---
license: mit
task_categories:
  - image-text-to-text
language:
  - en
tags:
  - visual-reasoning
  - synthetic
  - multimodal
  - benchmark
  - vision-language
arxiv: 2511.20814
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: eval
        path: data/eval-*
dataset_info:
  features:
    - name: images
      list: image
    - name: problem
      dtype: string
    - name: answer
      dtype: string
    - name: task
      dtype: string
  splits:
    - name: train
      num_bytes: 1511015259
      num_examples: 32000
    - name: eval
      num_bytes: 135942602
      num_examples: 2500
  download_size: 1625026463
  dataset_size: 1646957861
---

# SPHINX: A Synthetic Environment for Visual Perception and Reasoning

SPHINX is a synthetic multimodal reasoning benchmark and data-generation environment built around verifiable visual reasoning tasks. It contains procedurally generated puzzles spanning symmetry, geometric transformations, spatial reasoning, chart interpretation, and sequence prediction, each paired with a ground-truth answer. The dataset is designed both for precise evaluation of vision-language models and for large-scale supervised or reinforcement-learning-style post-training.

## Links

- Project page: https://maveryn.github.io/sphinx/
- Paper: https://arxiv.org/abs/2511.20814
- Code: https://github.com/maveryn/sphinx
- Models collection: https://huggingface.co/collections/maveryn/sphinx-models
- Qwen3 4B model: https://huggingface.co/maveryn/sphinx-qwen3-4b
- Qwen3 8B model: https://huggingface.co/maveryn/sphinx-qwen3-8b
- Interactive demo: https://maveryn.github.io/sphinx/demo/

## Dataset Summary

- Training examples: 32,000
- Evaluation examples: 2,500
- Task families: 25
- Modalities: image + text question -> text answer

Each example contains:

- `images`: one or more images associated with the problem
- `problem`: the natural-language question or instruction
- `answer`: the verified ground-truth answer
- `task`: the task name

## What SPHINX Covers

SPHINX includes 25 procedurally generated tasks across several core reasoning categories:

- symmetry and pattern completion
- geometric and spatial reasoning
- chart and proportion understanding
- transformations and analogical matching
- arithmetic and visual sequence prediction
- tile-based composition, counting, and path reasoning

The emphasis is on controlled synthetic tasks with verifiable answers, so model performance can be evaluated more cleanly than on open-ended real-world datasets alone.

## Loading the Dataset

```python
from datasets import load_dataset

train_ds = load_dataset("maveryn/sphinx", split="train")
eval_ds = load_dataset("maveryn/sphinx", split="eval")

print(train_ds[0].keys())
print(train_ds[0]["task"])
print(train_ds[0]["problem"])
print(train_ds[0]["answer"])
```

## Typical Usage

SPHINX can be used for:

- benchmarking multimodal reasoning systems
- analyzing per-task failure modes in vision-language models
- building synthetic training data for multimodal post-training
- evaluating transfer from verifiable synthetic reasoning to external benchmarks

## Citation

If you use SPHINX, please cite:

```bibtex
@inproceedings{alam2026sphinx,
  author    = {Md Tanvirul Alam and Saksham Aggarwal and Justin Yang Chae and Nidhi Rastogi},
  title     = {SPHINX: A Synthetic Environment for Visual Perception and Reasoning},
  booktitle = {2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition- FINDINGS Track (CVPRF)},
  year      = {2026}
}
```