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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
family: string
gate: string
items: list<item: struct<name: string, prompt: string, intermediates: list<item: string>, source: string, c (... 122 chars omitted)
  child 0, item: struct<name: string, prompt: string, intermediates: list<item: string>, source: string, consistency: (... 110 chars omitted)
      child 0, name: string
      child 1, prompt: string
      child 2, intermediates: list<item: string>
          child 0, item: string
      child 3, source: string
      child 4, consistency: double
      child 5, gate_variant: string
      child 6, eval_render: string
      child 7, readout: struct<kind: string, offsets: list<item: int64>>
          child 0, kind: string
          child 1, offsets: list<item: int64>
              child 0, item: int64
eval_render_note: string
to
{'family': Value('string'), 'gate': Value('string'), 'items': List({'name': Value('string'), 'prompt': Value('string'), 'intermediates': List(Value('string')), 'consistency': Value('float64'), 'consistency_strict': Value('float64'), 'source': Value('string'), 'gate_variant': Value('string'), 'eval_render': Value('string'), 'readout': {'kind': Value('string'), 'offsets': List(Value('int64'))}, 'probe_token_lens': {'intermediates': List(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
              family: string
              gate: string
              items: list<item: struct<name: string, prompt: string, intermediates: list<item: string>, source: string, c (... 122 chars omitted)
                child 0, item: struct<name: string, prompt: string, intermediates: list<item: string>, source: string, consistency: (... 110 chars omitted)
                    child 0, name: string
                    child 1, prompt: string
                    child 2, intermediates: list<item: string>
                        child 0, item: string
                    child 3, source: string
                    child 4, consistency: double
                    child 5, gate_variant: string
                    child 6, eval_render: string
                    child 7, readout: struct<kind: string, offsets: list<item: int64>>
                        child 0, kind: string
                        child 1, offsets: list<item: int64>
                            child 0, item: int64
              eval_render_note: string
              to
              {'family': Value('string'), 'gate': Value('string'), 'items': List({'name': Value('string'), 'prompt': Value('string'), 'intermediates': List(Value('string')), 'consistency': Value('float64'), 'consistency_strict': Value('float64'), 'source': Value('string'), 'gate_variant': Value('string'), 'eval_render': Value('string'), 'readout': {'kind': Value('string'), 'offsets': List(Value('int64'))}, 'probe_token_lens': {'intermediates': List(Value('int64'))}})}
              because column names don't match

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Oracle-Lens Behavioral Eval Banks — final

Frozen, pre-registered eval item banks for the oracle-lens project (MATS, Neel Nanda stream). 17 banks / 1,645 items: 10 base families (multihop, multilingual, order-ops, poetry, typo, association, directed-modulation, basic-readout, user-modeling, sandbagging) plus -mt multi-token variants for 7 of them.

Each file is lens-eval-<family>.json with an items list. Items carry an eval_render mode — most families are rendered as plain text with NO chat template; directed-modulation (chat_dm), basic-readout implicit prediction (chat), user-modeling (chat_context), and sandbagging (chat_sandbag) are the chat exceptions. Always render via global_workspace.oracle_lens_evals.render_for_eval from the source repo so this cannot be gotten wrong.

Source of truth: evals/oracle_lens/final/ in the global-workspace repo (which also holds the staging intermediates, generation sources, and the gen → check → finalize pipeline scripts under scripts/oracle_lens_evals/). This upload mirrors that directory verbatim.

⚠️ These are eval questions. Please do not include them in model training corpora.

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