TraceLM: Temporal Root-Cause Analysis with Contextual Embedding Language Models

TraceLM: Temporal Root-Cause Analysis with Contextual Embedding 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 TraceLM: Temporal Root-Cause Analysis with Contextual Embedding Language Models Bingxin Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6727010/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 Temporal analysis represents a crucial challenge in understanding event sequences, particularly in identifying root causes. Traditional methods often rely on static feature extraction, limiting their effectiveness in dynamic contexts. To tackle this issue, we introduce TraceLM, a framework that employs contextual embedding language models to enhance temporal root-cause analysis. By incorporating advanced representation learning techniques, TraceLM captures the temporal dynamics and relationships inherent in data. The multi-layered architecture allows for the identification of significant patterns indicative of root causes by processing sequences dynamically. Evaluations on benchmark datasets reveal that TraceLM outperforms existing methods in both accuracy and efficiency, demonstrating its strength in uncovering complex causative relationships. This capability provides actionable insights relevant to real-world domains such as system diagnostics and incident management, thus significantly advancing the field of root-cause analysis. Computer Architecture and Engineering Error Analysis Contextual Inference 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-6727010","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":460578423,"identity":"2d64785d-3a47-4141-b5d8-439034805b6c","order_by":0,"name":"Bingxin Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACA2YwySAH5j0AEQeI1GIM5iUQpQVKJzYQrcWcnffwa56CO+nzHXgMPyTUMMjx3UjAr8WymS/NcobBs9yNB3iMJRKOMRhLEtJicJjHzOCDweHcjQ08ZgwJbAyJG4jSkmBwON0QrOUfQz0xWowfAG1JkGcAaklsY0gwIOwXHjPGGQaHDTcwsxVLJPZJGM488wC/FnP+M8afef4clpdvb9744cM3G3m+4wRsAQI2CYgLwRwJgspBgPkDiJRvIErxKBgFo2AUjEQAAOoeQa+6DJ8LAAAAAElFTkSuQmCC","orcid":"","institution":"University of California, Los Angeles","correspondingAuthor":true,"prefix":"","firstName":"Bingxin","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-05-22 17:23:03","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-6727010/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6727010/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83425695,"identity":"8a7ea8c2-b7ab-4f5d-b731-8ae3a28aadb3","added_by":"auto","created_at":"2025-05-26 04:47:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":480313,"visible":true,"origin":"","legend":"","description":"","filename":"0f3b6983544b483fa8ff69beb007452c1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6727010/v1_covered_87b10705-e114-481b-9d87-48aab5322b0f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eTraceLM: Temporal Root-Cause Analysis with Contextual Embedding Language Models\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of California, Los Angeles","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":"Error Analysis, Contextual Inference, Language Models","lastPublishedDoi":"10.21203/rs.3.rs-6727010/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6727010/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTemporal analysis represents a crucial challenge in understanding event sequences, particularly in identifying root causes. 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