{"abstract":"Equal-distance candidates pick the wrong boss room.","category":"Procedural level generation constraints","checks":8,"contract":"Rooms 0..n-1 joined by two-way doors [a, b]. The boss room is the reachable room (other than start) with the largest BFS door distance from start, ties to the lowest room id. Returns [room, distance] or None when no other room is reachable.","contract_signature":"n, doors, start","evaluation_group":"w2-procedural-level-generation-constraints-boss-room","failed_approach":"Letting later entries win makes the result depend on BFS order.","family":"w2-procedural-level-generation-constraints-boss-room-tie-break","id":"FA-86651","implementations":{"attempt":{"sha256":"48c7f664f3d6dbe0880a848ddda2203432bfe45ad1972eab01c57dec17d849f1","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(n, doors, start):\n    adj = {i: [] for i in range(n)}\n    for a, b in doors:\n        adj[a].append(b)\n        adj[b].append(a)\n    dist = {start: 0}\n    queue = [start]\n    for u in queue:\n        for v in sorted(adj[u]):\n            if v not in dist:\n                dist[v] = dist[u] + 1\n                queue.append(v)\n    best = None\n    for room, d in dist.items():\n        if room == start:\n            continue\n        if best is None or d >= best[1]:\n            best = [room, d]\n    return best\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncases = [[('two leaves tie #1', [3, [[0, 2], [0, 1]], 0], [1, 1]),\n  ('regression tie break #1', [6, [[0, 1], [1, 2], [1, 3], [1, 4], [2, 0], [1, 4]], 2], [3, 2]),\n  ('regression tie break #2', [8, [[0, 1], [1, 2], [1, 4], [5, 2], [3, 6], [7, 3], [3, 7]], 2], [0, 2]),\n  ('regression tie break #3', [7, [[0, 1], [3, 0], [4, 1], [5, 4], [6, 5], [4, 0]], 4], [3, 2]),\n  ('regression tie break #4', [4, [[0, 1], [2, 0], [1, 3], [2, 3], [0, 3]], 0], [1, 1]),\n  ('door listed backwards #1', [2, [[1, 0]], 0], [1, 1]),\n  ('isolated start #1', [3, [[1, 2]], 0], None),\n  ('control #1', [1, [], 0], None)],\n [('two leaves tie #1', [3, [[0, 2], [0, 1]], 0], [1, 1]),\n  ('regression tie break #1', [8, [[0, 1], [1, 2], [1, 4], [5, 2], [3, 6], [7, 3], [3, 7]], 2], [0, 2]),\n  ('regression tie break #2', [7, [[0, 1], [3, 0], [4, 1], [5, 4], [6, 5], [4, 0]], 4], [3, 2]),\n  ('regression tie break #3', [4, [[0, 1], [2, 0], [1, 3], [2, 3], [0, 3]], 0], [1, 1]),\n  ('regression tie break #4', [3, [[2, 0], [0, 1]], 0], [1, 1]),\n  ('door listed backwards #1', [2, [[1, 0]], 0], [1, 1]),\n  ('isolated start #1', [3, [[1, 2]], 0], None),\n  ('control #1', [1, [], 0], None)],\n [('two leaves tie #1', [3, [[0, 2], [0, 1]], 0], [1, 1]),\n  ('regression tie break #1', [4, [[0, 1], [2, 0], [1, 3], [2, 3], [0, 3]], 0], [1, 1]),\n  ('regression tie break #2', [3, [[2, 0], [0, 1]], 0], [1, 1]),\n  ('regression tie break #3', [6, [[0, 1], [0, 3], [5, 0]], 0], [1, 1]),\n  ('regression tie break #4', [8, [[0, 1], [2, 0], [0, 4], [3, 5], [7, 6], [4, 3], [4, 6]], 7], [1, 4]),\n  ('door listed backwards #1', [2, [[1, 0]], 0], [1, 1]),\n  ('isolated start #1', [3, [[1, 2]], 0], None),\n  ('control #1', [1, [], 0], None)],\n [('two leaves tie #1', [3, [[0, 2], [0, 1]], 0], [1, 1]),\n  ('regression tie break #1', [6, [[0, 1], [0, 3], [5, 0]], 0], [1, 1]),\n  ('regression tie break #2', [8, [[0, 1], [2, 0], [0, 4], [3, 5], [7, 6], [4, 3], [4, 6]], 7], [1, 4]),\n  ('regression tie break #3', [6, [[2, 1], [3, 1], [1, 5]], 3], [2, 2]),\n  ('regression tie break #4', [4, [[1, 0], [2, 0], [3, 0]], 0], [1, 1]),\n  ('door listed backwards #1', [2, [[1, 0]], 0], [1, 1]),\n  ('isolated start #1', [3, [[1, 2]], 0], None),\n  ('control #1', [8, [[1, 0], [1, 3], [3, 4], [4, 5], [0, 6], [7, 4], [3, 7]], 2], None)],\n [('two leaves tie #1', [3, [[0, 2], [0, 1]], 0], [1, 1]),\n  ('regression tie break #1', [6, [[2, 1], [3, 1], [1, 5]], 3], [2, 2]),\n  ('regression tie break #2', [4, [[1, 0], [2, 0], [3, 0]], 0], [1, 1]),\n  ('regression tie break #3', [3, [[2, 0], [1, 2], [1, 2]], 2], [0, 1]),\n  ('regression tie break #4', [6, [[0, 1], [0, 2], [2, 3], [0, 4], [5, 4]], 0], [3, 2]),\n  ('door listed backwards #1', [2, [[1, 0]], 0], [1, 1]),\n  ('isolated start #1', [3, [[1, 2]], 0], None),\n  ('control #1', [5, [[0, 1], [0, 2], [3, 0], [0, 3]], 4], None)]]\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":"cec56a581efa91093bd40a3f6254116c0671bc7fab8426f27cb8f0b68018a94a","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(n, doors, start):\n    adj = {i: [] for i in range(n)}\n    for a, b in doors:\n        adj[a].append(b)\n        adj[b].append(a)\n    dist = {start: 0}\n    queue = [start]\n    for u in queue:\n        for v in sorted(adj[u]):\n            if v not in dist:\n                dist[v] = dist[u] + 1\n                queue.append(v)\n    best = None\n    for room, d in dist.items():\n        if room == start:\n            continue\n        if best is None or d > best[1] or (d == best[1] and room > best[0]):\n            best = [room, d]\n    return best\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncases = [[('two leaves tie #1', [3, [[0, 2], [0, 1]], 0], [1, 1]),\n  ('regression tie break #1', [6, [[0, 1], [1, 2], [1, 3], [1, 4], [2, 0], [1, 4]], 2], [3, 2]),\n  ('regression tie break #2', [8, [[0, 1], [1, 2], [1, 4], [5, 2], [3, 6], [7, 3], [3, 7]], 2], [0, 2]),\n  ('regression tie break #3', [7, [[0, 1], [3, 0], [4, 1], [5, 4], [6, 5], [4, 0]], 4], [3, 2]),\n  ('regression tie break #4', [4, [[0, 1], [2, 0], [1, 3], [2, 3], [0, 3]], 0], [1, 1]),\n  ('door listed backwards #1', [2, [[1, 0]], 0], [1, 1]),\n  ('isolated start #1', [3, [[1, 2]], 0], None),\n  ('control #1', [1, [], 0], None)],\n [('two leaves tie #1', [3, [[0, 2], [0, 1]], 0], [1, 1]),\n  ('regression tie break #1', [8, [[0, 1], [1, 2], [1, 4], [5, 2], [3, 6], [7, 3], [3, 7]], 2], [0, 2]),\n  ('regression tie break #2', [7, [[0, 1], [3, 0], [4, 1], [5, 4], [6, 5], [4, 0]], 4], [3, 2]),\n  ('regression tie break #3', [4, [[0, 1], [2, 0], [1, 3], [2, 3], [0, 3]], 0], [1, 1]),\n  ('regression tie break #4', [3, [[2, 0], [0, 1]], 0], [1, 1]),\n  ('door listed backwards #1', [2, [[1, 0]], 0], [1, 1]),\n  ('isolated start #1', [3, [[1, 2]], 0], None),\n  ('control #1', [1, [], 0], None)],\n [('two leaves tie #1', [3, [[0, 2], [0, 1]], 0], [1, 1]),\n  ('regression tie break #1', [4, [[0, 1], [2, 0], [1, 3], [2, 3], [0, 3]], 0], [1, 1]),\n  ('regression tie break #2', [3, [[2, 0], [0, 1]], 0], [1, 1]),\n  ('regression tie break #3', [6, [[0, 1], [0, 3], [5, 0]], 0], [1, 1]),\n  ('regression tie break #4', [8, [[0, 1], [2, 0], [0, 4], [3, 5], [7, 6], [4, 3], [4, 6]], 7], [1, 4]),\n  ('door listed backwards #1', [2, [[1, 0]], 0], [1, 1]),\n  ('isolated start #1', [3, [[1, 2]], 0], None),\n  ('control #1', [1, [], 0], None)],\n [('two leaves tie #1', [3, [[0, 2], [0, 1]], 0], [1, 1]),\n  ('regression tie break #1', [6, [[0, 1], [0, 3], [5, 0]], 0], [1, 1]),\n  ('regression tie break #2', [8, [[0, 1], [2, 0], [0, 4], [3, 5], [7, 6], [4, 3], [4, 6]], 7], [1, 4]),\n  ('regression tie break #3', [6, [[2, 1], [3, 1], [1, 5]], 3], [2, 2]),\n  ('regression tie break #4', [4, [[1, 0], [2, 0], [3, 0]], 0], [1, 1]),\n  ('door listed backwards #1', [2, [[1, 0]], 0], [1, 1]),\n  ('isolated start #1', [3, [[1, 2]], 0], None),\n  ('control #1', [8, [[1, 0], [1, 3], [3, 4], [4, 5], [0, 6], [7, 4], [3, 7]], 2], None)],\n [('two leaves tie #1', [3, [[0, 2], [0, 1]], 0], [1, 1]),\n  ('regression tie break #1', [6, [[2, 1], [3, 1], [1, 5]], 3], [2, 2]),\n  ('regression tie break #2', [4, [[1, 0], [2, 0], [3, 0]], 0], [1, 1]),\n  ('regression tie break #3', [3, [[2, 0], [1, 2], [1, 2]], 2], [0, 1]),\n  ('regression tie break #4', [6, [[0, 1], [0, 2], [2, 3], [0, 4], [5, 4]], 0], [3, 2]),\n  ('door listed backwards #1', [2, [[1, 0]], 0], [1, 1]),\n  ('isolated start #1', [3, [[1, 2]], 0], None),\n  ('control #1', [5, [[0, 1], [0, 2], [3, 0], [0, 3]], 4], None)]]\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-boss-room-tie-break","generated_at":"2026-09-29T14:50:51.500668+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":"Ties prefer larger room ids.","sha256":"8684838e548783882302597079f0425802124e6ffe504c8012129628832504e4","title":"Boss room selection: Ties choose the highest room id · 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":43.216,"exit_code":1,"observations":[{"actual":[2,1],"check":"two leaves tie #1","expected":[1,1],"passed":false},{"actual":[4,2],"check":"regression tie break #1","expected":[3,2],"passed":false},{"actual":[4,2],"check":"regression tie break #2","expected":[0,2],"passed":false},{"actual":[6,2],"check":"regression tie break #3","expected":[3,2],"passed":false},{"actual":[3,1],"check":"regression tie break #4","expected":[1,1],"passed":false},{"actual":[1,1],"check":"door listed backwards #1","expected":[1,1],"passed":true},{"actual":null,"check":"isolated start #1","expected":null,"passed":true},{"actual":null,"check":"control #1","expected":null,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"two leaves tie #1\", \"actual\": [2, 1], \"expected\": [1, 1], \"passed\": false}, {\"check\": \"regression tie break #1\", \"actual\": [4, 2], \"expected\": [3, 2], \"passed\": false}, {\"check\": \"regression tie break #2\", \"actual\": [4, 2], \"expected\": [0, 2], \"passed\": false}, {\"check\": \"regression tie break #3\", \"actual\": [6, 2], \"expected\": [3, 2], \"passed\": false}, {\"check\": \"regression tie break #4\", \"actual\": [3, 1], \"expected\": [1, 1], \"passed\": false}, {\"check\": \"door listed backwards #1\", \"actual\": [1, 1], \"expected\": [1, 1], \"passed\": true}, {\"check\": \"isolated start #1\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"control #1\", \"actual\": null, \"expected\": null, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":38.912,"exit_code":1,"observations":[{"actual":[2,1],"check":"two leaves tie #1","expected":[1,1],"passed":false},{"actual":[4,2],"check":"regression tie break #1","expected":[3,2],"passed":false},{"actual":[4,2],"check":"regression tie break #2","expected":[0,2],"passed":false},{"actual":[6,2],"check":"regression tie break #3","expected":[3,2],"passed":false},{"actual":[3,1],"check":"regression tie break #4","expected":[1,1],"passed":false},{"actual":[1,1],"check":"door listed backwards #1","expected":[1,1],"passed":true},{"actual":null,"check":"isolated start #1","expected":null,"passed":true},{"actual":null,"check":"control #1","expected":null,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"two leaves tie #1\", \"actual\": [2, 1], \"expected\": [1, 1], \"passed\": false}, {\"check\": \"regression tie break #1\", \"actual\": [4, 2], \"expected\": [3, 2], \"passed\": false}, {\"check\": \"regression tie break #2\", \"actual\": [4, 2], \"expected\": [0, 2], \"passed\": false}, {\"check\": \"regression tie break #3\", \"actual\": [6, 2], \"expected\": [3, 2], \"passed\": false}, {\"check\": \"regression tie break #4\", \"actual\": [3, 1], \"expected\": [1, 1], \"passed\": false}, {\"check\": \"door listed backwards #1\", \"actual\": [1, 1], \"expected\": [1, 1], \"passed\": true}, {\"check\": \"isolated start #1\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"control #1\", \"actual\": null, \"expected\": null, \"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."}}