| license: apache-2.0 | |
| task_categories: | |
| - image-text-to-text | |
| tags: | |
| - vlm | |
| - spatial-reasoning | |
| # SpaceNum: Revisiting Spatial Numerical Understanding in VLMs | |
| [Project Page](https://sterzhang.github.io/SpaceNum-Home/) | [Paper](https://huggingface.co/papers/2605.23898) | |
| SpaceNum is a unified framework designed to evaluate how well Vision-Language Models (VLMs) ground numerical outputs in spatial perception. It covers two complementary settings: | |
| 1. **Numbers as dynamic transitions** during spatial exploration. | |
| 2. **Numbers as static layouts** in spatial reasoning. | |
| The benchmark formulates two bidirectional tasks, **Num2Space** and **Space2Num**, to assess the mapping between vision-side spatial structures and language-side numerical representations. SpaceNum aims to determine if VLM numerical outputs are truly grounded in spatial perception or if models rely on shallow spatial cues. |