Innovative Algorithmic Mechanism for Knowledge Compression and Retrieval with Novel Self-Referential Vector Processing

Innovative Algorithmic Mechanism for Knowledge Compression and Retrieval with Novel Self-Referential Vector Processing | 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 Innovative Algorithmic Mechanism for Knowledge Compression and Retrieval with Novel Self-Referential Vector Processing Marian Nademort, Peter Simonsen, Frank Bianchi, Martin Schultz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5434644/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 exponential growth of data and the increasing complexity of information necessitate innovative approaches to knowledge representation and retrieval. Addressing this challenge, Self-Referential Vector Processing (SRVP) introduces a dynamic, self-referential mechanism within Large Language Models (LLMs), enabling adaptive restructuring of knowledge bases in response to contextual demands. The theoretical framework of SRVP, grounded in hyperdimensional computing and vector symbolic architectures, facilitates the encoding of complex data structures and relationships. Experimental implementation of SRVP in an open-source LLM demonstrated significant reductions in memory footprint, enhancements in retrieval accuracy, and improvements in processing speed, showing its potential to advance the efficiency and effectiveness of LLMs. These findings suggest that SRVP offers a transformative approach to knowledge compression and retrieval, with broad implications for the development of more sophisticated artificial intelligence systems. Self-Referential Vector Processing knowledge compression retrieval accuracy hyperdimensional computing vector symbolic architectures Large Language Models 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-5434644","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376855104,"identity":"839ab7e3-5380-49a5-89f1-62c0658727ab","order_by":0,"name":"Marian Nademort","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0006-6141-8971","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Marian","middleName":"","lastName":"Nademort","suffix":""},{"id":376855105,"identity":"f11a3ca4-6cce-4a68-9615-290a4511eff2","order_by":1,"name":"Peter Simonsen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Simonsen","suffix":""},{"id":376855106,"identity":"3e28ad59-43fb-4986-bcbb-365751512e30","order_by":2,"name":"Frank Bianchi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"","lastName":"Bianchi","suffix":""},{"id":376855107,"identity":"3a23409b-5280-4d97-8a04-de495b0852fd","order_by":3,"name":"Martin Schultz","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Schultz","suffix":""}],"badges":[],"createdAt":"2024-11-11 21:29:30","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-5434644/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5434644/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68777814,"identity":"6619f675-06cd-439a-be97-23c746e7f7ea","added_by":"auto","created_at":"2024-11-12 01:58:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":192716,"visible":true,"origin":"","legend":"","description":"","filename":"33fd57f0da0a42cca0b0b8372a4f91bf.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5434644/v1_covered_1d97d418-605d-470a-8057-fa5a1bb11974.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eInnovative Algorithmic Mechanism for Knowledge Compression and Retrieval with Novel Self-Referential Vector Processing\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":"Self-Referential Vector Processing, knowledge compression, retrieval accuracy, hyperdimensional computing, vector symbolic architectures, Large Language Models","lastPublishedDoi":"10.21203/rs.3.rs-5434644/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5434644/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe exponential growth of data and the increasing complexity of information necessitate innovative approaches to knowledge representation and retrieval. 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