README.md

---
license: cc-by-4.0
task_categories:
- visual-question-answering
language:
- en
pretty_name: SSI-Bench
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: test
    path: "SSI_Bench.parquet"
dataset_info:
  features:
    - name: index
      dtype: int64
    - name: image
      sequence: image
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: annotation_color
      dtype: string
    - name: category
      dtype: string
    - name: task
      dtype: string
---


# Thinking in Structures: Evaluating Spatial Intelligence through Reasoning on Constrained Manifolds

SSI-Bench is constructed from **complex real-world 3D structures**, where feasible configurations are tightly governed by **geometric, topological, and physical constraints**.

- Project page: https://ssi-bench.github.io/
- Code & evaluation: https://github.com/ccyydd/SSI-Bench
- Paper: https://arxiv.org/abs/2602.07864


## News
- 🔥 [2026-2-10]: We released our paper, benchmark, and evaluation codes.

## Load Dataset
```
from datasets import load_dataset

dataset = load_dataset("cyang203912/SSI-Bench")
print(dataset)
```

After downloading the parquet file, read each record, decode images from binary, and save them as JPG files.
```
import pandas as pd
import os

df = pd.read_parquet("SSI_Bench.parquet")

output_dir = "./images"
os.makedirs(output_dir, exist_ok=True)

for _, row in df.iterrows():
    index_val = row["index"]
    images = row["image"]
    question = row["question"]
    answer = row["answer"]
    annotation_color = row["annotation_color"]
    category = row["category"]
    task = row["task"]

    image_paths = []
    if images is not None:
        for n, img_data in enumerate(images):
            image_path = f"{output_dir}/{index_val}_{n}.jpg"
            with open(image_path, "wb") as f:
                f.write(img_data)
            image_paths.append(image_path)
    else:
        image_paths = []

    print(f"index: {index_val}")
    print(f"image: {image_paths}")
    print(f"question: {question}")
    print(f"answer: {answer}")
    print(f"annotation_color: {annotation_color}")
    print(f"category: {category}")
    print(f"task: {task}")
    print("-" * 50)
```

## Usage
To evaluate, follow the scripts in the code repository: https://github.com/ccyydd/SSI-Bench.

## Leaderboard
| Model | Avg. (%) | Type |
|---|---:|---|
| Human Performance | 91.60 | Baseline |
| Gemini-3-Flash | 33.60 | Proprietary |
| Gemini-3-Pro | 29.50 | Proprietary |
| GPT-5.2 | 29.10 | Proprietary |
| Gemini-2.5-Pro | 26.10 | Proprietary |
| GPT-5 mini | 25.90 | Proprietary |
| Seed-1.8 | 25.90 | Proprietary |
| GPT-4o | 22.60 | Proprietary |
| GPT-4.1 | 22.40 | Proprietary |
| Gemini-2.5-Flash | 22.30 | Proprietary |
| GLM-4.6V | 22.20 | Open-source |
| Qwen3-VL-235B-A22B | 21.90 | Open-source |
| GLM-4.5V | 21.40 | Open-source |
| GLM-4.6V-Flash | 21.10 | Open-source |
| Qwen3-VL-4B | 20.70 | Open-source |
| InternVL3.5-30B-A3B | 20.70 | Open-source |
| Qwen3-VL-30B-A3B | 20.60 | Open-source |
| Llama-4-Scout-17B-16E | 20.60 | Open-source |
| Gemma-3-27B | 20.50 | Open-source |
| InternVL3.5-8B | 20.20 | Open-source |
| Claude-Sonnet-4.5 | 19.90 | Proprietary |
| Gemma-3-4B | 19.70 | Open-source |
| Qwen3-VL-8B | 19.20 | Open-source |
| Qwen3-VL-2B | 19.20 | Open-source |
| InternVL3.5-38B | 19.00 | Open-source |
| InternVL3.5-241B-A28B | 18.30 | Open-source |
| InternVL3.5-14B | 17.90 | Open-source |
| Gemma-3-12B | 17.30 | Open-source |
| LLaVA-Onevision-72B | 17.20 | Open-source |
| InternVL3.5-4B | 16.80 | Open-source |
| LLaVA-Onevision-7B | 16.50 | Open-source |
| Random Guessing | 12.85 | Baseline |
| InternVL3.5-2B | 11.10 | Open-source |

## Citation
```bibtex
@article{yang2026thinking,
  title={Thinking in Structures: Evaluating Spatial Intelligence through Reasoning on Constrained Manifolds},
  author={Chen Yang and Guanxin Lin and Youquan He and Peiyao Chen and Guanghe Liu and Yufan Mo and Zhouyuan Xu and Linhao Wang and Guohui Zhang and Zihang Zhang and Shenxiang Zeng and Chen Wang and Jiansheng Fan},
  journal={arXiv preprint arXiv:2602.07864},
  year={2026}
}
```