sn99-router-min / proofs /87569.json
failmint's picture
Upload proofs/87569.json with huggingface_hub
29d0fe9 verified
Raw
History Blame Contribute Delete
16.6 kB
{"schema": 2, "epoch": 87569, "nonce": "9a38438598412408", "hotkey": "5CaXH581GtSjxFaFaJzSN35CyKzvFMQhRAxrNy6qmoztNiPz", "source_hash": "24837b9ae6895829747c5eb448673693fedbeb2e3e62a5ca170051a512053fc0", "weights_hash": "19aafcfeb694bb06e7fbbedbfd7fce6c50bd3389a57eeefd1b5d2e5475afdd62", "model_id": "router", "total_cost_usd": 0.00627808, "n_calls": 6, "call_log_hash": "ea22ab6503804cf704714d77d4d26676e7bdc5de57cc1507356fe2de2b9ad6e0", "measurement": "1449fadb4821cadef93f7eecc8c3b040e2cd244e01a2607ea531e5f7055c38d8", "confined": true, "latency_s": 293.916, "tokens_in": 2364, "tokens_out": 45161, "results": [{"benchmark": "mmlu", "task_id": "mmlu-7563", "answer": "B", "cost_usd": 7.5e-06, "chosen_rung": 4, "rungs_used": [4], "distribution": [0.093242, 0.000381, 0.000339, 0.000488, 0.904383, 0.000898, 0.00027]}, {"benchmark": "mmlu", "task_id": "mmlu-9954", "answer": "A", "cost_usd": 2.0300000000000002e-05, "chosen_rung": 4, "rungs_used": [4], "distribution": [0.034478, 0.000227, 0.000218, 0.000296, 0.964039, 0.000549, 0.000192]}, {"benchmark": "math", "task_id": "gsm8k-642", "answer": "10 suits: \\(10 \\times 750 = 7{,}500\\)\n\n10 dress pants: \\(10 \\times (750 \\div 5) = 10 \\times 150 = 1{,}500\\)\n\nDress shirts: \\(10 \\times 3 = 30\\) shirts, and \\(30 \\times 60 = 1{,}800\\)\n\nTotal: \\(7{,}500 + 1{,}500 + 1{,}800 = 10{,}800\\)\n\n10800", "cost_usd": 0.00012550000000000001, "chosen_rung": 4, "rungs_used": [4], "distribution": [0.001296, 2.5e-05, 2.2e-05, 3.3e-05, 0.998529, 7.5e-05, 2e-05]}, {"benchmark": "math", "task_id": "gsm8k-419", "answer": "1. The comb fell from 10,000 feet to \\(10{,}000 - 4{,}000 = 6{,}000\\) feet.\n2. Oliver is at 3,000 feet, so he must climb \\(6{,}000 - 3{,}000 = 3{,}000\\) feet.\n\n3000", "cost_usd": 9.92e-05, "chosen_rung": 4, "rungs_used": [4], "distribution": [0.014986, 9.9e-05, 8.7e-05, 0.000123, 0.984356, 0.000271, 7.7e-05]}, {"benchmark": "code", "task_id": "lcb-abc395_b", "answer": "import sys\n\nN = int(sys.stdin.readline())\nfor i in range(1, N + 1):\n row = []\n for j in range(1, N + 1):\n depth = min(i, j, N + 1 - i, N + 1 - j)\n row.append('#' if depth % 2 == 1 else '.')\n print(''.join(row))", "cost_usd": 0.00018840000000000003, "chosen_rung": 4, "rungs_used": [4], "distribution": [0.010037, 3.2e-05, 2.4e-05, 3.6e-05, 0.989772, 7.9e-05, 2.1e-05]}, {"benchmark": "code", "task_id": "lcb-abc400_d", "answer": "```python\nimport sys\nfrom collections import deque\n\ndef solve():\n # Read all input from stdin efficiently\n input_data = sys.stdin.read().split()\n if not input_data:\n return\n\n iterator = iter(input_data)\n \n try:\n H = int(next(iterator))\n W = int(next(iterator))\n \n S = []\n for _ in range(H):\n S.append(next(iterator))\n \n A = int(next(iterator))\n B = int(next(iterator))\n C = int(next(iterator))\n D = int(next(iterator))\n except StopIteration:\n return\n\n # Convert 1-based coordinates to 0-based\n start_r, start_c = A - 1, B - 1\n target_r, target_c = C - 1, D - 1\n\n # Distance matrix initialized to infinity\n INF = 10**9\n dist = [[INF] * W for _ in range(H)]\n \n # Deque for 0-1 BFS\n dq = deque()\n \n # Initial state\n dist[start_r][start_c] = 0\n dq.append((start_r, start_c))\n \n # Directions: Up, Down, Left, Right\n directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]\n \n while dq:\n r, c = dq.popleft()\n d = dist[r][c]\n \n # If we reached the target, print and exit\n # Since it's 0-1 BFS, the first time we pop the target, it's the shortest path\n if r == target_r and c == target_c:\n print(d)\n return\n\n # 1. Move to adjacent road cells (Cost 0)\n # We can only move to cells that are currently roads ('.')\n # Note: If a cell was a wall but cleared by a kick, its distance would have been updated,\n # so we don't strictly need to move into it to discover it, but we might need to move\n # to explore further. However, since cleared walls have low distance, they are in the queue.\n for dr, dc in directions:\n nr, nc = r + dr, c + dc\n if 0 <= nr < H and 0 <= nc < W:\n if S[nr][nc] == '.':\n if dist[nr][nc] > d:\n dist[nr][nc] = d\n dq.appendleft((nr, nc))\n \n # 2. Perform front kick (Cost 1)\n # A kick at (r, c) in a direction clears cells at distance 1 and 2 in that direction.\n # These cells become roads, so we can reach them with cost d + 1.\n for dr, dc in directions:\n # Cell at distance 1\n tr1, tc1 = r + dr, c + dc\n if 0 <= tr1 < H and 0 <= tc1 < W:\n # This cell becomes a road.\n # We can reach it with cost d + 1.\n if dist[tr1][tc1] > d + 1:\n dist[tr1][tc1] = d + 1\n dq.append((tr1, tc1))\n \n # Cell at distance 2\n tr2, tc2 = r + 2*dr, c + 2*dc\n if 0 <= tr2 < H and 0 <= tc2 < W:\n # This cell becomes a road.\n if dist[tr2][tc2] > d + 1:\n dist[tr2][tc2] = d + 1\n dq.append((tr2, tc2))\n\n # If target is unreachable (should not happen based on problem statement)\n print(dist[target_r][target_c])\n\nif __name__ == '__main__':\n solve()\n```", "cost_usd": 0.00583718, "chosen_rung": 0, "rungs_used": [0], "distribution": [0.997247, 2.1e-05, 1.4e-05, 2e-05, 0.002637, 4.7e-05, 1.4e-05]}], "quote": {"measurement": "1449fadb4821cadef93f7eecc8c3b040e2cd244e01a2607ea531e5f7055c38d8", "report_data": "00518fbf292fa04ab377ab620575febd89f155a91c7b80fb7899d1b3ea47e26a", "platform_sig": "tdx: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"}}