Adaptive Contextual Modulation for Token Prediction with Dynamic Semantic Weighting

Adaptive Contextual Modulation for Token Prediction with Dynamic Semantic Weighting | 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 Adaptive Contextual Modulation for Token Prediction with Dynamic Semantic Weighting Alexander Lefpar, Yorick Thackeray, David Miller, Montgomery Ellington, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5457893/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 increasing complexity and diversity of language tasks require models capable of maintaining contextual integrity over extended sequences. Addressing such challenges, Adaptive Contextual Modulation (ACM) introduces a dynamic mechanism that adjusts semantic weighting during token generation, thereby enhancing context preservation and prediction accuracy. Through integration with an open-source Large Language Model (LLM), ACM demonstrates significant improvements in token prediction accuracy, contextual coherence, and computational efficiency. Quantitative evaluations reveal that ACM-augmented models achieve higher BERTScores and Distinct-n metrics, indicating superior semantic alignment and response diversity. Additionally, the enhanced handling of ambiguous queries and long-form content generation underscores ACM's potential to advance LLM development and applications requiring high context fidelity. Artificial Intelligence and Machine Learning Adaptive Contextual Modulation semantic weighting token prediction contextual coherence Large Language Models natural language processing 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-5457893","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378418256,"identity":"4221915c-2167-446d-a654-4e8dc15cbadc","order_by":0,"name":"Alexander Lefpar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYDACdgaGD0BKDogZDyTYEKOFmYFxBpAyBrEPJKQZEK8lsQGkhYEYLfzNzAebC2rupc9vP2Nw4EHCHwaD8wfwa5E4zJbYPONYce6GM2kJBxISDBgMbiQQsOYwj/ljHraE3A0MyQcOJP4AaSGgQ/4w/8dmnn8J6fL9DxsgthBymMFhHsZm3raEBIYbQFvAWg4QcJjhYTbD5pl9CYYbbjwD+cWYR5KQX+SONz9sLviWIC/fn2P48EeCnBwfIYeBADMyh4ewenQto2AUjIJRMAowAAD950jkrwMdywAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0009-3484-8628","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Lefpar","suffix":""},{"id":378418257,"identity":"72f308b0-6735-4ad9-bd1b-986446f557f7","order_by":1,"name":"Yorick Thackeray","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yorick","middleName":"","lastName":"Thackeray","suffix":""},{"id":378418258,"identity":"648f6723-0b87-4528-8a5d-4f703ad8776b","order_by":2,"name":"David Miller","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Miller","suffix":""},{"id":378418259,"identity":"839893a2-3c9c-4c09-9829-8ef209aefd4a","order_by":3,"name":"Montgomery Ellington","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Montgomery","middleName":"","lastName":"Ellington","suffix":""},{"id":378418260,"identity":"16a8005a-cd1d-4949-ad07-baf75c127742","order_by":4,"name":"Kenneth Lee","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2024-11-15 05:55:08","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-5457893/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5457893/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69198173,"identity":"915aac0b-45b9-4187-ac5a-9c6ad2d58f87","added_by":"auto","created_at":"2024-11-18 02:05:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":164006,"visible":true,"origin":"","legend":"","description":"","filename":"Lefpar.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5457893/v1_covered_effd4b85-6c5d-4b49-8626-301d1bd9893a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAdaptive Contextual Modulation for Token Prediction with Dynamic Semantic Weighting\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":"Adaptive Contextual Modulation, semantic weighting, token prediction, contextual coherence, Large Language Models, natural language processing","lastPublishedDoi":"10.21203/rs.3.rs-5457893/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5457893/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing complexity and diversity of language tasks require models capable of maintaining contextual integrity over extended sequences. 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