ReGap-Bench

ReGap-Bench is the benchmark from Diagnosing the Reference Gap: What Visual Scaffolds Can and Cannot Fix in Multimodal Reasoning.

It is a paired reference-intervention benchmark of five visual reasoning tasks. Each scene is rendered under a plain condition and one or more reference conditions (numbering, shading, grid lines), while the scene content, question, and answer stay fixed. Comparing a model's accuracy across conditions isolates the effect of the reference itself.

Subsets

Each subset has 200 scenes in a single test split.

Subset	Question	Answer	Conditions
shape_counting	Count shapes of a given class	integer	plain, numbering, shading, grid
angle_ranking	Rank angles by size	list of labels	plain, shading, grid
size_ranking	Rank shapes by size	list of labels	plain, shading, grid
clock_ranking	Rank clocks by time	list of labels	plain, numbering, shading, grid
transformation_prediction	Pick the result of a transformation chain	option letter (A–D)	plain, shading, grid

Fields

Field	Description
image_plain	The scene without added references.
image_numbering	The scene with numbering. Only in shape_counting and clock_ranking.
image_shading	The scene with shaded interiors.
image_grid	The scene with grid lines.
question	The question asked under every condition.
answer	The gold answer, identical across conditions.
info	Task-specific metadata (below).

info by subset:

• shape_counting: grid_dimensions, total_shape_count, target_shape (labels of the shapes that match the question).
• angle_ranking: grid_dimensions, angles_present (angle sizes in degrees, in label order).
• size_ranking: grid_dimensions, scales (scale factor of each shape, in label order).
• clock_ranking: grid_dimensions, times_present (time shown on each clock, in label order).
• transformation_prediction: transform_chain (the transformation steps), options_details (how each option A–D was constructed).

Usage

from datasets import load_dataset

ds = load_dataset("regap-bench/ReGap-Bench", "shape_counting", split="test")
row = ds[0]
row["image_plain"]      # PIL image
row["question"], row["answer"]

Citation

@misc{chen2026diagnosingreferencegapvisual,
      title={Diagnosing the Reference Gap: What Visual Scaffolds Can and Cannot Fix in Multimodal Reasoning}, 
      author={Guizhen Chen and Weiwen Xu and Wotao Yin and Anh Tuan Luu and Hou Pong Chan},
      year={2026},
      eprint={2610.39419},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2610.39419}, 
}