A Novel Approach to Autonomous Language Model Contextualization Through Dynamic Knowledge Frames

A Novel Approach to Autonomous Language Model Contextualization Through Dynamic Knowledge Frames | 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 A Novel Approach to Autonomous Language Model Contextualization Through Dynamic Knowledge Frames Ryuji Soikao, Hiroki Chikafusa, Kiyomasa Sugino, Daiki Kuromaru, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5425698/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 Maintaining contextual coherence over extended sequences has long challenged language generation models. Addressing this issue, Dynamic Knowledge Frames (DKFs) have been introduced as an innovative framework that modularizes and adapts knowledge representation, thereby enhancing the contextual adaptability of large language models (LLMs). DKFs enable LLMs to dynamically adjust their focus in response to evolving contextual demands, facilitating more coherent and contextually pertinent language generation. Empirical evaluations demonstrate that DKF-augmented models surpass traditional context-handling methodologies, such as sliding windows and fixed attention spans, in maintaining contextual integrity over extended sequences. This advancement signifies a substantial progression in the development of LLMs capable of dynamic context management, addressing a critical limitation inherent in conventional models. The integration of DKFs into LLMs not only improves performance metrics, including perplexity reduction and elevated BLEU and ROUGE scores, but also positions DKFs as a promising framework for future research endeavors aimed at developing LLMs with advanced contextual understanding and reasoning capabilities. contextual coherence knowledge representation language generation modularization adaptability 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. 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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-5425698","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376227627,"identity":"bc672399-4bae-4bec-80e9-3b8e7cbc7e8e","order_by":0,"name":"Ryuji Soikao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYFACxmYgYcPAxwxkNhgwE60ljYGNmYGxkUgtDCBVhxnYGEBaGIjQIt/e3Gzwo+K8HBs78/GHMwqs7RnYDx/dgE+LwZmDzYk9Z24bszGzJTZuMEhPbOBJS7uBV4tEYvMB3rbbiW3MPIaNDwwOJzBI8Jjh1SI/I7H54N+2c/UwLfYEtTDcSGxO5m07kMAG0rLB4DBjAyEtIL8Yy5xJNmwD+mXmDKBf2gj5Rb69/bHkmwo7eX7+wwc+9vyxtudnP3wMv8MwABtpykfBKBgFo2AUYAMAt/ZHR1hLJEcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0001-8994-6593","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ryuji","middleName":"","lastName":"Soikao","suffix":""},{"id":376227628,"identity":"70967055-7472-4175-970c-d93c95efa81c","order_by":1,"name":"Hiroki Chikafusa","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hiroki","middleName":"","lastName":"Chikafusa","suffix":""},{"id":376227629,"identity":"31c60d9b-b35c-496d-b3bc-2da12194b684","order_by":2,"name":"Kiyomasa Sugino","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kiyomasa","middleName":"","lastName":"Sugino","suffix":""},{"id":376227630,"identity":"158a1749-15eb-427b-9d88-048315108d1c","order_by":3,"name":"Daiki Kuromaru","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Daiki","middleName":"","lastName":"Kuromaru","suffix":""},{"id":376227631,"identity":"3792c057-fc6a-4cc9-acc5-c30c36204ab8","order_by":4,"name":"Kazunari Osanai","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kazunari","middleName":"","lastName":"Osanai","suffix":""}],"badges":[],"createdAt":"2024-11-10 11:42:56","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-5425698/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5425698/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68777928,"identity":"66458721-1ede-4a9f-8a70-393ef3ddf8a8","added_by":"auto","created_at":"2024-11-12 02:04:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":193922,"visible":true,"origin":"","legend":"","description":"","filename":"RyujiSoikao.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5425698/v1_covered_8cbaa186-3888-4535-82a8-0dce752e5dd6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Novel Approach to Autonomous Language Model Contextualization Through Dynamic Knowledge Frames\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"contextual coherence, knowledge representation, language generation, modularization, adaptability","lastPublishedDoi":"10.21203/rs.3.rs-5425698/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5425698/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMaintaining contextual coherence over extended sequences has long challenged language generation models. 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