Datasets:
dataset_info:
features:
- name: images
list:
image:
decode: false
- name: image_group_id
dtype: string
- name: qa
list:
- name: inputs
dtype: string
- name: choices_list
list: string
- name: labels
dtype: int64
- name: metadata
dtype: string
splits:
- name: train
num_bytes: 1073898773
num_examples: 92114
- name: validation
num_bytes: 1087431
num_examples: 100
download_size: 1074000270
dataset_size: 1074986204
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
language:
- en
license: apache-2.0
source_datasets: []
task_categories:
- multiple-choice
- visual-question-answering
tags:
- decision-model
- procedural
visgym-mcq
Balanced static VisGym: mazes, equation repair, stick rotation and block legality.
One mirror, with 25,000 training and 25 held-out examples per family at the default limits. Every choice is verified against its instance. Fresh native procedural images only; no photographic assets or published benchmark rows. Metadata records family, seed, geometry and verification. Apache-2.0.
Original data: original distribution. Repackaged as parquet for tasksource by scripts/repackage_dataset/procedural/balanced_visgym.py.
Conversion provenance: source revisions, hashes, configuration and exclusions.
Upstream asset credits and licenses accompany the original rendered images.
The training split contains 100,000 QAs, 25,000 per family, stored in 92,114 image groups. Validation has 100 QAs, 25 per family. Images shared by several questions are stored once; load_task("visgym/mcq", vision=True) exposes the flattened examples. Both splits are shuffled with seed 42.