Datasets:
The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
EvolveBench Agent Traces
Complete execution and evaluation traces for two web-research agent runs over the same
78-task benchmark suite (tasks_version_v8_20260910). Both runs use an identical
harness, identical two-turn prompts and an identical evaluator; they differ only in the
agent's underlying model. They are the runs behind the capability-separation result.
| run | agent | tasks scored | mean reward |
|---|---|---|---|
runC_codex_sol_v8_capturefix |
codex / gpt-5.6-sol | 78 | 0.795 |
runD_codex_gpt55_v8_capturefix |
codex / gpt-5.5 | 78 | 0.702 |
Paired difference +0.0926 (SE 0.0235) across all 78 tasks, exceeding two standard errors. Measured against an evaluator run-mean standard error of 0.0024 — obtained from 78 tasks x 5 replicates with captures frozen, so that only the judge varies — the gap is roughly 39x the evaluator's noise floor. The separation is therefore not an artifact of evaluator nondeterminism.
Limitation, stated plainly. With a single run per configuration this gap mixes model capability with agent run-to-run variation and cannot separate the two. On an earlier pair of runs the same gpt-5.5 configuration scored 0.731 rather than 0.702, a swing more than ten times the evaluator standard error. Agent variance, not evaluator variance, is the binding uncertainty here, and it is unmeasured.
Contents
agent_traces_v8_20260912.tar.gz (83.5 MB compressed, ~2.6 GB expanded), per run:
RUN_CONFIG.json agent, model, judge model, suite, prompt mode
RUN_SUMMARY.json scored count, mean reward, silent-failure gate verdict
PROGRESS.json per-task status, wall time, turn count
logs/<task>.log driver stdout and stderr per task
runs/<task>/
agent.jsonl full turn-1 transcript: tool calls, reasoning events
turn2.jsonl turn-2 transcript, where the structured outcome is emitted
agent_result.json parsed conversation handed to the evaluator
agent.stderr.txt agent stderr
output/ artifacts the agent wrote, including summary.json
evaluation.json full evaluator output
reward.json final reward
Counts verified: 156 (= 78 x 2) of each per-task artifact, 529 agent-written output files, both run summaries.
Why evaluation.json is the interesting file
It embeds the complete grounding trace — the captured text of every URL the agent cited — together with the verbatim rubric-judge prompt. Any individual verdict can therefore be audited end to end, offline, without re-fetching a single page. This also makes the traces usable for replay experiments: an evaluator change can be measured against frozen evidence rather than against a moving web.
Provenance and safety
Evaluator gpt-5.4-mini through an OpenAI-compatible proxy. Harness commit 85d46a8.
Scanned for credentials before release; none are present. The 32 occurrences of
Authorization: Bearer $GROQ_API_KEY are the literal shell variable name, captured
from Groq's public API documentation by an agent reading that page, not a secret value.
Captured page text is public web content retrieved without authentication.
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