Vision-Language Models: Uncovering a Surprising Spatial Reasoning Gap
research#computer vision📝 Blog|Analyzed: Feb 20, 2026 17:47•
Published: Feb 20, 2026 13:30
•1 min read
•r/MachineLearningAnalysis
This research reveals exciting insights into how different types of visual input affect the spatial reasoning capabilities of Vision-Language Models. The findings highlight areas for innovation in visual processing and could lead to breakthroughs in how these models interpret and interact with the world.
Key Takeaways
- •VLMs perform significantly better at recognizing text-based grids than equivalent filled-square grids.
- •Different models exhibit unique failure modes when processing square grids, hinting at distinct visual processing strategies.
- •Gemini shows high performance on sparse grids, suggesting a strong visual pathway, but it struggles with increased density.
Reference / Citation
View Original"Vision-Language Models achieve ~84% F1 reading binary grids rendered as text characters (. and #) but collapse to 29-39% F1 when the exact same grids are rendered as filled squares, despite both being images through the same visual encoder."