README.md

---
license: mit
task_categories:
- image-text-to-text
---

# 🧩 VRQA-Bench

[![Paper](https://img.shields.io/badge/arXiv-2606.03603-b31b1b.svg)](https://arxiv.org/abs/2606.03603)
[![Code](https://img.shields.io/badge/GitHub-PF--OPSD-181717.svg?logo=github)](https://github.com/yczhou001/PF-OPSD)

VRQA-Bench is a multiple-choice visual reasoning benchmark built on 🌀 **maze** navigation and 📦 **Sokoban** puzzles, as presented in the paper [World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning](https://huggingface.co/papers/2606.03603). Each sample provides an initial-state image, an optimal-solution video, and a question about the planned trajectory.

## 🏷️ Categories

| Category | Task | Description |
|----------|------|-------------|
| `C1_turn_count`      | 🌀 maze    | Total number of direction changes along the path |
| `C2_turn_direction`  | 🌀 maze    | Number of LEFT / RIGHT turns |
| `C4_sokoban_push`    | 📦 sokoban | Direction of a specific push |
| `C5_direction_count` | 🌀 maze    | Number of steps in a given direction |
| `C6_push_dir_count`  | 📦 sokoban | Number of pushes in a given direction |

## 📝 Annotation Fields

`question`, `options` (A–D), `answer` (correct key), `correct_value`, `task_type`, `input_image` / `video_path` (relative to each split dir), `_solution` (ground-truth solution trace).

## 📌 Citation

```bibtex
@misc{zhou2026worldmodelsmeetlanguage,
      title={World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning},
      author={Yucheng Zhou and Wei Tao and Yiwen Guo and Jianbing Shen},
      year={2026},
      eprint={2606.03603},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.03603},
}
```