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clementepasti
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bfcl-activations-think

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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      Schema at index 2 was different: 
model: string
N: list<item: int64>
n_problems: int64
logprob_missing_rows: int64
groups: struct<nonlive: struct<n_problems: int64, categories: struct<simple_python: int64, simple_java: int64, simple_javascript: int64, multiple: int64, parallel: int64, parallel_multiple: int64>, lin: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, mlp: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, logprob: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, random: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, oracle: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>>, live: struct<n_problems: int64, categories: struct<live_simple: int64, live_multiple: int64, live_parallel: int64, live_parallel_multiple: int64>, lin: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, mlp: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, logprob: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, random: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, oracle: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>>, halluc: struct<n_problems: int64, categories: struct<irrelevance: int64, live_irrelevance: int64, live_relevance: int64>, lin: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, mlp: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, logprob: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, random: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, oracle: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>>>
vs
model: string
k: int64
seed: int64
winner: struct<layer: int64, pool: string, C: double>
used_layer: int64
full: bool
n_rows: int64
n_problems: int64
caveat: string
folds: struct<0: struct<n_test_problems: int64, n_train_rows: int64, lin_within: double>, 1: struct<n_test_problems: int64, n_train_rows: int64, lin_within: double>, 2: struct<n_test_problems: int64, n_train_rows: int64, lin_within: double>>
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 781, in finalize
                  self.write_rows_on_file()
                  ~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 662, in write_rows_on_file
                  table = pa.concat_tables(self.current_rows)
                File "pyarrow/table.pxi", line 6320, in pyarrow.lib.concat_tables
                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: Schema at index 2 was different: 
              model: string
              N: list<item: int64>
              n_problems: int64
              logprob_missing_rows: int64
              groups: struct<nonlive: struct<n_problems: int64, categories: struct<simple_python: int64, simple_java: int64, simple_javascript: int64, multiple: int64, parallel: int64, parallel_multiple: int64>, lin: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, mlp: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, logprob: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, random: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, oracle: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>>, live: struct<n_problems: int64, categories: struct<live_simple: int64, live_multiple: int64, live_parallel: int64, live_parallel_multiple: int64>, lin: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, mlp: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, logprob: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, random: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, oracle: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>>, halluc: struct<n_problems: int64, categories: struct<irrelevance: int64, live_irrelevance: int64, live_relevance: int64>, lin: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, mlp: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, logprob: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, random: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>, oracle: struct<mean: list<item: double>, lo: list<item: double>, hi: list<item: double>>>>
              vs
              model: string
              k: int64
              seed: int64
              winner: struct<layer: int64, pool: string, C: double>
              used_layer: int64
              full: bool
              n_rows: int64
              n_problems: int64
              caveat: string
              folds: struct<0: struct<n_test_problems: int64, n_train_rows: int64, lin_within: double>, 1: struct<n_test_problems: int64, n_train_rows: int64, lin_within: double>, 2: struct<n_test_problems: int64, n_train_rows: int64, lin_within: double>>
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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Check out the documentation for more information.

BFCL CoT-correctness probe activations (answer-WITHHELD: prompt+think_seg prefill, last-token + mean pooling, layer list in layers_*.json). Full 3,641-problem x 100-rollout t=1 pools: acts/ = 1,800-problem split, acts_extra/ = 1,841 complement (qwen3-8b; 14B only in acts/). kfold/ = deployed K=3 fold-blind linear classifiers + routing (BFCL_LIVE_BON_SPEC.md). Rebuild: experiments/bfcl_cot_clf/ in github.com/genlm/rollouts (branch clement/wip).
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