Spatial reasoning is foundational for Vision-Language Models (VLMs), particularly when deployed as Vision-Language-Action (VLA) agents in physical environments. However, existing benchmarks predominantly focus on elementary, single-hop relations, neglecting the multi-hop compositional reasoning and precise visual grounding essential for real-world scenarios. To address this, we introduce MultihopSpatial, offering three key contributions: (1) A comprehensive benchmark designed for multi-hop and compositional spatial reasoning, featuring 1- to 3-hop complex queries across diverse spatial perspectives. (2) Acc@50IoU, a complementary metric that simultaneously evaluates reasoning and visual grounding by requiring both answer selection and precise bounding box prediction - capabilities vital for robust VLA deployment. (3) MultihopSpatial-Train, a dedicated large-scale training corpus to foster spatial intelligence. Extensive evaluation of 37 state-of-the-art VLMs yields eight key insights, revealing that compositional spatial reasoning remains a formidable challenge. Finally, we demonstrate that reinforcement learning post-training on our corpus enhances both intrinsic VLM spatial reasoning and downstream embodied manipulation performance.

翻译：空间推理是视觉-语言模型（VLM）的基础能力，尤其在将模型部署为物理环境中的视觉-语言-动作（VLA）智能体时更为关键。然而，现有基准主要聚焦于基础的单跳关系，忽视了现实场景中必备的多跳组合推理与精细视觉定位能力。为此，我们提出MultihopSpatial，贡献如下：（1）面向多跳与组合空间推理的综合基准，包含跨越不同空间视角的1至3跳复杂查询；（2）Acc@50IoU互补评估指标，通过同时要求答案选择与精准边界框预测来综合评价推理与视觉定位能力——这对稳健的VLA部署至关重要；（3）MultihopSpatial-Train专用大规模训练语料库，旨在增强空间智能。对37个前沿VLM的全面评估揭示了八项关键发现，表明组合空间推理仍是重大挑战。最后，我们证明基于该语料库的强化学习后训练可同时增强VLM的内在空间推理能力与下游具身操作性能。