Datasets:

win7win
/

rap-benchmark-data

ArXiv:

License:

Dataset card Files Files and versions

xet

Community

Dataset Viewer

Duplicate

Split (1)

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
protocol: string
forecast_results_used: bool
membership_cutoff: timestamp[s]
assignments: string
assignments_sha256: string
parallel_seed_source: string
parallel_seed_source_sha256: string
strong_block_rule: struct<overlap_coefficient_minimum: double, minimum_intersection: int64, connected_components: bool>
  child 0, overlap_coefficient_minimum: double
  child 1, minimum_intersection: int64
  child 2, connected_components: bool
threshold_scan: struct<0.40: struct<edges: int64, blocks: int64, non_singleton_blocks: int64, fields_in_non_singleto (... 721 chars omitted)
  child 0, 0.40: struct<edges: int64, blocks: int64, non_singleton_blocks: int64, fields_in_non_singletons: int64, la (... 55 chars omitted)
      child 0, edges: int64
      child 1, blocks: int64
      child 2, non_singleton_blocks: int64
      child 3, fields_in_non_singletons: int64
      child 4, largest_block: int64
      child 5, top_block_sizes: list<item: int64>
          child 0, item: int64
  child 1, 0.50: struct<edges: int64, blocks: int64, non_singleton_blocks: int64, fields_in_non_singletons: int64, la (... 55 chars omitted)
      child 0, edges: int64
      child 1, blocks: int64
      child 2, non_singleton_blocks: int64
      child 3, fields_in_non_singletons: int64
      child 4, largest_block: int64
      child 5, top_block_sizes: list<item: int64>
          child 0, item: int64
  child 2, 0.60: struct<edges: int64, blocks: int64, non_singleton_blocks: int64, fields_in_non_singletons: int64, l
...
   child 0, item: int64
  child 3, fields: list<item: string>
      child 0, item: string
  child 4, field_count: int64
  child 5, episodes: int64
  child 6, purpose: string
test: struct<fields: list<item: string>, field_count: int64, episodes: int64, purpose: string>
  child 0, fields: list<item: string>
      child 0, item: string
  child 1, field_count: int64
  child 2, episodes: int64
  child 3, purpose: string
leakage_audit: struct<strong_cross_edges: int64, all_nonzero_cross_edges: int64, total_pair_intersection_mass: int6 (... 243 chars omitted)
  child 0, strong_cross_edges: int64
  child 1, all_nonzero_cross_edges: int64
  child 2, total_pair_intersection_mass: int64
  child 3, cross_pair_intersection_mass: int64
  child 4, cross_intersection_mass_fraction: double
  child 5, top_cross_edges: list<item: struct<field_a: string, field_b: string, intersection: int64, overlap_coefficient: double (... 19 chars omitted)
      child 0, item: struct<field_a: string, field_b: string, intersection: int64, overlap_coefficient: double, jaccard:  (... 7 chars omitted)
          child 0, field_a: string
          child 1, field_b: string
          child 2, intersection: int64
          child 3, overlap_coefficient: double
          child 4, jaccard: double
  child 6, interpretation: string
inference: struct<primary_ci: string, robustness_ci: string>
  child 0, primary_ci: string
  child 1, robustness_ci: string
scope: string
source: string
scope_sha256: string
source_sha256: string
to
{'protocol': Value('string'), 'source': Value('string'), 'source_sha256': Value('string'), 'scope': Value('string'), 'scope_sha256': Value('string'), 'dev': {'path': Value('string'), 'sha256': Value('string'), 'fields': Value('int64'), 'episodes': Value('int64')}, 'test': {'path': Value('string'), 'sha256': Value('string'), 'fields': Value('int64'), 'episodes': 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 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
              protocol: string
              forecast_results_used: bool
              membership_cutoff: timestamp[s]
              assignments: string
              assignments_sha256: string
              parallel_seed_source: string
              parallel_seed_source_sha256: string
              strong_block_rule: struct<overlap_coefficient_minimum: double, minimum_intersection: int64, connected_components: bool>
                child 0, overlap_coefficient_minimum: double
                child 1, minimum_intersection: int64
                child 2, connected_components: bool
              threshold_scan: struct<0.40: struct<edges: int64, blocks: int64, non_singleton_blocks: int64, fields_in_non_singleto (... 721 chars omitted)
                child 0, 0.40: struct<edges: int64, blocks: int64, non_singleton_blocks: int64, fields_in_non_singletons: int64, la (... 55 chars omitted)
                    child 0, edges: int64
                    child 1, blocks: int64
                    child 2, non_singleton_blocks: int64
                    child 3, fields_in_non_singletons: int64
                    child 4, largest_block: int64
                    child 5, top_block_sizes: list<item: int64>
                        child 0, item: int64
                child 1, 0.50: struct<edges: int64, blocks: int64, non_singleton_blocks: int64, fields_in_non_singletons: int64, la (... 55 chars omitted)
                    child 0, edges: int64
                    child 1, blocks: int64
                    child 2, non_singleton_blocks: int64
                    child 3, fields_in_non_singletons: int64
                    child 4, largest_block: int64
                    child 5, top_block_sizes: list<item: int64>
                        child 0, item: int64
                child 2, 0.60: struct<edges: int64, blocks: int64, non_singleton_blocks: int64, fields_in_non_singletons: int64, l
              ...
                 child 0, item: int64
                child 3, fields: list<item: string>
                    child 0, item: string
                child 4, field_count: int64
                child 5, episodes: int64
                child 6, purpose: string
              test: struct<fields: list<item: string>, field_count: int64, episodes: int64, purpose: string>
                child 0, fields: list<item: string>
                    child 0, item: string
                child 1, field_count: int64
                child 2, episodes: int64
                child 3, purpose: string
              leakage_audit: struct<strong_cross_edges: int64, all_nonzero_cross_edges: int64, total_pair_intersection_mass: int6 (... 243 chars omitted)
                child 0, strong_cross_edges: int64
                child 1, all_nonzero_cross_edges: int64
                child 2, total_pair_intersection_mass: int64
                child 3, cross_pair_intersection_mass: int64
                child 4, cross_intersection_mass_fraction: double
                child 5, top_cross_edges: list<item: struct<field_a: string, field_b: string, intersection: int64, overlap_coefficient: double (... 19 chars omitted)
                    child 0, item: struct<field_a: string, field_b: string, intersection: int64, overlap_coefficient: double, jaccard:  (... 7 chars omitted)
                        child 0, field_a: string
                        child 1, field_b: string
                        child 2, intersection: int64
                        child 3, overlap_coefficient: double
                        child 4, jaccard: double
                child 6, interpretation: string
              inference: struct<primary_ci: string, robustness_ci: string>
                child 0, primary_ci: string
                child 1, robustness_ci: string
              scope: string
              source: string
              scope_sha256: string
              source_sha256: string
              to
              {'protocol': Value('string'), 'source': Value('string'), 'source_sha256': Value('string'), 'scope': Value('string'), 'scope_sha256': Value('string'), 'dev': {'path': Value('string'), 'sha256': Value('string'), 'fields': Value('int64'), 'episodes': Value('int64')}, 'test': {'path': Value('string'), 'sha256': Value('string'), 'fields': Value('int64'), 'episodes': Value('int64')}}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

RAP Benchmark Dataset

This is the companion data release for Research Attention Prediction (RAP). RAP evaluates whether an agent can estimate the distribution of newly posted papers across eight fixed research directions, then forecast the distribution in the following six months.

The package contains the frozen benchmark interface and the metadata-only Search snapshot.

Snapshot

Item	Count
Fields	278
Dev episodes	335
Test episodes	1,055
Fixed Search shards	1,390
Expanding Search shards	1,390
Directions per field	8

Layout

Path	Use
`inputs/`	Safe model-facing Dev/Test episode inputs
`targets/`	Evaluator-only recent/future counts, shares, and diagnostics
`evaluator/`	Combined episodes for scoring and no-API baselines; do not send to agents
`data/`	Public runner scope and Search-manifest indexes
`search_shards/`	Fixed and Expanding metadata-only Search corpora
`DATA_CARD.md`	Task semantics, leakage boundary, and aggregation rules
`ARXIV_METADATA_NOTICE.md`	Provenance and redistribution boundary for arXiv metadata

Each row in inputs/ contains the field, cutoff date, history-window metadata, future-window metadata, and the eight direction definitions. It intentionally does not contain history counts, recent shares, future counts, or future shares. Those evaluator-only values live in targets/ and evaluator/.

Search snapshot

search_shards/fixed/ enforces [T-6m, T).
search_shards/expanding/ enforces <T.
Each shard contains exactly id, date, title, abstract, and categories.
Each manifest records the shard SHA-256, row count, and date-bound checks.

The shards contain descriptive arXiv metadata. See ARXIV_METADATA_NOTICE.md for provenance and source terms.

Integrity

Verify the complete snapshot from its root:

sha256sum -c SHA256SUMS

The evaluator contract and scoring commands are documented in the companion RAP evaluation-code repository.

License

This dataset repository is marked other because it combines two provenance classes. Original RAP-derived artifacts are released under CC BY 4.0. The metadata-only Search shards retain their arXiv provenance and source terms. Read LICENSE and ARXIV_METADATA_NOTICE.md before redistributing or adapting the snapshot.

Citation

Please cite the RAP paper:

@misc{wu2026rap,
  title  = {RAP: Research Attention Prediction Reveals Target-Conditioned Evidence Acquisition Biases},
  author = {Wu, Yingqian and Liang, Jingcong and Wang, Siyuan and Yin, Zhenfei
            and Torr, Philip and Yu, Junchi and Wei, Zhongyu},
  year   = {2026},
  url    = {https://arxiv.org/abs/2609.10092}
}

Downloads last month: 391

Total file size:

2.72 GB

Paper for win7win/rap-benchmark-data

RAP: Research Attention Prediction Reveals Target-Conditioned Evidence Acquisition Biases

Paper • 2609.10092 • Published 17 days ago