Revisiting Text Ranking in Deep Research
Paper
• 2602.21456 • Published
• 2
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 2 was different:
embedding_offset: int64
num_embeddings: int64
num_passages: int64
vs
text: int64
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
for key, example in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 588, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 2 was different:
embedding_offset: int64
num_embeddings: int64
num_passages: int64
vs
text: int64Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
This repository provides the retrieval indexes built on the passage and document corpora of the BrowseComp-Plus dataset, as used in the paper Revisiting Text Ranking in Deep Research.
The released indexes correspond to the following retrievers:
These indexes are provided to facilitate reproducibility and enable direct evaluation of text ranking methods in the deep research setting.
If you have any questions or suggestions, please contact:
If you find this work useful, please cite:
@article{meng2026revisiting,
title={Revisiting Text Ranking in Deep Research},
author={Meng, Chuan and Ou, Litu and MacAvaney, Sean and Dalton, Jeff},
year={2026},
journal={arXiv preprint arXiv:2602.21456}
}