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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:    ValueError
Message:      Invalid string class label FysicsReason@8b520a2071ff8637035f660002c2e96ad98a8e94
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 2386, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2303, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2178, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1483, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1158, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1095, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1116, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label FysicsReason@8b520a2071ff8637035f660002c2e96ad98a8e94

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FysicsReason: Benchmarking Verifiable World-State Reasoning Across Omni-Modalities

🏠 Project Page    📖 Paper    🤗 Dataset   

Dataset Overview

FysicsReason is a five-task omni-modal benchmark designed to evaluate physical reasoning across images, audio, video, and text. The benchmark contains five complementary tasks that cover visual understanding, audio-visual grounding, temporal reasoning, physical property comparison, and quantitative physical inference. Each task is provided as a separate Parquet file under task{1..5}/, together with its associated media files.

Task Composition

Task Samples Input Prediction Target
task1 562 Image and text question Target quantity and final answer
task2 779 Image, scene audio, and text question Object bounding box and answer option
task3 250 Video and multiple-choice text question Evidence interval and answer option
task4 350 Two object images, object audio, and text question Property, materials, and selected object
task5 387 Video and text question Physical property and numerical answer

Data Organization

Media paths stored in the Parquet files are relative to the dataset root. For example: data/task2/media/... When loading the dataset locally, these paths should therefore be resolved relative to the directory containing the data/ folder. Each task directory contains its corresponding annotation file and linked media resources, allowing the benchmark to be used directly for task-specific or unified omni-modal evaluation.

Sample Identification

The index field serves as the stable, zero-based row identifier within each task. For evaluation and result submission, a sample can be uniquely identified using the combination of:

  • task_source
  • index This convention provides a consistent mapping between dataset samples and inference outputs.

Evaluation

Inference results produced on FysicsReason can be evaluated using the official workflow provided in the FysicsReason repository. FysicsReason Evaluation Repository

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