![[2602.19357] MentalBlackboard: Evaluating Spatial Visualization via Mathematical Transformations](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.19357] MentalBlackboard: Evaluating Spatial Visualization via Mathematical Transformations
Summary
The paper 'MentalBlackboard' evaluates spatial visualization capabilities of Vision-Language Models (VLMs) through mathematical transformations, revealing significant challenges in tasks like prediction and planning.
Why It Matters
Understanding spatial visualization in AI is crucial as it impacts the development of models that can better interpret and interact with physical environments. This research highlights the limitations of current VLMs, guiding future improvements in AI capabilities.
Key Takeaways
- VLMs struggle with symmetrical transformations and rotations.
- Planning tasks expose limitations in analyzing symmetrical relationships.
- The highest accuracy achieved in planning tasks was only 10%.
- The top-performing model, o3, excelled in generalization but not in text-based predictions.
- This research sets a benchmark for future studies in spatial visualization.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.19357 (cs) [Submitted on 22 Feb 2026] Title:MentalBlackboard: Evaluating Spatial Visualization via Mathematical Transformations Authors:Nilay Yilmaz, Maitreya Patel, Naga Sai Abhiram Kusumba, Yixuan He, Yezhou Yang View a PDF of the paper titled MentalBlackboard: Evaluating Spatial Visualization via Mathematical Transformations, by Nilay Yilmaz and 4 other authors View PDF HTML (experimental) Abstract:Spatial visualization is the mental ability to imagine, transform, and manipulate the spatial characteristics of objects and actions. This intelligence is a part of human cognition where actions and perception are connected on a mental level. To explore whether state-of-the-art Vision-Language Models (VLMs) exhibit this ability, we develop MentalBlackboard, an open-ended spatial visualization benchmark for Paper Folding and Hole Punching tests within two core tasks: prediction and planning. Our prediction experiments reveal that models struggle with applying symmetrical transformations, even when they predict the sequence of unfolding steps correctly. Also, rotations introduce a significant challenge to the physical situational awareness for models. The planning task reveals limitations of models in analyzing symmetrical relationships and in implementing the multi-stage symmetry process, with Claude Opus 4.1 achieving the highest planning score at an accuracy of 10\%. The top-performing model, o3, attains a...
