MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Model
#MultihopSpatial #vision-language model #spatial reasoning #multi-hop #compositional reasoning #benchmark #AI evaluation
📌 Key Takeaways
- MultihopSpatial is a new benchmark for evaluating vision-language models on spatial reasoning tasks.
- It focuses on multi-hop compositional reasoning, requiring models to combine multiple spatial concepts.
- The benchmark aims to assess advanced capabilities beyond basic visual recognition in AI systems.
- It addresses gaps in current evaluations by emphasizing complex, step-by-step spatial understanding.
📖 Full Retelling
arXiv:2603.18892v1 Announce Type: cross
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 c
🏷️ Themes
AI Benchmarking, Spatial Reasoning
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Language model
Statistical model of language
A language model is a computational model that predicts sequences in natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimizati...
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--> Computer Science > Computer Vision and Pattern Recognition arXiv:2603.18892 [Submitted on 19 Mar 2026] Title: MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Model Authors: Youngwan Lee , Soojin Jang , Yoorhim Cho , Seunghwan Lee , Yong-Ju Lee , Sung Ju Hwang View a PDF of the paper titled MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Model, by Youngwan Lee and 5 other authors View PDF HTML Abstract: Spatial reasoning is foundational for Vision-Language Models , particularly when deployed as Vision-Language-Action 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. Comments: Project page: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.18892 [cs.CV] (or arXiv:2603.18892v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.18892 Focus to ...
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