conversations list | agent string | model string | model_provider string | date string | task string | episode string | run_id string | trial_name string | result string | instruction string | verifier_output string | prompt_token_ids list | completion_token_ids list | logprobs list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[{"content":"","role":"user"},{"content":"<think>The user wants me to implement a Kedro-like Node ab(...TRUNCATED) | opencode | vllm/9647069541629285 | vllm | 2026-07-11T19:48:30.499648Z | 5k-adversarial-1415 | episode-10 | 05a75ff4-f5e3-4156-bae1-e2b718da890b | 5k-adversarial-1415__QQ97v8U | AgentTimeoutError | Let me search for test files that might be related to this task: | null | [[248045,8678,198,2,13455,271,2523,599,2528,310,279,2614,5568,25,271,27,15449,29,198,4754,1267,763,3(...TRUNCATED) | [[760,1156,6587,728,310,4072,264,155954,298,12317,5829,56764,440,264,8369,709,321,5829,523,13,6558,7(...TRUNCATED) | [[-0.21058163046836853,-0.0009220149368047714,-0.003677153727039695,-0.000014781810023123398,-7.1525(...TRUNCATED) |
[{"content":"","role":"user"},{"content":"<think>The user wants me to implement a FileAlchemy utilit(...TRUNCATED) | opencode | vllm/9647069541629285 | vllm | 2026-07-12T08:04:51.265313Z | 5k-adversarial-0420 | episode-4 | 05a75ff4-f5e3-4156-bae1-e2b718da890b | 5k-adversarial-0420__6SysvBx | AgentTimeoutError | "I see the /app directory is empty. I need to create the Flask-FileAlchemy implementation from scrat(...TRUNCATED) | null | [] | [] | [] |
[{"content":"","role":"user"},{"content":"<think>The user wants me to implement an asynchronous emai(...TRUNCATED) | opencode | vllm/9647069541629285 | vllm | 2026-07-09T10:19:12.465723Z | 5k-adversarial-4792 | episode-18 | 05a75ff4-f5e3-4156-bae1-e2b718da890b | 5k-adversarial-4792__MDtRPTV | 0.0 | (tool use) | "Creating virtual environment...\nInstalling pytest...\nRunning pytest...\n=========================(...TRUNCATED) | [[248045,8678,198,2,13455,271,2523,599,2528,310,279,2614,5568,25,271,27,15449,29,198,4754,1267,763,3(...TRUNCATED) | [[760,1156,6587,728,310,4072,449,37715,2469,1876,4540,709,13,6558,728,1118,12804,279,1970,3051,310,3(...TRUNCATED) | [[-0.06260738521814346,-0.0007328674546442926,-0.04323504492640495,-0.000067708439019043,-5.96045083(...TRUNCATED) |
[{"content":"","role":"user"},{"content":"<think>The user wants me to implement a greedy coloring ut(...TRUNCATED) | opencode | vllm/9647069541629285 | vllm | 2026-07-12T07:29:26.116868Z | 5k-adversarial-0474 | episode-6 | 05a75ff4-f5e3-4156-bae1-e2b718da890b | 5k-adversarial-0474__SCsVHkS | AgentTimeoutError | "The workspace is empty. I need to create the entire project structure from scratch. Let me implemen(...TRUNCATED) | null | [[248045,8678,198,2,13455,271,2523,599,2528,310,279,2614,5568,25,271,27,15449,29,198,4754,1267,763,3(...TRUNCATED) | [[760,1156,6587,728,310,4072,264,54102,33730,15092,440,5081,11597,23065,286,14400,13,6558,728,1118,1(...TRUNCATED) | [[-0.3022925555706024,-0.0003048905055038631,-0.03374980390071869,-0.000012874488675151952,-2.384180(...TRUNCATED) |
[{"content":"","role":"user"},{"content":"<think>The user wants me to implement a BoxCoxTransformer (...TRUNCATED) | opencode | vllm/9647069541629285 | vllm | 2026-07-10T09:13:44.468069Z | 5k-adversarial-3530 | episode-3 | 05a75ff4-f5e3-4156-bae1-e2b718da890b | 5k-adversarial-3530__2yoKYvA | AgentTimeoutError | null | null | [[248045,8678,198,2,13455,271,2523,599,2528,310,279,2614,5568,25,271,27,15449,29,198,4754,1267,763,3(...TRUNCATED) | [[760,1156,6587,728,310,4072,264,8003,34,4967,44812,523,303,4416,24038,77358,1570,6971,13,6558,728,1(...TRUNCATED) | [[-0.02488027699291706,-0.00025078444741666317,-0.11315203458070755,-0.00010322968591935933,-2.38418(...TRUNCATED) |
[{"content":"","role":"user"},{"content":"<think>The user wants me to implement a Python module for (...TRUNCATED) | opencode | vllm/9647069541629285 | vllm | 2026-07-10T09:00:14.320169Z | 5k-adversarial-3544 | episode-3 | 05a75ff4-f5e3-4156-bae1-e2b718da890b | 5k-adversarial-3544__6ZVS9UU | AgentTimeoutError | null | null | [[248045,8678,198,2,13455,271,2523,599,2528,310,279,2614,5568,25,271,27,15449,29,198,4754,1267,763,3(...TRUNCATED) | [[760,1156,6587,728,310,4072,264,12654,4536,364,53304,9672,627,53,18,17066,421,15734,264,3050,1228,1(...TRUNCATED) | [[-0.08348855376243591,-0.00018869027553591877,-0.20245057344436646,-0.00004863608774030581,-3.69547(...TRUNCATED) |
[{"content":"","role":"user"},{"content":"<think>The user wants me to implement a function `ridge_al(...TRUNCATED) | opencode | vllm/9647069541629285 | vllm | 2026-07-11T21:45:50.269623Z | 5k-adversarial-1260 | episode-7 | 05a75ff4-f5e3-4156-bae1-e2b718da890b | 5k-adversarial-1260__8jiySDK | VerifierTimeoutError | "The `ridge_alpha_tuning` function has been implemented in `/app/src/analysis/alpha_tuning.py`. The (...TRUNCATED) | null | [[248045,8678,198,2,13455,271,2523,599,2528,310,279,2614,5568,25,271,27,15449,29,198,4754,1267,763,3(...TRUNCATED) | [[760,1156,6587,728,310,4072,264,709,1510,10858,25781,516,35971,63,303,1510,3431,14,33340,14,6918,51(...TRUNCATED) | [[-0.060820817947387695,-0.0011823319364339113,-0.00333550781942904,-2.8610108984139515e-6,0.0,-0.00(...TRUNCATED) |
[{"content":"","role":"user"},{"content":"<think>The user is asking me to implement a composite_desc(...TRUNCATED) | opencode | vllm/9647069541629285 | vllm | 2026-07-11T14:23:59.142139Z | 5k-adversarial-1888 | episode-24 | 05a75ff4-f5e3-4156-bae1-e2b718da890b | 5k-adversarial-1888__VA3wnoQ | AgentTimeoutError | "I'll implement the composite_descent utility for Dagster. Let me start by exploring the codebase st(...TRUNCATED) | null | [[248045,8678,198,2,13455,271,2523,599,2528,310,279,2614,5568,25,271,27,15449,29,198,4754,1267,763,3(...TRUNCATED) | [[760,1156,369,9859,728,310,4072,264,26816,15304,1129,15092,364,74393,3541,13,1061,369,264,6150,3274(...TRUNCATED) | [[-0.6106624603271484,-0.0017527445452287793,-0.975085437297821,-0.0011978112161159515,-0.0036684819(...TRUNCATED) |
[{"content":"","role":"user"},{"content":"<think>The user wants me to implement a lightweight, panda(...TRUNCATED) | opencode | vllm/9647069541629285 | vllm | 2026-07-12T07:16:32.001559Z | 5k-adversarial-0505 | episode-4 | 05a75ff4-f5e3-4156-bae1-e2b718da890b | 5k-adversarial-0505__RzR9mQE | AgentTimeoutError | "I'll start by exploring the test files to understand the expected behavior, then implement the modu(...TRUNCATED) | null | [[248045,8678,198,2,13455,271,2523,599,2528,310,279,2614,5568,25,271,27,15449,29,198,4754,1267,763,3(...TRUNCATED) | [[760,1156,6587,728,310,4072,264,28191,11,18056,12317,6317,1498,5165,13,6558,728,1118,12804,279,1228(...TRUNCATED) | [[-0.07503840327262878,-0.000633514893706888,-0.06217329576611519,-0.00005411946040112525,-1.1920901(...TRUNCATED) |
[{"content":"","role":"user"},{"content":"<think>The user wants me to implement a Django Slack slash(...TRUNCATED) | opencode | vllm/9647069541629285 | vllm | 2026-07-11T17:17:46.221792Z | 5k-adversarial-1642 | episode-10 | 05a75ff4-f5e3-4156-bae1-e2b718da890b | 5k-adversarial-1642__oAFwxE7 | 0.0 | "The workspace is empty. I need to create the Django project structure from scratch. Let me create t(...TRUNCATED) | "Creating virtual environment...\nInstalling pytest...\nRunning pytest...\n=========================(...TRUNCATED) | [[248045,8678,198,2,13455,271,2523,599,2528,310,279,2614,5568,25,271,27,15449,29,198,4754,1267,763,3(...TRUNCATED) | [[760,1156,6587,728,310,4072,264,50832,55327,35414,3108,72703,6795,13,6558,728,1387,1441,279,8242,25(...TRUNCATED) | [[-0.05558769777417183,-0.0009258260834030807,-0.012650024145841599,-0.0001338628790108487,-1.788134(...TRUNCATED) |
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Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers import AutoTokenizer
# Use the served model's own tokenizer (pull from the ref above; if it is a
# gs:// mirror, copy tokenizer.json/tokenizer_config.json/vocab.json/merges.txt
# locally first and point AutoTokenizer at that dir).
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-122B-A10B-FP8")
# token_ids are list-of-lists (one list per turn) — decode each turn:
text = [tok.decode(turn, skip_special_tokens=False) for turn in completion_token_ids]
Engine-reported served model name: 9647069541629285
See tokenizer_provenance.json for a machine-readable version.
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