bandit-delayed-feedback
machine-learning timeout 6 turns 2025s 6 tool calls
reconstructed
TBLite drops the in‑progress transcript when it kills a timing‑out task.
The events below were captured turn‑by‑turn by the agent‑loop patch
and pieced back together — full content, reasoning, and tool calls are
all real; they just came from a different stream than the other tasks.
turn 1 · tool · read_file
{"content": " 1|impression_id,arm_id,timestamp,hour_of_day,day_of_week,user_segment,device_type,context_feature_1,context_feature_2\n 2|IMP_000000,0,1709340758,9,5,young,mobile,0.7797,0.5969\n 3|IMP_000001,6,1710354874,3,3,young,desktop,0.7081,0.0206\n 4|IMP_000002,9,1710010543,3,6,young,desktop,0.6117,0.0071\n 5|IMP_000003,3,1710416879,20,3,senior,mobile,0.7852,0.1997\n 6|IMP_000004,8,1709492338,3,0,young,desktop,0.1705,0.0651\n 7|IMP_000005,4,1709917161,1,5,young,mobile,0.2309,0.241\n 8|IMP_000006,3,1710022391,7,6,senior,mobile,0.3911,0.1822\n 9|IMP_000007,5,1709490636,3,0,senior,desktop,0.4498,0.3952\n 10|IMP_000008,1,1709706679,15,2,middle,mobile,0.0452,0.3253\n 11|", "total_lines": 50001, "file_size": 2818804, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 of 50001 lines)", "is_binary": false, "is_image": false}
turn 1 · assistant 25.5s api
→ read_file({"path": "/app/data/impressions.csv", "limit": 10})
turn 2 · tool · read_file
{"content": " 1|impression_id,conversion_timestamp,delay_hours,conversion_value\n 2|IMP_000010,1709305348,10.43,3.58\n 3|IMP_000013,1709320719,21.49,1.07\n 4|IMP_000022,1709843815,9.32,3.47\n 5|IMP_000069,1710312110,4.57,2.48\n 6|IMP_000071,1710446648,25.86,1.37\n 7|IMP_000090,1709973192,28.94,1.46\n 8|IMP_000097,1710100917,68.48,1.67\n 9|IMP_000109,1709851031,16.83,1.67\n 10|IMP_000119,1709561787,17.17,3.28\n 11|", "total_lines": 4175, "file_size": 135100, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 of 4175 lines)", "is_binary": false, "is_image": false}
turn 2 · assistant 12.7s api
→ read_file({"path": "/app/data/conversions.csv", "limit": 10})
turn 3 · tool · read_file
{"content": " 1|{\n 2| \"arms\": [\n 3| {\n 4| \"arm_id\": 0,\n 5| \"name\": \"Campaign A\",\n 6| \"category\": \"electronics\",\n 7| \"base_bid\": 0.3\n 8| },\n 9| {\n 10| \"arm_id\": 1,\n 11| \"name\": \"Campaign B\",\n 12| \"category\": \"fashion\",\n 13| \"base_bid\": 0.4\n 14| },\n 15| {\n 16| \"arm_id\": 2,\n 17| \"name\": \"Campaign C\",\n 18| \"category\": \"home\",\n 19| \"base_bid\": 0.5\n 20| },\n 21| {\n 22| \"arm_id\": 3,\n 23| \"name\": \"Campaign D\",\n 24| \"category\": \"sports\",\n 25| \"base_bid\": 0.6\n 26| },\n 27| {\n 28| \"arm_id\": 4,\n 29| \"name\": \"Campaign E\",\n 30| \"category\": \"beauty\",\n 31| \"base_bid\": 0.7\n 32| },\n 33| {\n 34| \"arm_id\": 5,\n 35| \"name\": \"Campaign F\",\n 36| \"category\": \"food\",\n 37| \"base_bid\": 0.8\n 38| },\n 39| {\n 40| \"arm_id\": 6,\n 41| \"name\": \"Campaign G\",\n 42| \"category\": \"travel\",\n 43| \"base_bid\": 0.9\n 44| },\n 45| {\n 46| \"arm_id\": 7,\n 47| \"name\": \"Campaign H\",\n 48| \"category\": \"finance\",\n 49| \"base_bid\": 1.0\n 50| },\n 51| {\n 52| \"arm_id\": 8,\n 53| \"name\": \"Campaign I\",\n 54| \"category\": \"entertainment\",\n 55| \"base_bid\": 1.1\n 56| },\n 57| {\n 58| \"arm_id\": 9,\n 59| \"name\": \"Campaign J\",\n 60| \"category\": \"health\",\n 61| \"base_bid\": 1.2\n 62| }\n 63| ]\n 64|}", "total_lines": 63, "file_size": 1128, "truncated": false, "is_binary": false, "is_image": false}
turn 3 · assistant 10.7s api
→ read_file({"path": "/app/data/arms_metadata.json"})
turn 4 · tool · read_file
{"content": " 1|context_id,timestamp,hour_of_day,day_of_week,user_segment,device_type,context_feature_1,context_feature_2\n 2|CTX_000000,1710307872,14,2,middle,mobile,0.747,0.0933\n 3|CTX_000001,1710319811,17,2,senior,mobile,0.9059,0.1018\n 4|CTX_000002,1710341834,23,2,senior,mobile,0.0783,0.8936\n 5|CTX_000003,1710405538,17,3,middle,mobile,0.4505,0.7765\n 6|CTX_000004,1710411058,19,3,middle,mobile,0.1365,0.0416\n 7|CTX_000005,1710408367,18,3,middle,desktop,0.9182,0.4332\n 8|CTX_000006,1710363663,6,3,young,tablet,0.8606,0.5293\n 9|CTX_000007,1710307909,14,2,young,mobile,0.3778,0.9319\n 10|CTX_000008,1710410830,19,3,senior,desktop,0.0584,0.8807\n 11|", "total_lines": 2001, "file_size": 108862, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 of 2001 lines)", "is_binary": false, "is_image": false}
turn 4 · assistant 17.1s api
→ read_file({"path": "/app/data/test_contexts.csv", "limit": 10})
turn 5 · tool · terminal
{"output": "bash: eval: line 200: unexpected EOF while looking for matching `\"'", "exit_code": 2, "error": null}
turn 5 · assistant 1043.1s api
→ terminal({"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport json\nfrom scipy.stats import lognorm\nimport os\n\n# Set random seed for reproducibility\nnp.random.seed(42)\n\n# Load data\nimpressions = pd.read_csv('/app/data/impressions.csv')\nimpressions['timestamp'] = pd.to_datetime(impressions['timestamp'], unit='s')\nconversions = pd.read_csv('/app/data/conversions.csv')\nconvers…)
turn 6 · tool · terminal
{"output": "bash: eval: line 200: unexpected EOF while looking for matching `\"'", "exit_code": 2, "error": null}
turn 6 · assistant 916.3s api
→ terminal({"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport json\nfrom scipy.stats import lognorm\nimport os\n\n# Set random seed for reproducibility\nnp.random.seed(42)\n\n# Load data\nimpressions = pd.read_csv('/app/data/impressions.csv')\nimpressions['timestamp'] = pd.to_datetime(impressions['timestamp'], unit='s')\nconversions = pd.read_csv('/app/data/conversions.csv')\nconvers…)