Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<type: string, id: string, classification: string, kind: string, actor: string, state_version: string, parents: list<item: null>, action: string, namespace: string, item: string, version: string, state: string, event: string>
to
{'type': Value('string'), 'id': Value('string'), 'classification': Value('string'), 'kind': Value('string'), 'actor': Value('string'), 'state_version': Value('string'), 'parents': List(Value('null'))}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<type: string, id: string, classification: string, kind: string, actor: string, state_version: string, parents: list<item: null>, action: string, namespace: string, item: string, version: string, state: string, event: string>
to
{'type': Value('string'), 'id': Value('string'), 'classification': Value('string'), 'kind': Value('string'), 'actor': Value('string'), 'state_version': Value('string'), 'parents': List(Value('null'))}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.
Trajectory Ledger Fixtures
44 synthetic, inert agent-trace fixtures plus an evaluation manifest — the accepted corpus and negative cases behind Trajectory Ledger, a local evidence-linked diagnostic for AI-agent traces that abstains by default.
What's here
- 41 accepted fixtures across 8 families: clean controls, missing lineage, conflicting lineage, stale-memory boundaries, tainted-reuse ordering, compound multiple-hypotheses, abstention and truncation, classification propagation.
- 17 negative cases (in
manifest.json): malformed shape, header, enum, identifier, size, depth, symlink, path-escape and sandbox boundaries — each with its expected fail-closed error. manifest.jsonbinds every fixture to its expected result, expected invariants, patch disposition, and replay disposition.
Provenance and boundary
Every fixture is synthetic and inert: authored for mechanism testing, containing no real user data, no real agent transcripts, and no executable content. The corpus is mechanism evidence, not efficacy evidence — it demonstrates what the diagnostic detects and refuses, and makes no localization-accuracy claim.
Use
import json, pathlib
manifest = json.loads(pathlib.Path("manifest.json").read_text())
for entry in manifest["accepted"]:
fixture = json.loads(pathlib.Path(entry["fixture"]).read_text())
# run your own trace tooling against a known-answer corpus
Read the launch post: https://abhid.substack.com/p/a-failing-agent-trace-tells-you-what
MIT © Abhi Das
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