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

[2506.14512] SIRI-Bench: Challenging VLMs' Spatial Intelligence through Complex Reasoning Tasks Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2506.14512 (cs) [Submitted on 17 Jun 2025 ( v1 ), last revised 14 Apr 2026 (this version, v4)] Title: SIRI-Bench: Challenging VLMs' Spatial Intelligence through Complex Reasoning Tasks Authors: Zijian Song , Xiaoxin Lin , Qiuming Huang , Sihan Qin , Guangrun Wang , Liang Lin View a PDF of the paper titled SIRI-Bench: Challenging VLMs' Spatial Intelligence through Complex Reasoning Tasks, by Zijian Song and 5 other authors View PDF HTML (experimental) 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. Comments: 20 pages, 11 figures Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2506.14512 [cs.CV] (or arXiv:2506.14512v4 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2506.14512 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zijian Song [ view email ] [v1] Tue, 17 Jun 2025 13:40:00 UTC (1,276 KB) [v2] Mon, 6 Oct 2025 04:31:42 UTC (2,026 KB) [v3] Fri, 17 Oct 2025 02:36:30 UTC (2,047 KB) [v4] Tue, 14 Apr 2026 07:32:54 UTC (2,047 KB) Full-text links: Access Paper: View a PDF of the paper titled SIRI-Bench: Challenging VLMs' Spatial Intelligence through Complex Reasoning Tasks, by Zijian Song and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• ReVSI: Rebuilding Visual Spatial Intelligence Evaluation for Accurate Assessment of VLM 3D Reasoning#141783
• Rethinking the Simulation vs. Rendering Dichotomy: No Free Lunch in Spatial World Modelling#17831
• OneCanvas: 3D Scene Understanding via Panoramic Reprojection#1118611
• VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning#999858
• TriViewBench: Controlled Complexity Scaling for Multi-View Structural Reasoning in MLLMs#307047