Datasets:

forseasons
/

dataagentbench-derived-official54-altimate-prompt-shell

Tasks:

Text Generation

Question Answering

Size:

n<1K

ArXiv:

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
answers_materialized: bool
next_required_step: string
requires_live_teacher_rollout: bool
rl_rows_are_prompt_shells_not_training_rewards: bool
selected_domains: struct<DEPS_DEV_V1: int64, GITHUB_REPOS: int64, PANCANCER_ATLAS: int64, PATENTS: int64, agnews: int6 (... 224 chars omitted)
  child 0, DEPS_DEV_V1: int64
  child 1, GITHUB_REPOS: int64
  child 2, PANCANCER_ATLAS: int64
  child 3, PATENTS: int64
  child 4, agnews: int64
  child 5, bookreview: int64
  child 6, civic_unstructured: int64
  child 7, crmarenapro: int64
  child 8, cve: int64
  child 9, googlelocal: int64
  child 10, imdb: int64
  child 11, krama: int64
  child 12, music_brainz_20k: int64
  child 13, stockindex: int64
  child 14, stockmarket: int64
  child 15, usaspending: int64
  child 16, yelp: int64
selected_tasks: int64
status: string
trajectories_materialized: bool
warnings: list<item: null>
  child 0, item: null
dataset_dirs_scanned: int64
include_synthetic: bool
skipped_preview: list<item: struct<dataset_dir: string, reason: string>>
  child 0, item: struct<dataset_dir: string, reason: string>
      child 0, dataset_dir: string
      child 1, reason: string
tasks_discovered: int64
skipped_count: int64
source_kinds: struct<official_like: int64>
  child 0, official_like: int64
bench_root: string
domains: struct<DEPS_DEV_V1: int64, GITHUB_REPOS: int64, PANCANCER_ATLAS: int64, PATENTS: int64, agnews: int6 (... 224 chars omitted)
  child 0, DEPS_DEV_V1: int64
  child 1, GITHUB_REPOS: int64
  child 2, PANCANCER_ATLAS: int64
  child 3, PATENTS: int64
  child 4, agnews: int64
  child 5, bookreview: int64
  child 6, civic_unstructured: int64
  child 7, crmarenapro: int64
  child 8, cve: int64
  child 9, googlelocal: int64
  child 10, imdb: int64
  child 11, krama: int64
  child 12, music_brainz_20k: int64
  child 13, stockindex: int64
  child 14, stockmarket: int64
  child 15, usaspending: int64
  child 16, yelp: int64
to
{'bench_root': Value('string'), 'dataset_dirs_scanned': Value('int64'), 'domains': {'DEPS_DEV_V1': Value('int64'), 'GITHUB_REPOS': Value('int64'), 'PANCANCER_ATLAS': Value('int64'), 'PATENTS': Value('int64'), 'agnews': Value('int64'), 'bookreview': Value('int64'), 'civic_unstructured': Value('int64'), 'crmarenapro': Value('int64'), 'cve': Value('int64'), 'googlelocal': Value('int64'), 'imdb': Value('int64'), 'krama': Value('int64'), 'music_brainz_20k': Value('int64'), 'stockindex': Value('int64'), 'stockmarket': Value('int64'), 'usaspending': Value('int64'), 'yelp': Value('int64')}, 'include_synthetic': Value('bool'), 'skipped_count': Value('int64'), 'skipped_preview': List({'dataset_dir': Value('string'), 'reason': Value('string')}), 'source_kinds': {'official_like': Value('int64')}, 'tasks_discovered': 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 478, 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
              answers_materialized: bool
              next_required_step: string
              requires_live_teacher_rollout: bool
              rl_rows_are_prompt_shells_not_training_rewards: bool
              selected_domains: struct<DEPS_DEV_V1: int64, GITHUB_REPOS: int64, PANCANCER_ATLAS: int64, PATENTS: int64, agnews: int6 (... 224 chars omitted)
                child 0, DEPS_DEV_V1: int64
                child 1, GITHUB_REPOS: int64
                child 2, PANCANCER_ATLAS: int64
                child 3, PATENTS: int64
                child 4, agnews: int64
                child 5, bookreview: int64
                child 6, civic_unstructured: int64
                child 7, crmarenapro: int64
                child 8, cve: int64
                child 9, googlelocal: int64
                child 10, imdb: int64
                child 11, krama: int64
                child 12, music_brainz_20k: int64
                child 13, stockindex: int64
                child 14, stockmarket: int64
                child 15, usaspending: int64
                child 16, yelp: int64
              selected_tasks: int64
              status: string
              trajectories_materialized: bool
              warnings: list<item: null>
                child 0, item: null
              dataset_dirs_scanned: int64
              include_synthetic: bool
              skipped_preview: list<item: struct<dataset_dir: string, reason: string>>
                child 0, item: struct<dataset_dir: string, reason: string>
                    child 0, dataset_dir: string
                    child 1, reason: string
              tasks_discovered: int64
              skipped_count: int64
              source_kinds: struct<official_like: int64>
                child 0, official_like: int64
              bench_root: string
              domains: struct<DEPS_DEV_V1: int64, GITHUB_REPOS: int64, PANCANCER_ATLAS: int64, PATENTS: int64, agnews: int6 (... 224 chars omitted)
                child 0, DEPS_DEV_V1: int64
                child 1, GITHUB_REPOS: int64
                child 2, PANCANCER_ATLAS: int64
                child 3, PATENTS: int64
                child 4, agnews: int64
                child 5, bookreview: int64
                child 6, civic_unstructured: int64
                child 7, crmarenapro: int64
                child 8, cve: int64
                child 9, googlelocal: int64
                child 10, imdb: int64
                child 11, krama: int64
                child 12, music_brainz_20k: int64
                child 13, stockindex: int64
                child 14, stockmarket: int64
                child 15, usaspending: int64
                child 16, yelp: int64
              to
              {'bench_root': Value('string'), 'dataset_dirs_scanned': Value('int64'), 'domains': {'DEPS_DEV_V1': Value('int64'), 'GITHUB_REPOS': Value('int64'), 'PANCANCER_ATLAS': Value('int64'), 'PATENTS': Value('int64'), 'agnews': Value('int64'), 'bookreview': Value('int64'), 'civic_unstructured': Value('int64'), 'crmarenapro': Value('int64'), 'cve': Value('int64'), 'googlelocal': Value('int64'), 'imdb': Value('int64'), 'krama': Value('int64'), 'music_brainz_20k': Value('int64'), 'stockindex': Value('int64'), 'stockmarket': Value('int64'), 'usaspending': Value('int64'), 'yelp': Value('int64')}, 'include_synthetic': Value('bool'), 'skipped_count': Value('int64'), 'skipped_preview': List({'dataset_dir': Value('string'), 'reason': Value('string')}), 'source_kinds': {'official_like': Value('int64')}, 'tasks_discovered': 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.

DataAgentBench-Derived Official54 Altimate Prompt Shell

This public dataset contains 54 DataAgentBench-derived task prompt-shell rows compiled for the Altimate-centered DAB sandbox runtime. It is a derived compile/export artifact, not the official raw DataAgentBench release.

It is intended as a portable task/prompt/manifest source for downstream Altimate teacher rollouts, SFT construction, or RL data compilation. It is not a completed rollout dataset: the rows do not contain agent trajectories, materialized answers, or realized rewards yet.

Files

rl/altimate_noctx/prompt_shell.parquet: canonical VERL-style prompt shell rows.
rl/altimate_noctx/prompt_shell.jsonl: JSONL copy of the same rows.
metadata/summary.json: discovery and selection summary.
metadata/readiness_report.json: readiness contract and caveats.
metadata/selection_report.json: selected task/domain/operation distribution.
metadata/discovery_report.json: discovered task-signature information.
metadata/selected_task_list.json: selected task identities and context.
metadata/sandbox_task_manifest.json: task manifest shared by RL/SFT rollouts.

Schema

Each row has the following columns:

level
type
data_source
prompt
ability
reward_model
extra_info

The reward_model includes the strict validator path. The extra_info field stores task identity, database context, valid DB names, operation tags, and the Altimate runtime contract.

Runtime Contract

All rows use:

data_source: dab_sandbox_altimate_noctx
dab_agent_loop_variant: altimate_noctx
auto_context_mode: none
max_iterations: 200
max_function_calls_per_turn: 10
max_single_observation_chars: 24000
max_turn_observation_chars: 48000
large_preview_chars: 16000
large_preview_rows: 12

Paths were sanitized from the local machine into portable DataAgentBench/... style paths. Consumers should point DATAAGENTBENCH_ROOT or equivalent runtime configuration at their local checkout of DataAgentBench task artifacts.

Quality Snapshot

Rows: 54
Task/source kind: official_like / DataAgentBench-derived
Readiness: ready_for_live_altimate_teacher_rollout
Answers materialized: False
Trajectories materialized: False
RL rows are prompt shells, not realized reward rollouts: True

Domain distribution:

stockmarket: 5
civic_unstructured: 4
cve: 4
imdb: 4
krama: 4
usaspending: 4
crmarenapro: 3
stockindex: 3
music_brainz_20k: 3
bookreview: 3
PATENTS: 3
googlelocal: 2
yelp: 2
agnews: 2
GITHUB_REPOS: 2
DEPS_DEV_V1: 2
PANCANCER_ATLAS: 2
eval46_crmarenapro: 2

Operation tags are available in extra_info.operation_tags and summarized in metadata/selection_report.json / metadata/summary.json.

Relation to DataAgentBench

This dataset is built from selected DataAgentBench task artifacts such as user queries, database configs, db_description_withhint.txt, and validate.py paths, then converted into the local Altimate/VERL prompt-shell schema. It does not redistribute the full benchmark database environment as a standalone replacement for the upstream DataAgentBench repository. Please cite the DAB paper when using this artifact.

@article{ma2026can,
  title={Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents},
  author={Ma, Ruiying and Shankar, Shreya and Chen, Ruiqi and Lin, Yiming and Zeighami, Sepanta and Ghosh, Rajoshi and Gupta, Abhinav and Gupta, Anushrut and Gopal, Tanmai and Parameswaran, Aditya G},
  journal={arXiv preprint arXiv:2603.20576},
  year={2026}
}

Intended Use

Use this dataset to launch Altimate-native teacher rollouts or compile downstream SFT/RL artifacts that share the same sandbox task manifest and validator/reward path.

A typical pipeline is:

prompt shell + sandbox_task_manifest
  -> Altimate no-context sandbox rollout
  -> strict validator / reward check
  -> SFT trajectory selection or RL training rows

Caveats

Because this is a prompt-shell dataset, reward_model.ground_truth is intentionally empty and teacher_rollout_required=true for all rows. Do not treat it as finished RL trajectory data without running the sandbox harness and validators.

Downloads last month: 35

Total file size:

478 kB

Paper for forseasons/dataagentbench-derived-official54-altimate-prompt-shell

Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents

Paper • 2603.20576 • Published Mar 21 • 4