The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or valueNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Journeyman — judge-calibration set (82 cases, 7 axes)
This is the exam a judge sits before it is allowed to score anything in Journeyman, a process-quality benchmark for LLM agents. Each case is a real agent record plus the label a judge should return for one axis. A judge that misses any axis does not get the badge; the ones that failed are published too, in the judge registry.
What is in it
| axis | cases |
|---|---|
empty-measure |
17 |
grounding |
14 |
handoff-verification |
11 |
object-hold |
8 |
relief-page |
9 |
route-discipline |
9 |
wall-pricing |
14 |
Labels come from a three-family council (claude-sonnet-5, kimi-k2, grok-4.3): a blind round, then an anonymous round where each labeller had to quote its evidence. A label is sealed only with support from at least two families; the maintainer ruled the cases the council split on; empty-measure is counted mechanically under its published definition.
Honest limits
- 82 cases. Small next to human-annotated process benchmarks. It is sized for what it claims — licensing a judge — not for ranking a field.
- Labels are model-produced, not human. The council is cross-family and the protocol was frozen before labelling, but this is not a human gold standard.
- The set has been corrected, and every correction is in the file's own
notefield: four cases whose only closing report the scene refused were relabelled, twelve fresh cases were harvested when unfiled reports came to dominate two axes, and one case was relabelled after every examined judge read it against the key.
Provenance
Set version 2.3. Shipped inside the package as
journeyman/calibration/v2_real.json — pip install journeyman-bench.
Archived releases carry a DOI: 10.5281/zenodo.22085820.
journeyman qualify --judge <endpoint> --judge-model <model> --repeats 3
Looking for an arXiv endorser (cs.AI / cs.LG)
The write-up describing this set is in progress. arXiv requires an endorsement for a first submission in a category and we have no institutional affiliation to bypass it. If you publish in this area and think the work is worth endorsing, please open an issue on the repository — and if you read it and think it is not, that is useful to hear too.
Generated from the set itself by tools/hf_sync.py.
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