CogSynth: From Data Diagnosis to Production-Ready Scientific Software

CogSynth: From Data Diagnosis to Production-Ready Scientific Software | 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 CogSynth: From Data Diagnosis to Production-Ready Scientific Software Zhe Zhao, Haibin Wen, Pingxing Wang, Nianping Liu, Zhiheng, Pengkun Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9189702/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 Accelerating scientific discovery is hindered by the ``Triple Chasm''---the cognitive, methodological, and implementation divides separating raw data from valid computational solutions. We introduce the \textbf{Cognitive Synthesis Framework (CogSynth)}, an autonomous system that bridges these divides through two synergistic phases: Cognition (diagnosing data pathologies) and Synthesis (architecting executable software). Unlike rigid coding assistants, CogSynth functions as a ``Research Architect'' capable of reframing problem spaces. In geometric optimization, it autonomously derived symmetry constraints (first principles) from raw objectives. For pathological long-tailed data (CIFAR-100), it diagnosed imbalance and synthesized a composite architecture, achieving competitive accuracy. Furthermore, in symbolic regression, it discovered interpretable physical laws with a $>50$-fold reduction in token consumption compared to state-of-the-art LLM methods, identifying critical physical constraints like zero-inflation. This work demonstrates a paradigm shift from human-guided trial-and-error to autonomous methodological invention. Computer Architecture and Engineering Autonomous system Cognition 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-9189702","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610064694,"identity":"9a4ce298-d224-4e57-a83b-945d9e5832e6","order_by":0,"name":"Zhe Zhao","email":"","orcid":"","institution":"University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Zhao","suffix":""},{"id":610067963,"identity":"13953ec2-98d5-4151-ab65-0468a9a421e9","order_by":1,"name":"Haibin Wen","email":"","orcid":"","institution":"City University of Hong 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