Neural Synthesis through Probabilistic Layer Decomposition in Large Language Models

Neural Synthesis through Probabilistic Layer Decomposition 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 Neural Synthesis through Probabilistic Layer Decomposition in Large Language Models Phillip Beaumont, Genevieve Harrington, Rosalind Pemberton, Greta Johansson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5534735/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 Probabilistic Layer Decomposition (PLD) introduces a novel framework for enhancing the interpretability and computational efficiency of Large Language Models (LLMs). By assigning variable weights to each layer, PLD allows individual components of the architecture to contribute variably to the generation of linguistic representations. This probabilistic approach introduces uncertainty into layer interactions, which enhances the model’s ability to reconcile ambiguous or conflicting input data while maintaining computational coherence. The decomposition process relies on probabilistic matrix factorization techniques, partitioning the weight matrices into interpretable subcomponents, enabling the model to segment contextual understanding across hierarchical structures. Consequently, PLD offers a more transparent framework for understanding the internal mechanisms of LLMs, facilitating the identification of specific layers responsible for particular linguistic tasks. Experimental evaluations demonstrate that PLD models achieve convergence in fewer epochs across various tasks, suggesting improved training efficiency. Additionally, the probabilistic nature of PLD facilitates improved modularity within the model by separating deterministic operations from stochastic contributions. The proposed framework utilizes Markov Chain Monte Carlo sampling to optimize layer specific parameters, ensuring a balance between computational efficiency and representational fidelity. The incorporation of a hierarchical prior distribution allows the architecture to impose constraints on the layer activation probabilities, resulting in more structured and interpretable pathways for information flow. The integration of these probabilistic mechanisms establishes a foundation for reducing redundancies in layer operations while preserving the contextual depth required for high-performance linguistic tasks. Artificial Intelligence and Machine Learning probabilistic modeling Layer decomposition Computational efficiency Interpretability Linguistic reasoning Bayesian inference 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-5534735","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383331782,"identity":"3458b75d-f169-421e-b4e3-870f9f867b1b","order_by":0,"name":"Phillip Beaumont","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACCcbGAw8YDsC4NvxsUHEG9gacWhoOJMC1JKRJtrFBtfAcwKWFgQFZy2HJBqg1OLWYSzc3HEhsuyPHPyM78XHhj/MSfPLNzx7zMFjk8eBwmeWcgyAtz4wlbuRuNp6RcFuCjY3N3JiHQaKYB4c1BjcSQVoOJzbcyN0mzZNwu46NjcFMGqglcb9EAl4t9fMhWs4BbWH/BtbSQ0BLggFEywGgFh4zvFosZwC1JJx7ZrjxzNvNxjxpyUAtOWWScwyAWnD4xVwi/eGDD2V35OWO5258zGNjJyHffHybxJuKusQeHCFmACIY2bCLYwcQqT+4FYyCUTAKRsEoYAAAucFhcyGAI84AAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0007-0311-5216","institution":"Fitanu","correspondingAuthor":true,"prefix":"","firstName":"Phillip","middleName":"","lastName":"Beaumont","suffix":""},{"id":383331783,"identity":"24e05b36-79e9-44b6-ae64-dfd4ce45b8c7","order_by":1,"name":"Genevieve Harrington","email":"","orcid":"","institution":"Fitanu","correspondingAuthor":false,"prefix":"","firstName":"Genevieve","middleName":"","lastName":"Harrington","suffix":""},{"id":383331784,"identity":"6747e2c4-11ff-4b62-b92d-19ead8e1aed3","order_by":2,"name":"Rosalind Pemberton","email":"","orcid":"","institution":"Fitanu","correspondingAuthor":false,"prefix":"","firstName":"Rosalind","middleName":"","lastName":"Pemberton","suffix":""},{"id":383331785,"identity":"ca4dca88-b908-4f29-834d-2771601d4ef6","order_by":3,"name":"Greta Johansson","email":"","orcid":"","institution":"Fitanu","correspondingAuthor":false,"prefix":"","firstName":"Greta","middleName":"","lastName":"Johansson","suffix":""}],"badges":[],"createdAt":"2024-11-27 11:04:39","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-5534735/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5534735/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70058330,"identity":"198dfd03-438f-434c-a448-cf4173cb0a03","added_by":"auto","created_at":"2024-11-28 02:21:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":220040,"visible":true,"origin":"","legend":"","description":"","filename":"elsarticleLLM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5534735/v1_covered_919934b4-3f47-4c1e-845b-f27b16c3ba26.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eNeural Synthesis through Probabilistic Layer Decomposition 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":"probabilistic modeling, Layer decomposition, Computational efficiency, Interpretability, Linguistic reasoning, Bayesian inference","lastPublishedDoi":"10.21203/rs.3.rs-5534735/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5534735/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProbabilistic Layer Decomposition (PLD) introduces a novel framework for enhancing the interpretability and computational efficiency of Large Language Models (LLMs). 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