Energy-Efficient Artificial Intelligence via the Universal Quantum Mesh Equation (WME System)

Energy-Efficient Artificial Intelligence via the Universal Quantum Mesh Equation (WME System) | 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 Energy-Efficient Artificial Intelligence via the Universal Quantum Mesh Equation (WME System) Willianos Rego This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7577429/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 Artificial Intelligence systems are rapidly expanding in scale and complexity, but their growing energy consumption presents a critical barrier for scalability and sustainability. We introduce the WME System, a proportional consensus framework derived from the universal 72–28–2 stability law. Implemented as an additional inference layer, the WME System redistributes information proportionally before expansion and anchoring, reducing redundancy without altering model capacity. Comparative tests demonstrate consistent efficiency gains across multiple domains. In a 175B-parameter language model on NVIDIA A100 hardware, energy per 1,000 tokens decreased from 0.45 kWh to 0.29 kWh (−35%), while latency remained stable (120 → 118 ms/token) and accuracy unchanged. In computer vision (ResNet-50 on ImageNet), energy was reduced by ~32%, Top-1 accuracy preserved at 76.2%, and efficiency improved from 1.8 to 2.6 images/joule. In multimodal CLIP-like models, energy consumption decreased by 30–35%, latency was stable, accuracy preserved, and long-context semantic drift reduced. These results indicate that proportional consensus can serve as a generalizable mechanism for energy-efficient AI, enabling up to 35% energy savings without measurable trade-offs in accuracy or latency. The findings highlight a scalable and architecture-agnostic approach to sustainable artificial intelligence. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations There is NO Competing Interest. Supplementary Files WMEAISupportDocumentFull.docx Supplementary Information: Extended Data and Methods 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. 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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-7577429","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":513807725,"identity":"b45f197b-8ef1-441e-9a4f-cf719f409ad1","order_by":0,"name":"Willianos Rego","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACZubGAwlAmp+BsfHAAwO4+AE8WhgbwFokG0AMorQwAFWCKAMgCdZLUIvBcaCWBzV37I1vJANtKbDLk29gfvjoBsOdfJxaDoPcc+xZ4rYbiSCHJRcbHGAzNs5heGbZgEOLGVgL2+EEs9tgLcyJGxh42KRzGA4b4NAB1fLvsL3xbLCW+sT5DcRoSWw7zLhBGqzlMJAkoMUerKXvcOKM+w9BWo4nbjgM8ovBM5xaJPsPH3z449the/6e4w8ffPhTnTi/vfnh45yKOzi1YAHMIIIUDaNgFIyCUTAKMAAAyrxl73/6no8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0003-1952-9596","institution":"CODE CLUB","correspondingAuthor":true,"prefix":"","firstName":"Willianos","middleName":"","lastName":"Rego","suffix":""}],"badges":[],"createdAt":"2025-09-09 23:55:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7577429/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7577429/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91187335,"identity":"d519a74a-2af4-40c9-9d6d-576652369973","added_by":"auto","created_at":"2025-09-12 14:12:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":231615,"visible":true,"origin":"","legend":"","description":"","filename":"WMEAIManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7577429/v1_covered_c9318ebc-89ef-411b-b426-7420abf8c295.pdf"},{"id":91186349,"identity":"b4d59b74-2d22-4e48-982d-d2b021ec2242","added_by":"auto","created_at":"2025-09-12 13:56:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10531,"visible":true,"origin":"","legend":"Supplementary Information: Extended Data and Methods","description":"","filename":"WMEAISupportDocumentFull.docx","url":"https://assets-eu.researchsquare.com/files/rs-7577429/v1/de2f41d636945c1f0dcb1cee.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Energy-Efficient Artificial Intelligence via the Universal Quantum Mesh Equation (WME System)","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":"","lastPublishedDoi":"10.21203/rs.3.rs-7577429/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7577429/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial Intelligence systems are rapidly expanding in scale and complexity, but their growing energy consumption presents a critical barrier for scalability and sustainability. 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