Transfer Learning in Agentic Systems: Improving Cross-Task Knowledge Application in AI Agents

Transfer Learning in Agentic Systems: Improving Cross-Task Knowledge Application in AI Agents | 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 Transfer Learning in Agentic Systems: Improving Cross-Task Knowledge Application in AI Agents Yu Nong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5911274/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 This paper presents a novel framework for enhancing transfer learning capabilities in artificial intelligence agents, focusing on the crucial challenge of knowledge generalization across diverse tasks. We introduce the Adaptive Knowledge Transfer Network (AKTN), a hierarchical architecture that enables AI agents to decompose learned behaviors into fundamental cognitive primitives and recombine them for novel task execution. Our research demonstrates significant improvements in cross-domain knowledge application, reducing the learning curve for new tasks by up to 73\% compared to traditional approaches. Artificial Intelligence and Machine Learning Agentic AI Transfer Learning NN Adaptive Knowledge Transfer Network (AKTN) 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. 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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-5911274","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":407934115,"identity":"b4aa105f-98b4-4ce1-9972-131367df9583","order_by":0,"name":"Yu Nong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBADOQZmMG3BwAamC5gJajEGamFsYGCQgGoxIKwlEagcooWBkBaDGwlsDz7uqE3fcJz3+WOeGgl5PgYew8cVBtb4tLAbzjxzPHfDYXbDZp5jEoZtDDzGhmcM0vHaIs3bdix3ZjMbYzMPmwRjGwPvNskGg8MEtaRLgrX8k7AnVktNAj8zUAtvm0QiQS2SZx62Sc5sO2DYD9Qyc26fRHIbM/9nwwY8fuE7nnxM4mNbnTwb/zGGD2++2djOb29LfNhQgTvEFA6AYoMB2RmEIlG+AUzVEVA2CkbBKBgFIxoAAFCASkBMPJDYAAAAAElFTkSuQmCC","orcid":"","institution":"VirtualAI China","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Nong","suffix":""}],"badges":[],"createdAt":"2025-01-27 10:06:56","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-5911274/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5911274/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78932844,"identity":"509d973d-ad24-4e04-9f43-4768280dafa9","added_by":"auto","created_at":"2025-03-21 03:41:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":301193,"visible":true,"origin":"","legend":"","description":"","filename":"transferagenticAI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5911274/v1_covered_cc5faaae-daca-4d6b-9fb3-418644d5fb36.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eTransfer Learning in Agentic Systems: Improving Cross-Task Knowledge Application in AI Agents\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"VirtualAI China","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":"Agentic AI, Transfer Learning, NN, Adaptive Knowledge Transfer Network (AKTN) ","lastPublishedDoi":"10.21203/rs.3.rs-5911274/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5911274/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a novel framework for enhancing transfer learning capabilities in artificial intelligence agents, focusing on the crucial challenge of knowledge generalization across diverse tasks. 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