MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Model

Spatial reasoning is foundational for Vision-Language Mod-els (VLMs), particularly when deployed as Vision-Language-Action (VLA)agents in physical environments. However, existing benchmarks predomi-nantly focus on elementary, single-hop relations, neglecting the multi-hopcompositional reasoning and precise visual grounding essential for real-world scenarios. To address this, we introduce MultihopSpatial, o!er-ing three key contributions: (1) A comprehensive benchmark designedfor multi-hop and compositional spatial reasoning, featuring 1- to 3-hopcomplex queries across diverse spatial perspectives. (2) Acc@50IoU,a complementary metric that simultaneously evaluates reasoning andvisual grounding by requiring both answer selection and precise bound-ing box prediction—capabilities vital for robust VLA deployment. (3)MultihopSpatial-Train, a dedicated large-scale training corpus to fos-ter spatial intelligence. Extensive evaluation of 37 state-of-the-art VLMsyields eight key insights, revealing that compositional spatial reasoningremains a formidable challenge. Finally, we demonstrate that reinforce-ment learning post-training on our corpus enhances both intrinsic VLMspatial reasoning and downstream embodied manipulation performance.