('leftward corridor #1', [[5, 1], [2, 3], True], [[5, 1], [4, 1], [3, 1], [2, 1], [2, 2], [2, 3]]),\n  ('fault site leg order flag #1',\n   [[3, 5], [8, 1], False],\n   [[3, 5], [3, 4], [3, 3], [3, 2], [3, 1], [4, 1], [5, 1], [6, 1], [7, 1], [8, 1]]),\n  ('fault site leg order flag #2',\n   [[1, 4], [6, 1], False],\n   [[1, 4], [1, 3], [1, 2], [1, 1], [2, 1], [3, 1], [4, 1], [5, 1], [6, 1]]),\n  ('regression leg order flag #1',\n   [[1, 3], [0, 8], True],\n   [[1, 3], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 8]]),\n  ('regression leg order flag #2',\n   [[5, 5], [0, 2], False],\n   [[5, 5], [5, 4], [5, 3], [5, 2], [4, 2], [3, 2], [2, 2], [1, 2], [0, 2]]),\n  ('same column #1', [[3, 0], [3, 2], False], [[3, 0], [3, 1], [3, 2]]),\n  ('single cell #1', [[4, 4], [4, 4], True], [[4, 4]]),\n  ('control #1', [[4, 2], [4, 0], False], [[4, 2], [4, 1], [4, 0]])],\n [('leftward corridor #1', [[5, 1], [2, 3], True], [[5, 1], [4, 1], [3, 1], [2, 1], [2, 2], [2, 3]]),\n  ('regression leg order flag #1',\n   [[7, 2], [6, 8], False],\n   [[7, 2], [7, 3], [7, 4], [7, 5], [7, 6], [7, 7], [7, 8], [6, 8]]),\n  ('fault site leg order flag #1',\n   [[0, 6], [5, 0], False],\n   [[0, 6], [0, 5], [0, 4], [0, 3], [0, 2], [0, 1], [0, 0], [1, 0], [2, 0], [3, 0], [4, 0], [5, 0]]),\n  ('regression leg order flag #2', [[5, 2], [3, 4], True], [[5, 2], [4, 2], [3, 2], [3, 3], [3, 4]]),\n  ('regression leg order flag #3', [[3, 8], [2, 7], True], [[3, 8], [2, 8], [2, 7]]),\n  ('same column #1', [[3, 0], [3, 2], False], [[3, 0], [3, 1], [3, 2]]),\n  ('single cell #1', [[4, 4], [4, 4], True], [[4, 4]]),\n  ('control #1', [[7, 6], [7, 3], False], [[7, 6], [7, 5], [7, 4], [7, 3]])]]\nfor label, args, expected in cases[N-1]:\n    check(label, solve(*args), expected)\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":"Deterministic toy contract stipulated for this model; integer or exact arithmetic only, not a reproduction of any specific game engine. 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":"w2-procedural-level-generation-constraints-l-corridor-leg-order-flag","generated_at":"2026-09-29T14:50:50.909315+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Procedural generators silently emit unplayable or unfair levels when a single constraint check uses the wrong boundary, axis, neighborhood or update order; the defect is visible in exact generated geometry.","root_cause":"The horizontal-first flag is negated.","sha256":"464d4329be79b638b9796efb87895b9200eb5b1bd88dc4df4111405807434154","title":"L-shaped corridor carving: Leg order is inverted · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":40.385,"exit_code":1,"observations":[{"actual":[[5,1],[5,2],[5,3],[4,3],[3,3],[2,3]],"check":"leftward corridor #1","expected":[[5,1],[4,1],[3,1],[2,1],[2,2],[2,3]],"passed":false},{"actual":[[8,6],[8,5],[7,5],[6,5],[5,5],[4,5]],"check":"regression leg order flag #1","expected":[[8,6],[7,6],[6,6],[5,6],[4,6],[4,5]],"passed":false},{"actual":[[8,4],[8,3],[8,2],[8,1],[8,0],[7,0],[6,0],[5,0],[4,0],[3,0],[2,0],[1,0]],"check":"regression leg order flag #2","expected":[[8,4],[7,4],[6,4],[5,4],[4,4],[3,4],[2,4],[1,4],[1,3],[1,2],[1,1],[1,0]],"passed":false},{"actual":[[7,2],[6,2],[6,3],[6,4],[6,5],[6,6],[6,7],[6,8]],"check":"regression leg order flag #3","expected":[[7,2],[7,3],[7,4],[7,5],[7,6],[7,7],[7,8],[6,8]],"passed":false},{"actual":[[5,1],[5,2],[5,3],[5,4],[5,5],[4,5]],"check":"regression leg order flag #4","expected":[[5,1],[4,1],[4,2],[4,3],[4,4],[4,5]],"passed":false},{"actual":[[3,0],[3,1],[3,2]],"check":"same column #1","expected":[[3,0],[3,1],[3,2]],"passed":true},{"actual":[[4,4]],"check":"single cell #1","expected":[[4,4]],"passed":true},{"actual":[[8,1],[8,2],[8,3]],"check":"control #1","expected":[[8,1],[8,2],[8,3]],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"leftward corridor #1\", \"actual\": [[5, 1], [5, 2], [5, 3], [4, 3], [3, 3], [2, 3]], \"expected\": [[5, 1], [4, 1], [3, 1], [2, 1], [2, 2], [2, 3]], \"passed\": false}, {\"check\": \"regression leg order flag #1\", \"actual\": [[8, 6], [8, 5], [7, 5], [6, 5], [5, 5], [4, 5]], \"expected\": [[8, 6], [7, 6], [6, 6], [5, 6], [4, 6], [4, 5]], \"passed\": false}, {\"check\": \"regression leg order flag #2\", \"actual\": [[8, 4], [8, 3], [8, 2], [8, 1], [8, 0], [7, 0], [6, 0], [5, 0], [4, 0], [3, 0], [2, 0], [1, 0]], \"expected\": [[8, 4], [7, 4], [6, 4], [5, 4], [4, 4], [3, 4], [2, 4], [1, 4], [1, 3], [1, 2], [1, 1], [1, 0]], \"passed\": false}, {\"check\": \"regression leg order flag #3\", \"actual\": [[7, 2], [6, 2], [6, 3], [6, 4], [6, 5], [6, 6], [6, 7], [6, 8]], \"expected\": [[7, 2], [7, 3], [7, 4], [7, 5], [7, 6], [7, 7], [7, 8], [6, 8]], \"passed\": false}, {\"check\": \"regression leg order flag #4\", \"actual\": [[5, 1], [5, 2], [5, 3], [5, 4], [5, 5], [4, 5]], \"expected\": [[5, 1], [4, 1], [4, 2], [4, 3], [4, 4], [4, 5]], \"passed\": false}, {\"check\": \"same column #1\", \"actual\": [[3, 0], [3, 1], [3, 2]], \"expected\": [[3, 0], [3, 1], [3, 2]], \"passed\": true}, {\"check\": \"single cell #1\", \"actual\": [[4, 4]], \"expected\": [[4, 4]], \"passed\": true}, {\"check\": \"control #1\", \"actual\": [[8, 1], [8, 2], [8, 3]], \"expected\": [[8, 1], [8, 2], [8, 3]], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":40.5,"exit_code":1,"observations":[{"actual":[[5,1],[5,2],[5,3],[4,3],[3,3],[2,3]],"check":"leftward corridor #1","expected":[[5,1],[4,1],[3,1],[2,1],[2,2],[2,3]],"passed":false},{"actual":[[8,6],[8,5],[7,5],[6,5],[5,5],[4,5]],"check":"regression leg order flag #1","expected":[[8,6],[7,6],[6,6],[5,6],[4,6],[4,5]],"passed":false},{"actual":[[8,4],[8,3],[8,2],[8,1],[8,0],[7,0],[6,0],[5,0],[4,0],[3,0],[2,0],[1,0]],"check":"regression leg order flag #2","expected":[[8,4],[7,4],[6,4],[5,4],[4,4],[3,4],[2,4],[1,4],[1,3],[1,2],[1,1],[1,0]],"passed":false},{"actual":[[7,2],[6,2],[6,3],[6,4],[6,5],[6,6],[6,7],[6,8]],"check":"regression leg order flag #3","expected":[[7,2],[7,3],[7,4],[7,5],[7,6],[7,7],[7,8],[6,8]],"passed":false},{"actual":[[5,1],[5,2],[5,3],[5,4],[5,5],[4,5]],"check":"regression leg order flag #4","expected":[[5,1],[4,1],[4,2],[4,3],[4,4],[4,5]],"passed":false},{"actual":[[3,0],[3,1],[3,2]],"check":"same column #1","expected":[[3,0],[3,1],[3,2]],"passed":true},{"actual":[[4,4]],"check":"single cell #1","expected":[[4,4]],"passed":true},{"actual":[[8,1],[8,2],[8,3]],"check":"control #1","expected":[[8,1],[8,2],[8,3]],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"leftward corridor #1\", \"actual\": [[5, 1], [5, 2], [5, 3], [4, 3], [3, 3], [2, 3]], \"expected\": [[5, 1], [4, 1], [3, 1], [2, 1], [2, 2], [2, 3]], \"passed\": false}, {\"check\": \"regression leg order flag #1\", \"actual\": [[8, 6], [8, 5], [7, 5], [6, 5], [5, 5], [4, 5]], \"expected\": [[8, 6], [7, 6], [6, 6], [5, 6], [4, 6], [4, 5]], \"passed\": false}, {\"check\": \"regression leg order flag #2\", \"actual\": [[8, 4], [8, 3], [8, 2], [8, 1], [8, 0], [7, 0], [6, 0], [5, 0], [4, 0], [3, 0], [2, 0], [1, 0]], \"expected\": [[8, 4], [7, 4], [6, 4], [5, 4], [4, 4], [3, 4], [2, 4], [1, 4], [1, 3], [1, 2], [1, 1], [1, 0]], \"passed\": false}, {\"check\": \"regression leg order flag #3\", \"actual\": [[7, 2], [6, 2], [6, 3], [6, 4], [6, 5], [6, 6], [6, 7], [6, 8]], \"expected\": [[7, 2], [7, 3], [7, 4], [7, 5], [7, 6], [7, 7], [7, 8], [6, 8]], \"passed\": false}, {\"check\": \"regression leg order flag #4\", \"actual\": [[5, 1], [5, 2], [5, 3], [5, 4], [5, 5], [4, 5]], \"expected\": [[5, 1], [4, 1], [4, 2], [4, 3], [4, 4], [4, 5]], \"passed\": false}, {\"check\": \"same column #1\", \"actual\": [[3, 0], [3, 1], [3, 2]], \"expected\": [[3, 0], [3, 1], [3, 2]], \"passed\": true}, {\"check\": \"single cell #1\", \"actual\": [[4, 4]], \"expected\": [[4, 4]], \"passed\": true}, {\"check\": \"control #1\", \"actual\": [[8, 1], [8, 2], [8, 3]], \"expected\": [[8, 1], [8, 2], [8, 3]], \"passed\": true}], \"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."}}