PolyComp: A Polycube-based Benchmark for Compositional 3D Spatial Reasoning in Multimodal Models

[2608.14741] PolyComp: A Polycube-based Benchmark for Compositional 3D Spatial Reasoning in Multimodal Models 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:2608.14741 (cs) [Submitted on 13 Aug 2026 ( v1 ), last revised 27 Aug 2026 (this version, v2)] Title: PolyComp: A Polycube-based Benchmark for Compositional 3D Spatial Reasoning in Multimodal Models Authors: Siddharth Patel View a PDF of the paper titled PolyComp: A Polycube-based Benchmark for Compositional 3D Spatial Reasoning in Multimodal Models, by Siddharth Patel View PDF HTML (experimental) Abstract: We introduce PolyComp, a procedurally generated and verified benchmark that stresses visual recognition and compositional spatial reasoning. In each problem, a model must identify which of four options shows a pair of polycube components that can be combined to form a target solid. The benchmark contains 120 problems across four geometry families, and each problem has three different presentation formats using either a single image or multiple images. The random guessing baseline is 25%. Across the three presentations (360 presented problems per model), GPT-5.6 Sol with max effort attains 50.0% accuracy (95% problem-cluster CI 43.3-56.7%) at a mean cost of \$0.951 per presented problem, Claude Fable 5 with max effort attains 39.4% (33.1-46.1%) at \$0.701, and Gemini 3.1 Pro Preview with thinking level high attains 27.5% (22.8-32.5%), near the 25% random guessing baseline, at \$0.350. The observed accuracy spread across geometry families is larger than across presentation formats. We present a problem development and evaluation protocol, cost and token accounting, and release the 120 problems. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14741 [cs.CV] (or arXiv:2608.14741v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14741 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Siddharth Patel [ view email ] [v1] Thu, 13 Aug 2026 20:19:34 UTC (557 KB) [v2] Thu, 27 Aug 2026 21:51:02 UTC (557 KB) Full-text links: Access Paper: View a PDF of the paper titled PolyComp: A Polycube-based Benchmark for Compositional 3D Spatial Reasoning in Multimodal Models, by Siddharth Patel View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI 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

• HARMONY: Hierarchical Agentic Reasoning for MONocular Image-to-Scene Synthesis#1081342
• HARMONY: Hierarchical Agentic Reasoning for MONocular Image-to-Scene Synthesis#1062717
• Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models#471275
• WorldSculpt: Generating Compositional Worlds from Grounded Videos#974645
• Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs#31317