Enhancing Spatial Reasoning in Large Vision-Language Models

Enhancing Spatial Reasoning in Large Vision-Language Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Spatial Reasoning in Large Vision-Language Models Shiquan Tao, Arthit Wongsawat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5547040/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this paper, we present a novel approach, Spatial-Aware Language Enhancement (SALE) , designed to improve the spatial reasoning capabilities of large language models (LLMs) using structured textual descriptions, without the need for visual inputs. Spatial reasoning is critical for understanding object relationships, navigation, and scene layouts in applications such as robotics and autonomous systems. Despite significant advancements in vision-language models (LVLMs), existing models still struggle with spatial tasks, especially when reliant solely on textual data. To address this, we propose a two-phase training strategy involving Spatial Description Pre-training (SDP) and fine-tuning on an enhanced benchmark dataset, SpatialEval+. Our experimental results demonstrate that SALE achieves state-of-the-art performance across various spatial tasks, outperforming existing models like GPT-4 and GPT-4+BLIP, while also being more resource-efficient. Further analysis, including ablation studies and human evaluation, confirms the effectiveness of our approach, indicating its potential for real-world applications where efficient and accurate spatial understanding is required. Artificial Intelligence and Machine Learning Large Vision-Language Models Natural Language processing Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5547040","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":384149040,"identity":"6059766d-e820-4370-a8a8-64c70ca0d73b","order_by":0,"name":"Shiquan Tao","email":"data:image/png;base64,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","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Shiquan","middleName":"","lastName":"Tao","suffix":""},{"id":384149041,"identity":"fd978345-895a-4d2b-9a87-a923d7199faf","order_by":1,"name":"Arthit Wongsawat","email":"","orcid":"","institution":"Rajamangala University of Technology Thanyaburi","correspondingAuthor":false,"prefix":"","firstName":"Arthit","middleName":"","lastName":"Wongsawat","suffix":""}],"badges":[],"createdAt":"2024-11-29 07:35:25","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5547040/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5547040/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70414562,"identity":"08e09eeb-1ef1-4c9b-b7ed-f0c10460c90a","added_by":"auto","created_at":"2024-12-03 02:40:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":167231,"visible":true,"origin":"","legend":"","description":"","filename":"SpatialLVLM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5547040/v1_covered_ee945750-37d7-4967-8fa1-21948746981b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEnhancing Spatial Reasoning in Large Vision-Language Models\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Henan Agricultural University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Large Vision-Language Models, Natural Language processing","lastPublishedDoi":"10.21203/rs.3.rs-5547040/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5547040/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, we present a novel approach, Spatial-Aware \u003cstrong\u003eLanguage Enhancement (SALE)\u003c/strong\u003e, designed to improve the spatial reasoning capabilities of large language models (LLMs) using structured textual descriptions, without the need for visual inputs. Spatial reasoning is critical for understanding object relationships, navigation, and scene layouts in applications such as robotics and autonomous systems. Despite significant advancements in vision-language models (LVLMs), existing models still struggle with spatial tasks, especially when reliant solely on textual data. To address this, we propose a two-phase training strategy involving Spatial Description Pre-training (SDP) and fine-tuning on an enhanced benchmark dataset, SpatialEval+. Our experimental results demonstrate that \u003cstrong\u003eSALE\u003c/strong\u003e achieves state-of-the-art performance across various spatial tasks, outperforming existing models like GPT-4 and GPT-4+BLIP, while also being more resource-efficient. 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