ProtoMind: Modeling Driven NAS and SIP Message Sequence Modeling for Smart Regression Detection

ProtoMind: Modeling Driven NAS and SIP Message Sequence Modeling for Smart Regression Detection | 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 ProtoMind: Modeling Driven NAS and SIP Message Sequence Modeling for Smart Regression Detection Tongwei Tu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6866907/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 Large language models (LLMs) face significant challenges in effectively detecting regressions in software systems, particularly in the context of dynamic environments. The need for an efficient framework that can adaptively learn and model architectural and sequential data is evident. We present ProtoMind, a unique approach that combines Modeling Driven Network Attached Storage (NAS) with Session Initiation Protocol (SIP) message sequence modeling to enhance regression detection. The NAS component autonomously discovers the most effective neural architectures specifically tailored to regression tasks, leading to notable improvements in performance. Concurrently, the SIP message sequence modeling captures complex temporal patterns and interactions, facilitating a more accurate identification of regression faults. Experiments validate that ProtoMind surpasses conventional methods in regression detection, exhibiting enhanced accuracy and expedited processing times. Computer Architecture and Engineering Smart Regression Detection Message Sequence Modeling Network License Protocol 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-6866907","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469502852,"identity":"d2b97514-203e-49bf-8ac8-1f8962268eed","order_by":0,"name":"Tongwei Tu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYLCCBAYJOXmGwweATAkZgqp52MBaLIwNG48lgLTwEKeFgaEiseHwGQOwAEEt9vI9hg8eMEgwNrad+fzqRo0FDwP74aMb8NvCY2wA9AszO8/ZbdY5x4AO40lLu4FfC+82CaAWNsYZZ7cZ57ABtUjwmBHSsv1HAsjX9988M875R5yWbaBAlmA4cIb5cW4bMVqO5X+WSDCQMDBsOGbGnNsnAfQcAb+wNx9L/Pijoq5+PsPhx59zvtXJ8bMfPoZXCwSAY4SBTQJMElaOAMwfSFE9CkbBKBgFIwcAAHgWQg6+jz8fAAAAAElFTkSuQmCC","orcid":"","institution":"Embry-Riddle Aeronautical University","correspondingAuthor":true,"prefix":"","firstName":"Tongwei","middleName":"","lastName":"Tu","suffix":""}],"badges":[],"createdAt":"2025-06-11 01:49:55","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-6866907/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6866907/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84434941,"identity":"2d4d9d4f-1fb0-431c-8a15-235cb7323ea3","added_by":"auto","created_at":"2025-06-12 01:58:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":632213,"visible":true,"origin":"","legend":"","description":"","filename":"ProtoMindModelingDrivenNASandSIPMessageSequenceModelingforSmartRegressionDetection7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6866907/v1_covered_a8cc2451-2d90-452d-a573-be2ef9c74aad.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eProtoMind: Modeling Driven NAS and SIP Message Sequence Modeling for Smart Regression Detection\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Embry-Riddle Aeronautical University","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":"Smart Regression Detection, Message Sequence Modeling, Network License Protocol","lastPublishedDoi":"10.21203/rs.3.rs-6866907/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6866907/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLarge language models (LLMs) face significant challenges in effectively detecting regressions in software systems, particularly in the context of dynamic environments. 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