A Novel Framework for Adaptive Neural Contextual Modulation in Large Language Models

A Novel Framework for Adaptive Neural Contextual Modulation 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 A Novel Framework for Adaptive Neural Contextual Modulation in Large Language Models Hiroto Higasigi, Tatsuya Nagamori, Takumi Kuroyanagi, Ryunosuke Toyokawa, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5465145/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 Modern artificial intelligence systems have faced persistent challenges in adapting dynamically to evolving linguistic contexts, particularly in tasks requiring precision and domain-specific comprehension. Adaptive Neural Contextual Modulation represents a novel framework designed to overcome such limitations through the introduction of auxiliary neural pathways that dynamically recalibrate attention mechanisms to maintain contextual coherence and semantic alignment. The approach integrates seamlessly into transformer architectures, achieving enhanced adaptability without significant computational trade-offs. Rigorous experimental validation demonstrates its superiority across benchmarks, including improved performance in cross-domain generalization, contextual reconfigurability, and long-form text coherence. These results suggest the potential of the proposed framework to redefine the adaptability of language models, paving the way for their application in increasingly complex and specialized scenarios. Contextual Adaptation Attention Modulation Transformer Architectures Neural Pathways Semantic Coherence Domain Generalization 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-5465145","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378849553,"identity":"63bd5790-c7da-4ed9-af6f-022205a2b316","order_by":0,"name":"Hiroto 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