SPACENUM: Revisiting Spatial Numerical Understanding in VLMs
The paper titled 'SPACENUM: Revisiting Spatial Numerical Understanding in VLMs' explores the capabilities of Vision-Language Models (VLMs) in producing numerical outputs related to spatial perception. The authors introduce a framework to evaluate how well these models understand the relationship between spatial structures and numerical representations. Their findings indicate that current VLMs struggle to accurately ground numerical values in spatial contexts, often performing close to random guessing.
- ▪The study focuses on evaluating Vision-Language Models in embodied environments.
- ▪Two tasks, Num2Space and Space2Num, are formulated to assess the models' understanding of spatial numerical relationships.
- ▪Results show that VLMs largely fail to ground numbers in spatial meaning and rely on shallow spatial cues.
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Computer Science > Artificial Intelligence arXiv:2605.23898 (cs) [Submitted on 22 May 2026] Title:SPACENUM: Revisiting Spatial Numerical Understanding in VLMs Authors:Jianshu Zhang, Yijiang Li, Huifeixin Chen, Haoran Lu, Letian Xue, Bingyang Wang, Han Liu View a PDF of the paper titled SPACENUM: Revisiting Spatial Numerical Understanding in VLMs, by Jianshu Zhang and 6 other authors View PDF HTML (experimental) Abstract:Vision-Language Models (VLMs) are increasingly deployed in embodied environments, where they need produce numerical outputs such as action magnitudes and spatial coordinates. Although these numbers appear meaningful, it remains unclear whether these numerical outputs are genuinely grounded in spatial perception.
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