---
pretty_name: ReGap-Bench
language:
- en
task_categories:
- visual-question-answering
tags:
- multimodal
- visual-reasoning
- reference-gap
- benchmark
size_categories:
- n<1K
configs:
- config_name: shape_counting
  data_files:
  - split: test
    path: shape_counting/test-*.parquet
- config_name: angle_ranking
  data_files:
  - split: test
    path: angle_ranking/test-*.parquet
- config_name: size_ranking
  data_files:
  - split: test
    path: size_ranking/test-*.parquet
- config_name: clock_ranking
  data_files:
  - split: test
    path: clock_ranking/test-*.parquet
- config_name: transformation_prediction
  data_files:
  - split: test
    path: transformation_prediction/test-*.parquet
---

# 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

```python
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

```bibtex
@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}, 
}
```