Light-MLLMAD: A Lightweight Multimodal Large Language Model for One-Shot Industrial Visual Anomaly Detection

Light-MLLMAD: A Lightweight Multimodal Large Language Model for One-Shot Industrial Visual Anomaly Detection | 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 Article Light-MLLMAD: A Lightweight Multimodal Large Language Model for One-Shot Industrial Visual Anomaly Detection Augustian Isaac R, Sundaravadivel P, Vinoth kumar E.S, Priyanga G This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7853870/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 Industrial visual anomaly detection plays a pivotal role in ensuring product quality and operational safety across manufacturing, energy, and precision engineering sectors. However, most deep learning approaches rely on extensive defect datasets, making them unsuitable for real-world scenarios where only a single defective instance may be available. To address this challenge, this paper introduces Light-MLLMAD, a Lightweight Multimodal Large Language Model framework designed for one-shot industrial anomaly detection. The proposed model integrates a compact vision encoder with parameter-efficient adapter layers and a text-guided reasoning module, enabling efficient learning from minimal examples. By employing prompt-conditioned anomaly grounding, Light-MLLMAD leverages natural-language prompts to describe contextual attributes such as texture, color deviation, or surface irregularity, thus enhancing interpretability and localization accuracy. A contrastive embedding regularization strategy further ensures robust separation between normal and anomalous features even with limited samples. Extensive experiments conducted on benchmark datasets—covering metallic surfaces, printed circuit boards, and industrial components—demonstrate that Light-MLLMAD achieves superior detection accuracy while reducing computational cost by over 60% compared to traditional vision-language models. The system also achieves near real-time inference on edge hardware, confirming its deployability in factory settings. Overall, the proposed framework bridges the gap between multimodal reasoning and lightweight industrial implementation, offering an interpretable, resource-efficient, and scalable approach for one-shot visual anomaly detection. Physical sciences/Engineering Physical sciences/Mathematics and computing Industrial anomaly detection Multimodal large language models One-shot learning Lightweight architecture Vision-language integration Prompt-conditioned grounding Contrastive embedding Edge AI Visual inspection Manufacturing quality control Full Text Additional Declarations No competing interests reported. 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. 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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-7853870","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":529172475,"identity":"486643ff-eb5e-4aed-ace6-adfe7278d5b1","order_by":0,"name":"Augustian Isaac R","email":"","orcid":"","institution":"Saveetha Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Augustian","middleName":"Isaac","lastName":"R","suffix":""},{"id":529172476,"identity":"07292ac5-0026-40ac-8999-89607df23821","order_by":1,"name":"Sundaravadivel 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