Datasets:

Apexintelligence-AI
/

ASI-Bench-seed31415

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
schema_version: int64
task_id: string
prompt_level: string
expected_outputs: list<item: struct<name: string, type: string>>
  child 0, item: struct<name: string, type: string>
      child 0, name: string
      child 1, type: string
timeout_seconds: int64
instance_id: string
parameters: struct<seed: int64, generation_method: string, n_genes: int64, n_control: int64, n_perturbation_a: i (... 150 chars omitted)
  child 0, seed: int64
  child 1, generation_method: string
  child 2, n_genes: int64
  child 3, n_control: int64
  child 4, n_perturbation_a: int64
  child 5, n_perturbation_b: int64
  child 6, n_modules: int64
  child 7, effect_size: double
  child 8, noise_scale: double
  child 9, correlation_strength: double
  child 10, strength_variability: double
convergence_decision_values: struct<convergent: int64, not_convergent: int64>
  child 0, convergent: int64
  child 1, not_convergent: int64
n_genes: int64
control_label: string
case_id: string
perturbation_b_label: string
gene_columns: list<item: string>
  child 0, item: string
case_output_dir: string
alpha: double
required_outputs: list<item: string>
  child 0, item: string
perturbation_a_label: string
to
{'case_id': Value('string'), 'control_label': Value('string'), 'perturbation_a_label': Value('string'), 'perturbation_b_label': Value('string'), 'alpha': Value('float64'), 'n_genes': Value('int64'), 'gene_columns': List(Value('string')), 'required_outputs': List(Value('string')), 'case_output_dir': Value('string'), 'convergence_decision_values': {'convergent': Value('int64'), 'not_convergent': 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
              schema_version: int64
              task_id: string
              prompt_level: string
              expected_outputs: list<item: struct<name: string, type: string>>
                child 0, item: struct<name: string, type: string>
                    child 0, name: string
                    child 1, type: string
              timeout_seconds: int64
              instance_id: string
              parameters: struct<seed: int64, generation_method: string, n_genes: int64, n_control: int64, n_perturbation_a: i (... 150 chars omitted)
                child 0, seed: int64
                child 1, generation_method: string
                child 2, n_genes: int64
                child 3, n_control: int64
                child 4, n_perturbation_a: int64
                child 5, n_perturbation_b: int64
                child 6, n_modules: int64
                child 7, effect_size: double
                child 8, noise_scale: double
                child 9, correlation_strength: double
                child 10, strength_variability: double
              convergence_decision_values: struct<convergent: int64, not_convergent: int64>
                child 0, convergent: int64
                child 1, not_convergent: int64
              n_genes: int64
              control_label: string
              case_id: string
              perturbation_b_label: string
              gene_columns: list<item: string>
                child 0, item: string
              case_output_dir: string
              alpha: double
              required_outputs: list<item: string>
                child 0, item: string
              perturbation_a_label: string
              to
              {'case_id': Value('string'), 'control_label': Value('string'), 'perturbation_a_label': Value('string'), 'perturbation_b_label': Value('string'), 'alpha': Value('float64'), 'n_genes': Value('int64'), 'gene_columns': List(Value('string')), 'required_outputs': List(Value('string')), 'case_output_dir': Value('string'), 'convergence_decision_values': {'convergent': Value('int64'), 'not_convergent': Value('int64')}}
              because column names don't match

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ASI-Bench Generated Instances — Seed 31415

Website · GitHub · Leaderboard

Dataset Summary

ASI-Bench evaluates general intelligence, innovation, and autonomous execution through 60 project-level scientific research tasks spanning 11 domains. Each task provides four matched prompt conditions: B1 gives full methodological guidance, B2 identifies the method without the full procedure, B3 provides only the research objective and data, and B4 adds task-irrelevant information to B3.

This repository contains the fixed seed 31415 generated instances, including prompts, task inputs, reference artifacts, and available instance-generation metadata.

For complete benchmark information and instructions for downloading, running, and submitting evaluations, see the ASI-Bench GitHub repository.

Dataset Structure

tasks/
└── <task_id>__seed31415/
    ├── prompt_b1.md
    ├── prompt_b2.md
    ├── prompt_b3.md
    ├── prompt_b4.md
    ├── data/
    │   └── ...
    ├── reference/
    │   └── ...
    ├── instance_meta.json          # where available
    └── framework_task_info.json    # where available

Each directory under tasks/ is one generated benchmark instance. data/ contains task inputs, reference/ contains reference artifacts, and the JSON files record available generation and framework metadata.

License

This dataset is licensed under the Apache License 2.0.

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Total file size:

119 MB