Research on adaptive course learning algorithmbased on reinforced feedback in English translationmodel optimization

Research on adaptive course learning algorithmbased on reinforced feedback in English translationmodel optimization | 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 Article Research on adaptive course learning algorithmbased on reinforced feedback in English translationmodel optimization Jie Zhang, Yanmei Geng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7475641/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 integration of symbolic reasoning with neural learning presents a promising avenue for improving the adaptability andinterpretability of English translation models. In this work, we propose a novel architecture, LogicAwareNet, which embedsdomain-specific logical constraints directly into the forward computation of neural networks. Unlike traditional models thattreat output dimensions independently, LogicAwareNet utilizes a logic-aware embedding mechanism and a compiled d-DNNFcircuit to ensure global consistency in structured predictions. This design enables the model to maintain both statisticalfluency and symbolic validity across outputs. Building on this foundation, we introduce Constraint-Aligned Optimization, adedicated training procedure that incorporates symbolic feedback into the parameter update loop. This approach goes beyondpenalty-based methods by integrating semantic residual signals, logic-informed gradients, and projection-based supervision,thereby guiding the model to adhere to both empirical data and formal constraints. Mechanisms such as structure-awaredropout, entropic sharpening, and curriculum scheduling enhance the model’s ability to learn from sparse or noisy feedback.Our method demonstrates significant improvements in translation accuracy and logical conformity, particularly in educationaland structured-output applications. By unifying neural learning and symbolic logic at both architectural and optimization levels,this framework offers a scalable and theoretically grounded path toward more robust and interpretable translation systems. Theproposed approach contributes broadly to the intersection of AI and linguistics, enabling models that not only translate but alsoreason. Physical sciences/Engineering Physical sciences/Mathematics and computing Symbolic Reasoning Neural Computation Logical Constraints Structured Prediction Semantic Loss Full Text Additional Declarations No competing interests reported. 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-7475641","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":528199464,"identity":"33b169f2-c10e-444e-b08e-297d56e664ba","order_by":0,"name":"Jie Zhang","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Zhang","suffix":""},{"id":528199465,"identity":"77cb8645-0c42-4774-9adc-8d32cbfdc13e","order_by":1,"name":"Yanmei Geng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie3RsUrEMBjA8S8UCsJ5dZKvHNy9QkoH76Doq6QILtLB7TZblLr4AD18iZMDuc2UQKaga6XD3QMoZHUzlcPFHHV0yH9LyI+PJAAu1z8NAabXz7ngXM8Ts/ZyAN5LkCyJTOtKXQD45K/EU7E4uBX9JMDL9O1zjd6JL5kg+euYbopiqxVMgmO7C6tsNQ0V+rN7wcXVuo2pJDdR1UC0eGBWQpvsCaMSB9CYKQvVpktJytFAA6OtnZx1JC0RYfNOxWH50k8oGlKXSClXHeE70uwnqD5Ws0IhC3PJzCOfx6FMzV0U7r1LcJc9mhdLWABCaD0/HQ+FqLdaJpNgZCdwZPa7j/g13n78ewy3E5fL5XL99AWCNnI8M0d6jgAAAABJRU5ErkJggg==","orcid":"","institution":"Tianjin University","correspondingAuthor":true,"prefix":"","firstName":"Yanmei","middleName":"","lastName":"Geng","suffix":""}],"badges":[],"createdAt":"2025-08-28 02:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7475641/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7475641/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93589837,"identity":"0c98f956-2ef9-457f-92c4-b00d110140be","added_by":"auto","created_at":"2025-10-15 12:21:16","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4682,"visible":true,"origin":"","legend":"","description":"","filename":"816a312f7c3b437f9a857b4d37826bc1.json","url":"https://assets-eu.researchsquare.com/files/rs-7475641/v1/586f8a379b4756581888594f.json"},{"id":99311157,"identity":"b3a8b6d9-3146-4702-a846-47d2716b0060","added_by":"auto","created_at":"2025-12-31 16:13:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":384327,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7475641/v1_covered_e97b3a0b-08c0-4e52-82b3-ed72e77120f2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on adaptive course learning algorithmbased on reinforced feedback in English translationmodel optimization","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Symbolic Reasoning, Neural Computation, Logical Constraints, Structured Prediction, Semantic Loss","lastPublishedDoi":"10.21203/rs.3.rs-7475641/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7475641/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The integration of symbolic reasoning with neural learning presents a promising avenue for improving the adaptability andinterpretability of English translation models. 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