Datasets:

thuml
/

VisWorld-Eval

task_categories:
  - any-to-any
language:
  - en
configs:
  - config_name: main
    data_files:
      - split: ballgame
        path: ballgame/*.parquet
      - split: cube
        path: cube/*.parquet
      - split: maze
        path: maze/*.parquet
      - split: mmsi
        path: mmsi/*.parquet
      - split: multihop
        path: multihop/*.parquet
      - split: paperfolding
        path: paperfolding/*.parquet
      - split: sokoban
        path: sokoban/*.parquet
tags:
  - multimodal
  - reasoning
  - world-models

VisWorld-Eval: Task Suite for Reasoning with Visual World Modeling 🌏

📋 Introduction

The VisWorld-Eval suite is for assessing multimodal reasoning with visual world modeling. It comprises seven tasks spanning both synthetic and real-world domains, each designed to isolate and demand specific atomic world-model capabilities.

Task	Capability	Domain	Test Samples	Source / Reference
Paper folding	Simulation	Synthetic	480	SpatialViz
Multi-hop manipulation	Simulation	Synthetic	480	ZebraCoT, CLEVR
Ball tracking	Simulation	Synthetic	1,024	RBench-V
Maze	Simulation	Synthetic	480	maze-dataset
Sokoban	Simulation	Synthetic	480	Game-RL
Cube 3-view projection	Reconstruction	Synthetic	480	SpatialViz
Real-world spatial reasoning	Reconstruction	Real-world	522	MMSI-Bench

⚙️ Load Data

Load from 🤗 HuggingFace:

from datasets import load_dataset
ds = load_dataset("thuml/VisWorld-Eval")

🏆 Leaderboard

Zero-shot evaluation of advanced VLMs on VisWorld-Eval: We report the average accuracy over five tasks (excluding Maze and Sokoban) and over all seven tasks.

Models	Paper Folding	Multi-Hop Manip.	Ball Tracking	Cube 3-View	MMSI (Pos. Rel.)	Maze	Sokoban	Overall (5 tasks)	Overall (7 tasks)
Gemini 3 Flash	25.6	75.4	55.3	52.7	41.3	73.9	99.3	50.0	60.5
Gemini 3 Pro	27.0	74.5	44.7	53.3	49.6	33.5	90.2	49.8	53.2
Seed 1.8	10.6	75.2	24.4	42.5	38.8	83.9	68.3	38.3	49.1
GPT 5.1	6.4	73.9	34.8	44.5	44.8	0.6	62.8	40.8	38.2
o3	13.5	68.1	24.7	37.7	44.4	0.0	36.0	37.6	32.0
Qwen3-VL-8B-Thinking	11.0	49.3	17.8	21.2	27.7	0.0	5.8	25.4	18.9
BAGEL-7B-MoT	11.2	31.6	19.4	26.8	27.2	0.0	0.2	23.2	16.6

🚀 Release Progress

VisWorld-Eval data
VisWorld-Eval evaluation scripts

📜 Citation

If you find this project useful, please cite our paper as:

@article{wu2026visual,
    title={Visual Generation Unlocks Human-Like Reasoning through Multimodal World Models}, 
    author={Jialong Wu and Xiaoying Zhang and Hongyi Yuan and Xiangcheng Zhang and Tianhao
Huang and Changjing He and Chaoyi Deng and Renrui Zhang and Youbin Wu and Mingsheng
Long},
    journal={arXiv preprint arXiv:2601.19834},
    year={2026},
}

🤝 Contact

If you have any questions, please contact wujialong0229@gmail.com.

💡 Acknowledgement

We sincerely appreciate the following projects for their valuable codebase and task design: SpatialViz, RBench-V, maze-dataset, Game-RL, clevr-dataset-gen, MMSI-Bench.