Scientific discovery as meta-optimization: a combinatorial optimization case study

Scientific discovery as meta-optimization: a combinatorial optimization case study | 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 Scientific discovery as meta-optimization: a combinatorial optimization case study Yuan-Hang Zhang, Chesson Sipling, Massimiliano Di Ventra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9108409/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 Scientific discovery is fundamentally an optimization problem, defined by a vast state space'' of theories and experiments, and an evaluation criterion based on quality, novelty, and validity. Large language models (LLMs) have enabled automated exploration of this space, but we argue that simultaneous modification of the evaluation criteria is equally important. Here, we propose formalizing research as meta-optimization, where the optimization objective itself is also being optimized. Our key contribution is consensus objective aggregation,'' where LLM-generated objective functions are combined via correlation-weighted voting, yielding a stable, self-correcting evaluation criterion that evolves as understanding deepens. We apply this framework to algorithm discovery for 3-SAT problems based on digital MemComputing machines, reducing the baseline scaling exponent for problem size $N$ from $N^{2.51}$ to $N^{1.33}$ and delivering a $\sim 67\times$ speedup on the largest instances tested. As a problem-agnostic framework, we hope this approach will considerably aid scientific discovery. Physical sciences/Mathematics and computing/Computational science Physical sciences/Physics/Statistical physics, thermodynamics and nonlinear dynamics/Complex networks Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supplementarynmisubmission.pdf Supplementary Information for "Scientific discovery as meta-optimization: a combinatorial optimization case study" 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. 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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-9108409","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":605709996,"identity":"47778110-2a48-4c5c-9c70-f2a7cdea4670","order_by":0,"name":"Yuan-Hang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYJCCAx8qbEC0AQMDG3E6GB/OOJNGmhZmY962wyRoMbiR/kyah+18Yn//4Q0MH8oOE6MlIU1yDs/txBk30goYZ5wjTssxiTcStxM3SPAYMENdSEhLYhtQ8bnEDfxnDJj/EqclmdmQJ+FA4gaGHANmRmK0SJ55BgzkA8nGIL8c7DmXTlgL3/H0Bwc+/rOTBYbYxgc/yqwJa1E4gMQ5gEMRKpBvIErZKBgFo2AUjGgAALD7Qedi9p4fAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5415-3307","institution":"University of California San Diego","correspondingAuthor":true,"prefix":"","firstName":"Yuan-Hang","middleName":"","lastName":"Zhang","suffix":""},{"id":605709997,"identity":"5e50d613-a679-400d-8862-2c296b38b56d","order_by":1,"name":"Chesson Sipling","email":"","orcid":"","institution":"University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"Chesson","middleName":"","lastName":"Sipling","suffix":""},{"id":605709998,"identity":"a9331117-0a99-4e51-b060-99bd5a94f3be","order_by":2,"name":"Massimiliano Di Ventra","email":"","orcid":"https://orcid.org/0000-0001-9416-189X","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Massimiliano","middleName":"Di","lastName":"Ventra","suffix":""}],"badges":[],"createdAt":"2026-03-12 21:50:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9108409/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9108409/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106993575,"identity":"5887dcbf-afde-41ca-a641-3ca1918778d9","added_by":"auto","created_at":"2026-04-15 14:37:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1878158,"visible":true,"origin":"","legend":"Article File","description":"","filename":"mainnmisubmission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9108409/v1_covered_0e4000cc-dd83-4696-b62f-94a3c3dc5ecc.pdf"},{"id":104711254,"identity":"3568c24c-11b7-4855-81b4-7c32efe5d8f1","added_by":"auto","created_at":"2026-03-16 10:20:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1143348,"visible":true,"origin":"","legend":"Supplementary Information for \"Scientific discovery as meta-optimization: a combinatorial optimization case study\"","description":"","filename":"supplementarynmisubmission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9108409/v1/2107f2fb3f17fe6f08f85229.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Scientific discovery as meta-optimization: a combinatorial optimization case study","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-9108409/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9108409/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Scientific discovery is fundamentally an optimization problem, defined by a vast ``state space'' of theories and experiments, and an evaluation criterion based on quality, novelty, and validity. 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