Mind the Spatial Gap: A Benchmark for Geospatial Reasoning and Constrained Spatial Editing
Understanding and manipulating spatial relations is fundamental to AI systems that reason about physical environments and construct grounded world models. However, existing evaluations of multimodal large language models (MLLMs) for spatial reasoning rely predominantly on static question-answering (QA) over small-scale or synthetic scenes, rarely probing whether models can modify spatial configurations while satisfying explicit geometric constraints. We introduce GeoSpatialBench, a benchmark designed to evaluate an escalating progression of spatial cognition, ranging from spatial relation understanding and geometric reasoning to constrained spatial editing. Grounded in real-world urban da...
| Authors | Zheng Ren, Yanning Wang, Ziheng Huang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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