README.md

---
pretty_name: VisReason
task_categories:
  - visual-question-answering
tags:
  - multimodal
  - visual-reasoning
  - mllm
  - benchmark
size_categories:
  - 1K<n<10K
---

# VisReason

VisReason is a benchmark for evaluating vision-centric reasoning in everyday scenarios where perception and inference are tightly coupled. It is designed to test whether multimodal large language models can reason directly from visual evidence rather than relying mainly on language-mediated abstractions.

The dataset contains **1,505** carefully curated questions across **10 reasoning categories**, covering perceptual, structural, and conceptual reasoning tasks.

## 📥 Download

You can download the dataset with the Hugging Face CLI:

```bash
hf download CASIA-IVA-Lab/VisReason --repo-type dataset --local-dir ./data
```

The official evaluation scripts expect the dataset to be available under `./data` in the project root.



## 🗂️ Dataset Structure

```text
data/
  img_<class_number>/
    datajson_label.<ext>
  class_1.jsonl
  class_2.jsonl
  ...
  class_10.jsonl
  datasets.json
```

`datasets.json` is the dataset index used by the evaluation scripts. Modify this file to select or adjust the classes to evaluate.

Images are stored under the corresponding `img_<class_number>` folders. Each image file is named with the source dataset key and sample label:

```text
datajson_label.<ext>
```



## 📊 Data Files

| Class | Data file | Samples | Image folder |
| --- | --- | ---: | --- |
| `class_1` | `class_1.jsonl` | 40 | `img_1/` |
| `class_2` | `class_2.jsonl` | 100 | `img_2/` |
| `class_3` | `class_3.jsonl` | 46 | `img_3/` |
| `class_4` | `class_4.jsonl` | 130 | `img_4/` |
| `class_5` | `class_5.jsonl` | 200 | `img_5/` |
| `class_6` | `class_6.jsonl` | 135 | `img_6/` |
| `class_7` | `class_7.jsonl` | 200 | `img_7/` |
| `class_8` | `class_8.jsonl` | 111 | `img_8/` |
| `class_9` | `class_9.jsonl` | 275 | `img_9/` |
| `class_10` | `class_10.jsonl` | 268 | `img_10/` |



## 🧾 Data Format

Each `class_*.jsonl` file contains one JSON object per line. A sample has the following fields:

### ◆ Sample Fields

| Field | Description |
| --- | --- |
| `class` | Class identifier. |
| `label` | Sample identifier within the source data. |
| `question type` | Question format used for prompting and evaluation. |
| `question` | Natural-language question. |
| `answer` | Ground-truth answer, including boxes for localization tasks. |
| `images` | Image paths associated with the sample. |
| `datajson` | Source split. |
| `Height` / `Weight` | Image size fields used by the released files. |
| `url` | Original source URL, when available. |

### ◆ Question Type

| Type | Format |
| --- | --- |
| `1` | Multiple-choice |
| `2` | Short-answer |
| `3` | Open-ended |
| `4` | Bounding-box localization |



## 🧪 Evaluation

The evaluation code is available in the GitHub repository:

```text
https://github.com/CASIA-IVA-Lab/VisReason
```

Inference results are expected to be saved as:

```text
results/<model>/class_X_results.json
results/<model>_cot/class_X_results.json
```

The evaluator writes per-class judging files and a final summary:

```text
results/<model>/class_X_judge.json
results/<model>/summary.json
```

For `class_1` and `class_2`, bounding-box predictions are evaluated with IoU at threshold 0.5. Other classes are evaluated by an LLM-based judge. The final score is the unweighted mean over all class accuracies.



## 📄 License


## 📝 Citation

```bibtex
@inproceedings{guo-etal-2026-mllms,
    title = "Can {MLLM}s Reason Beyond Language? {V}is{R}eason: A Comprehensive Benchmark for Vision-Centric Reasoning",
    author = "Guo, Longteng  and
      Wang, Yifan  and
      Huo, Pengkang  and
      Chen, Tailai  and
      Wu, Yuze  and
      Liu, Jing  and
      Zhu, Xinxin",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-acl.1996/",
    doi = "10.18653/v1/2026.findings-acl.1996",
    pages = "40149--40192",
    ISBN = "979-8-89176-395-1",
    abstract = "Recent multimodal large language models (MLLMs) achieve strong performance on visual reasoning benchmarks, yet it remains unclear to what extent such performance reflects reasoning directly grounded in visual evidence. We introduce VisReason, a benchmark for vision-centric reasoning in everyday scenarios where perception and inference are tightly coupled. VisReason contains 1,505 questions across 10 categories spanning perceptual, structural, and conceptual reasoning. Our evaluation shows that VisReason poses a qualitatively different challenge from existing benchmarks, exposing substantial gaps between humans and current MLLMs and revealing limited benefits from test-time reasoning strategies. VisReason offers a focused diagnostic for evaluating vision-centric reasoning beyond language."
}
```

## 📬 Contact

If you have any questions, please reach out to:

*   Yifan Wang - wangyifan2026@ia.ac.cn