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

• URL: http://arxiv.org/abs/2603.18892v1
• Date: Thu, 19 Mar 2026 13:33:26 GMT
• Title: MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Model
• Abstract summary: We introduce MultihopSpatial, a benchmark for multi-hop and compositional spatial reasoning.<n>A complementary metric, Acc@50IoU, evaluates reasoning and visual grounding by requiring both answer selection and precise bounding box prediction.<n>A dedicated large-scale training corpus, MultihopSpatial-Train, fosters spatial intelligence.
• Score: 51.107890058344644
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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