README.md

metadata

license: apache-2.0
task_categories:
  - image-text-to-text
tags:
  - vlm
  - spatial-reasoning

SpaceNum: Revisiting Spatial Numerical Understanding in VLMs

Project Page | Paper

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.