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Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
clues_changed: int64
constraints_violation: int64
completion_accuracy: int64
action_reflection: int64
overall_score: int64
cross_wall: int64
target_achievement: int64
maze_changed: int64
to
{'maze_changed': Value('int64'), 'cross_wall': Value('int64'), 'action_reflection': Value('int64'), 'target_achievement': Value('int64'), 'overall_score': Value('int64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
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
clues_changed: int64
constraints_violation: int64
completion_accuracy: int64
action_reflection: int64
overall_score: int64
cross_wall: int64
target_achievement: int64
maze_changed: int64
to
{'maze_changed': Value('int64'), 'cross_wall': Value('int64'), 'action_reflection': Value('int64'), 'target_achievement': Value('int64'), 'overall_score': Value('int64')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
maze_changed int64 | cross_wall int64 | action_reflection int64 | target_achievement int64 | overall_score int64 |
|---|---|---|---|---|
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 0 | 1 | 0 |
1 | 0 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 0 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 0 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 0 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 0 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 0 | 0 | 0 |
1 | 1 | 0 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 0 | 0 | 0 | 0 |
1 | 1 | 0 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 0 | 0 | 0 |
1 | 1 | 0 | 0 | 0 |
1 | 1 | 0 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 0 | 1 | 0 |
1 | 1 | 0 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 0 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 0 | 1 | 0 |
1 | 1 | 0 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 0 | 1 | 0 |
1 | 1 | 0 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 0 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 0 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 0 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 0 | 1 | 0 |
1 | 1 | 0 | 1 | 0 |
1 | 1 | 0 | 1 | 0 |
1 | 0 | 0 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 1 | 1 | 0 |
1 | 1 | 0 | 0 | 0 |
1 | 1 | 1 | 0 | 0 |
1 | 1 | 1 | 1 | 0 |
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MMGR-Open
This repository is the public release mirror for the MMGR benchmark assets that are currently organized under code/ and data/.
Layout
code/ Evaluation prompts and scripts.
data/ Input images, generation prompts, ground-truth solutions where applicable.
The machine-readable release index is dataset_manifest.json.
Dataset Manifest
dataset_manifest.json describes:
- task names and directory locations;
- sample counts for prompts, images, and solutions;
- how images, prompts, solutions, and task-level metadata are paired;
- which missing fields are intentional and which still require source mapping.
Use the manifest as the authoritative starting point for consuming this release.
Pairing Rules
Abstract Reasoning
ARC, Maze, and Sudoku provide one input image per ground-truth solution image. Their image and solution files are paired by matching task-specific filenames or relative directory structure.
Math uses rendered problem images plus aggregated JSON files under data/solution/abstract_reasoning/math/. The JSON files contain the corresponding problem statements and solutions.
Embodied Navigation
Embodied-navigation prompt templates live in data/prompt/embodied_nav/<task>/.
For color-goal samples, the visible target is encoded in the input image and the matching floor/quality/turn subset directory.
For object-goal samples, prompts contain {LOCATION_DESCRIPTION}. Resolve that placeholder with:
data/prompt/embodied_nav/location_descriptions_manifest.jsonl
Each JSONL record maps one public object-goal input image to its location_description, prompt template paths, subset directory, and source file. The original source text files are not required for normal use; if retained later, they should be treated only as optional human-readable copies under data/prompt/embodied_nav/location_descriptions/<task>/<subset_dir>/<sample_id>.txt.
The location-description manifest follows the canonical benchmark split: 60 object-goal records per embodied-navigation task, 240 records total. It does not include fallback records from another quality or subset.
Embodied-navigation images also follow the canonical benchmark split: each of the four tasks has 60 color-goal input images and 60 object-goal input images, for 480 navigation images total.
Navigation ground-truth solution files are not included in this release mirror yet. Their source mapping is still pending.
Physical Commonsense
Physical commonsense is a prompt-driven video-generation/evaluation task. It does not require input images or solution images in this release layout. The prompt JSON files under data/prompt/physical_commonsense/ define the generation prompts; the VLM evaluation prompt is under code/physical_commonsense/vlm_based_evaluation/.
sports_prompts.json already contains prompt, physics_focus, and expected_motion. physical_concept_prompts.json still needs an explicit mapping for the VLM placeholders prompt_text, physics_info, and expected_motion.
Known Missing Or Pending Items
data/metadata/is not present. The rootdataset_manifest.jsoncurrently serves as the release-level manifest, but task-level metadata may still need to be added.- Embodied-navigation solutions are not present and still require an approved mapping from source ground-truth files.
- Physical commonsense images and solutions are intentionally absent for the current prompt-only video task design.
- Two duplicate Google Drive math JSON files were observed during local sync; the local mirror collapsed them to unique files.
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