SIRI-Bench: Challenging VLMs' Spatial Intelligence through Complex Reasoning Tasks

• URL: http://arxiv.org/abs/2506.14512v3
• Date: Fri, 17 Oct 2025 02:36:30 GMT
• Title: SIRI-Bench: Challenging VLMs' Spatial Intelligence through Complex Reasoning Tasks
• Authors: Zijian Song, Xiaoxin Lin, Qiuming Huang, Guangrun Wang, Liang Lin,
• Abstract summary: We introduce SIRI-Bench, a benchmark designed to evaluate Vision-Language Models' structural spatial intelligence.<n>Bench comprises 9,000 video-question-answer triplets, where each problem is embedded in a realistic 3D scene.<n> Experimental results reveal that state-of-the-art VLMs struggle significantly on SIRI-Bench, underscoring the challenge of structural spatial reasoning.
• Score: 51.774165536666864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have undergone rapid progress, largely attributed to reinforcement learning on complex reasoning tasks. In contrast, while spatial intelligence is fundamental for Vision-Language Models (VLMs) in real-world interaction, the systematic study of their complex spatial reasoning remains underexplored. To bridge this gap, we introduce SIRI-Bench, a benchmark designed to evaluate VLMs' structural spatial intelligence through spatial-grounded reasoning tasks. SIRI-Bench comprises 9,000 video-question-answer triplets, where each problem is embedded in a realistic 3D scene. The benchmark is carefully designed so that solving each problem requires both spatial comprehension and structural reasoning. To facilitate large-scale data synthesis, we develop an Automatic Scene Creation Engine that employs collaborative LLM agents to translate abstract mathematical problems into faithful 3D scenes. Experimental results reveal that state-of-the-art VLMs struggle significantly on SIRI-Bench, underscoring the challenge of structural spatial reasoning. We hope that our study will bring researchers' attention to spatially grounded reasoning and advance VLMs in visual problem-solving.

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