It's All Connected: A Survey for Multimodal Arabic AI

It's All Connected: A Survey for Multimodal Arabic AI | 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 It's All Connected: A Survey for Multimodal Arabic AI Farizeh Aldabbas, Hossam Elsafty, Rafet Sifa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8007923/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Multimodal AI integrates text, vision, and speech within unified reasoning frameworks, yet Arabic remains significantly underrepresented due to diglossia, morphological complexity, and scarce multimodal resources. This survey delivers the first comprehensive technical roadmap for Arabic multimodal AI, covering the progression from unimodal Arabic NLP, OCR, and ASR to recent Arabic-capable Multimodal Large Language Models (MLLMs). We review available multimodal datasets, modality encoders, tokenization approaches, connector designs, and fusion strategies used in state-of-the-art systems. We also provide the first consolidated evaluation of Arabic-capable MLLMs on multimodal benchmarks ARB and PEARL analyzing performance, robustness, and domain generalization across OCR-grounded and open-domain VQA settings. Despite recent progress, challenges persist in cultural grounding, dialect inclusivity, dataset scale, and open-access ecosystem maturity. We outline actionable directions for scalable and culturally aligned Arabic multimodal intelligence, including parameter-efficient adaptation, broader corpus development, and unified evaluation protocols. By consolidating technical advances and empirical insights, this survey establishes a foundation to guide the next generation of Arabic-centric multimodal research. Arabic multimodal modality fusion dialectal variation low-resource languages Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Jan, 2026 Reviews received at journal 03 Jan, 2026 Reviews received at journal 23 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviews received at journal 07 Dec, 2025 Reviews received at journal 15 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers invited by journal 10 Nov, 2025 Editor assigned by journal 10 Nov, 2025 Submission checks completed at journal 04 Nov, 2025 First submitted to journal 01 Nov, 2025 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. 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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-8007923","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545452864,"identity":"36966aa6-35ae-4c48-b213-eb140e43c7c5","order_by":0,"name":"Farizeh Aldabbas","email":"","orcid":"","institution":"Fraunhofer Institute for Intelligent Analysis and Information Systems","correspondingAuthor":false,"prefix":"","firstName":"Farizeh","middleName":"","lastName":"Aldabbas","suffix":""},{"id":545452868,"identity":"1b749118-297d-4e1c-8462-e6f182e81fbe","order_by":1,"name":"Hossam 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