---
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 Vision-Language Models (VLMs) on their ability to ground numerical outputs in spatial perception. It addresses two complementary settings:
1. **Dynamic transitions** during spatial exploration.
2. **Static layouts** in spatial reasoning.

## Tasks
The benchmark formulates two bidirectional tasks to evaluate the mapping between vision-side spatial structure and language-side numerical representations:
- **Num2Space**: Evaluating how models map numerical representations to spatial structures.
- **Space2Num**: Evaluating how models extract numerical values from visual spatial observations.

SpaceNum systematically studies whether current VLMs truly understand numerical values in spatial settings or rely on shallow spatial cues.