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train
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:    CastError
Message:      Couldn't cast
applicable_n: int64
bias_count: int64
bootstrap_ci_high: double
bootstrap_ci_low: double
bootstrap_confidence_level: double
bootstrap_resamples: int64
condition: string
diagnostic_biases_with_nonzero_denominator: int64
inconsistent_applied_without_applicable_n: int64
macro_diagnostic_applied_given_applicable_rate: double
macro_primary_applied_rate: double
pooled_diagnostic_applied_given_applicable_rate: double
pooled_primary_applied_rate: double
split: string
targeted_n: int64
allow_subset: bool
row_count: int64
outputs: struct<figure_pdf: string, figure_png: string, per_bias_csv: string, summary_csv: string, summary_js (... 11 chars omitted)
  child 0, figure_pdf: string
  child 1, figure_png: string
  child 2, per_bias_csv: string
  child 3, summary_csv: string
  child 4, summary_json: string
aggregation: struct<also_report_pooled: bool, bar_aggregation: string, bootstrap: struct<confidence_level: double (... 157 chars omitted)
  child 0, also_report_pooled: bool
  child 1, bar_aggregation: string
  child 2, bootstrap: struct<confidence_level: double, paired_across_conditions: bool, resamples: int64, seed: int64, stra (... 30 chars omitted)
      child 0, confidence_level: double
      child 1, paired_across_conditions: bool
      child 2, resamples: int64
      child 3, seed: int64
      child 4, stratify_by: string
      child 5, unit: string
  child 3, diagnostic_estimator: string
  child 4, primary_estimator: string
judged_generations_sha256: string
condition_order: list<item: string>
  child 0, item: string
protocol_path: string
judged_generations_path: string
protocol_sha256: string
created_utc: timestamp[s]
to
{'aggregation': {'also_report_pooled': Value('bool'), 'bar_aggregation': Value('string'), 'bootstrap': {'confidence_level': Value('float64'), 'paired_across_conditions': Value('bool'), 'resamples': Value('int64'), 'seed': Value('int64'), 'stratify_by': Value('string'), 'unit': Value('string')}, 'diagnostic_estimator': Value('string'), 'primary_estimator': Value('string')}, 'allow_subset': Value('bool'), 'condition_order': List(Value('string')), 'created_utc': Value('timestamp[s]'), 'judged_generations_path': Value('string'), 'judged_generations_sha256': Value('string'), 'outputs': {'figure_pdf': Value('string'), 'figure_png': Value('string'), 'per_bias_csv': Value('string'), 'summary_csv': Value('string'), 'summary_json': Value('string')}, 'protocol_path': Value('string'), 'protocol_sha256': Value('string'), 'row_count': Value('int64')}
because column names don't match
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 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              applicable_n: int64
              bias_count: int64
              bootstrap_ci_high: double
              bootstrap_ci_low: double
              bootstrap_confidence_level: double
              bootstrap_resamples: int64
              condition: string
              diagnostic_biases_with_nonzero_denominator: int64
              inconsistent_applied_without_applicable_n: int64
              macro_diagnostic_applied_given_applicable_rate: double
              macro_primary_applied_rate: double
              pooled_diagnostic_applied_given_applicable_rate: double
              pooled_primary_applied_rate: double
              split: string
              targeted_n: int64
              allow_subset: bool
              row_count: int64
              outputs: struct<figure_pdf: string, figure_png: string, per_bias_csv: string, summary_csv: string, summary_js (... 11 chars omitted)
                child 0, figure_pdf: string
                child 1, figure_png: string
                child 2, per_bias_csv: string
                child 3, summary_csv: string
                child 4, summary_json: string
              aggregation: struct<also_report_pooled: bool, bar_aggregation: string, bootstrap: struct<confidence_level: double (... 157 chars omitted)
                child 0, also_report_pooled: bool
                child 1, bar_aggregation: string
                child 2, bootstrap: struct<confidence_level: double, paired_across_conditions: bool, resamples: int64, seed: int64, stra (... 30 chars omitted)
                    child 0, confidence_level: double
                    child 1, paired_across_conditions: bool
                    child 2, resamples: int64
                    child 3, seed: int64
                    child 4, stratify_by: string
                    child 5, unit: string
                child 3, diagnostic_estimator: string
                child 4, primary_estimator: string
              judged_generations_sha256: string
              condition_order: list<item: string>
                child 0, item: string
              protocol_path: string
              judged_generations_path: string
              protocol_sha256: string
              created_utc: timestamp[s]
              to
              {'aggregation': {'also_report_pooled': Value('bool'), 'bar_aggregation': Value('string'), 'bootstrap': {'confidence_level': Value('float64'), 'paired_across_conditions': Value('bool'), 'resamples': Value('int64'), 'seed': Value('int64'), 'stratify_by': Value('string'), 'unit': Value('string')}, 'diagnostic_estimator': Value('string'), 'primary_estimator': Value('string')}, 'allow_subset': Value('bool'), 'condition_order': List(Value('string')), 'created_utc': Value('timestamp[s]'), 'judged_generations_path': Value('string'), 'judged_generations_sha256': Value('string'), 'outputs': {'figure_pdf': Value('string'), 'figure_png': Value('string'), 'per_bias_csv': Value('string'), 'summary_csv': Value('string'), 'summary_json': Value('string')}, 'protocol_path': Value('string'), 'protocol_sha256': Value('string'), 'row_count': Value('int64')}
              because column names don't match
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Auditing Games evaluation artifacts

This dataset stores immutable evaluation artifacts for the auditing-games model organisms.
Behavioral evaluations

Llama 3.3 reward-model-bias exploitation v1 (legacy four-condition grid)
The link above is pinned through the immutable legacy-llama33-bias-exploitation-v1 tag. Each bundle contains its own provenance and deterministic artifact inventory.
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