Dynamic Contextual Neural Architectures for Adaptive Token Prediction in Large Language Models

Dynamic Contextual Neural Architectures for Adaptive Token Prediction in Large 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 Dynamic Contextual Neural Architectures for Adaptive Token Prediction in Large Language Models Yosef Margarita, Nathaniel Beaumont, Paul Thompson, Robert Aldridge, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5480098/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 The Dynamic Contextual Neural Architecture (DCNA) introduces a paradigm shift in language modeling through its adaptive mechanisms that align computational processes with the contextual complexities of input data. Empirical evaluations reveal that DCNA achieves a reduction in perplexity scores across diverse datasets, indicating enhanced predictive accuracy. Additionally, the model demonstrates decreased inference times and memory utilization, showing its computational efficiency. Notably, DCNA exhibits robustness to noisy inputs, maintaining consistent performance across varying noise levels, and showcases scalability across different hardware platforms, highlighting its versatility. These findings suggest that DCNA effectively addresses limitations inherent in traditional static models, offering a responsive and context-aware solution for advanced language processing tasks. Dynamic Contextual Neural Architecture adaptive mechanisms predictive accuracy computational efficiency robustness scalability 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-5480098","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379777088,"identity":"7aee75c7-7078-4add-bdb2-857cffd9801c","order_by":0,"name":"Yosef Margarita","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBADHgYG5gMSQIKZeC08DGwJpGkBWsNjANJCWKXBtcPPPn6puSdjz37m440PFdbsDOyHj27Aq+V2mvFsmWPFPDw8uZstZ5xJZ2bgSUu7gV9LgjGzBFsC0C+526R52w4zM0jwmBHQkv6ZWeIfUAv/m2fEaskxZvzYBtQikcNGnBbJ2znFzIx9QC03nhmD/cJGyC98t9M3M/74lmDP3p/8EBRiyfzsh4/h1aJwABgXPEgCyWz4lIOAfAMDA+MPJAE7QjpGwSgYBaNg5AEAmh5ECsy05CwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0005-8956-1211","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Yosef","middleName":"","lastName":"Margarita","suffix":""},{"id":379777089,"identity":"ba3da34d-86af-4d6c-aa03-54dcc5e2f4e2","order_by":1,"name":"Nathaniel Beaumont","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nathaniel","middleName":"","lastName":"Beaumont","suffix":""},{"id":379777090,"identity":"64807088-b46a-4a9c-9987-f7f9f9a1aaa9","order_by":2,"name":"Paul Thompson","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Thompson","suffix":""},{"id":379777091,"identity":"a8328c15-b4fd-4087-833e-7891089e4aa7","order_by":3,"name":"Robert Aldridge","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Aldridge","suffix":""},{"id":379777092,"identity":"1a6e02eb-d30a-48e9-9df6-3a9691a2bdf9","order_by":4,"name":"Anthony White","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"","lastName":"White","suffix":""}],"badges":[],"createdAt":"2024-11-19 04:48:51","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-5480098/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5480098/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69540653,"identity":"d5d80c3c-ffd8-497e-a483-27538741abf0","added_by":"auto","created_at":"2024-11-21 12:45:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":178432,"visible":true,"origin":"","legend":"","description":"","filename":"RSarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5480098/v1_covered_8e14b102-9de7-423a-b57e-59e41bed108e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDynamic Contextual Neural Architectures for Adaptive Token Prediction in Large Language Models\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":"Dynamic Contextual Neural Architecture, adaptive mechanisms, predictive accuracy, computational efficiency, robustness, scalability","lastPublishedDoi":"10.21203/rs.3.rs-5480098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5480098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Dynamic Contextual Neural Architecture (DCNA) introduces a paradigm shift in language modeling through its adaptive mechanisms that align computational processes with the contextual complexities of input data. 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