Project page for the paper "MindCube: Spatial Mental Modeling from Limited Views" (arXiv:2506.21458), live at mind-cube.github.io.
MindCube is a spatial reasoning benchmark that asks whether Vision-Language Models (VLMs) can imagine a full scene from just a few views, the way humans form spatial mental models of unseen space. The benchmark contains 21,154 questions across 3,268 multi-view images, testing three abilities from limited views: representing positions (cognitive mapping), orientations (perspective-taking), and dynamics (mental simulation for "what-if" movements). Existing VLMs perform near-randomly on it. Jointly training a model to first generate a cognitive map of the scene and then reason over it ("map-then-reason") lifts accuracy from 37.8% to 57.8%, and adding reinforcement learning pushes it to 61.3%.
- Paper: https://arxiv.org/abs/2506.21458
- Code: https://github.com/mll-lab-nu/MindCube
- Dataset: https://huggingface.co/datasets/MLL-Lab/MindCube
- Model checkpoints: https://huggingface.co/MLL-Lab/models
- MindCube Challenge (CVinW Workshop @ CVPR 2026): https://mind-cube.github.io/challenge
- LLM-facing summary: https://mind-cube.github.io/llms.txt
@misc{wang2025mindcube,
title={MindCube: Spatial Mental Modeling from Limited Views},
author={Qineng Wang and Baiqiao Yin and Pingyue Zhang and Jianshu Zhang and Kangrui Wang and Zihan Wang and Jieyu Zhang and Keshigeyan Chandrasegaran and Han Liu and Ranjay Krishna and Saining Xie and Jiajun Wu and Li Fei-Fei and Manling Li},
year={2025},
eprint={2506.21458},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2506.21458},
}MindCube is a project of the MLL Lab at Northwestern University (PI: Manling Li), with collaborators at Stanford University, New York University, and the University of Washington.
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