SPACENUM: Revisiting Spatial Numerical Understanding in VLMs
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. Therefore, in this work, we revisit spatial numerical understanding through SpaceNum, a unified framework that captures two complementary settings: numbers as dynamic transitions during spatial exploration, and numbers as static layouts in spatial reasoning. We formulate two bidirectional tasks, Num2Space and Space2Num, to evaluate how well VLMs map between vision-side spatial str...
| Authors | Jianshu Zhang, Yijiang Li, Huifeixin Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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