Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
family: string
instructions: string
level: int64
manifest: struct<observation_cutoff: int64, observations: int64, schema_version: string, sha256: string, synth (... 11 chars omitted)
child 0, observation_cutoff: int64
child 1, observations: int64
child 2, schema_version: string
child 3, sha256: string
child 4, synthetic: bool
seed: int64
split: string
task_id: string
namespace_shims: list<item: struct<path: string, sha256: string>>
child 0, item: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
source_repository: string
selected_seams: list<item: string>
child 0, item: string
files: list<item: struct<path: string, sha256: string, bytes: int64>>
child 0, item: struct<path: string, sha256: string, bytes: int64>
child 0, path: string
child 1, sha256: string
child 2, bytes: int64
source_commit: string
copy_policy: string
to
{'source_repository': Value('string'), 'source_commit': Value('string'), 'selected_seams': List(Value('string')), 'copy_policy': Value('string'), 'namespace_shims': List({'path': Value('string'), 'sha256': Value('string')}), 'files': List({'path': Value('string'), 'sha256': Value('string'), 'bytes': Value('int64')})}
because column names don't match
Traceback: 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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
family: string
instructions: string
level: int64
manifest: struct<observation_cutoff: int64, observations: int64, schema_version: string, sha256: string, synth (... 11 chars omitted)
child 0, observation_cutoff: int64
child 1, observations: int64
child 2, schema_version: string
child 3, sha256: string
child 4, synthetic: bool
seed: int64
split: string
task_id: string
namespace_shims: list<item: struct<path: string, sha256: string>>
child 0, item: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
source_repository: string
selected_seams: list<item: string>
child 0, item: string
files: list<item: struct<path: string, sha256: string, bytes: int64>>
child 0, item: struct<path: string, sha256: string, bytes: int64>
child 0, path: string
child 1, sha256: string
child 2, bytes: int64
source_commit: string
copy_policy: string
to
{'source_repository': Value('string'), 'source_commit': Value('string'), 'selected_seams': List(Value('string')), 'copy_policy': Value('string'), 'namespace_shims': List({'path': Value('string'), 'sha256': Value('string')}), 'files': List({'path': Value('string'), 'sha256': Value('string'), 'bytes': Value('int64')})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Cognitive RAN OpenEnv
This environment wraps the existing private Cognitive RAN project, not a new analytical implementation. The image contains 15 byte-identical deterministic analytics modules plus inert namespace initializers. CORE_MANIFEST.json records the original source commit, selected files, and hashes. The full repository, fleet configuration, credentials, real CSVs, evaluator internals, and Prime Agent runtime are excluded.
Twenty-four synthetic task ids: 18 train and six holdout across level shifts, transient spikes, missingness, directed topology reachability, lane capability scoping, and digest-mismatch abstention. Each dataset row contains the seed, instructions, and bounded manifest. No raw arrays or answer labels are included. Synthetic source generation and grading run inside the image; raw series are never exposed by its tools.
The image uses the existing anomaly.detector.detect, specialists.timerlm.skills, specialists.toporlm.skills.propagation_reach, and pipeline.domain_profiles.get_domain seams. Tools return bounded summaries with verified evidence handles. Every tool invocation verifies the synthetic source digest; mismatches deny analytics. Output remains recommendation-only: graph reachability is not fault propagation, correlation is not causation, and missing evidence is recorded rather than inferred.
The agent submits three report fields stated in the observation. Each strictly typed correct field earns one-third; all correct earns one. Finish and the 16-action budget yield zero. Canonical task ids generate deterministic comparable evidence; optional seeds provide alternative synthetic cases. Holdout ids are excluded from the Arena request.
Public image: ghcr.io/ricable/cognitive-ran-openenv:v1 (linux/amd64). OpenEnv revision: 86a180ede21e044f7929b9a7783ad83aa67d83a3.
The public image necessarily exposes the selected deterministic source bytes listed in CORE_MANIFEST.json. Original source rights are retained; no open-source relicensing is asserted. The source repository remains private. No Qwen3.8-27B difficulty calibration, GRPO training gain, transfer result, operational RAN qualification, or private leaderboard score is claimed. Focused synthetic checks and protocol validation establish only their named local checks.
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