Topological Materialization of Information via Spatiotemporal Resonance: Overcoming the Thermodynamic and Structural Limits of Computational Storage

Topological Materialization of Information via Spatiotemporal Resonance: Overcoming the Thermodynamic and Structural Limits of Computational Storage | 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 Topological Materialization of Information via Spatiotemporal Resonance: Overcoming the Thermodynamic and Structural Limits of Computational Storage Jung Min Ho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9369436/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 As the global appetite for artificial intelligence infrastructure drives data ingestion toward the zettabyte scale, the fundamental limits of the von Neumann computing architecture form an insurmountable thermal and temporal bottleneck. Conventional "Storage-and-Forward" paradigms demand linear memory scaling (O(n)), precipitating critical resource depletion and excessive thermodynamic entropy (Landauer's limit) during petabyte-scale (PB) operations. In this paper, we present a radical convergence of mathematical topology, solid-state physics, and network communications: Spatiotemporal Coordinate Resonance (STCR) driven by the J.M. Resonance functon(R_JM). By structuring ingress data as mathematical topological coordinates mapped to a multi-dimensional Mersenne grid, our Hierarchical Spatiotemporal Key Generation (HSKG) algorithm facilitates a Stateless I/O Direct-Path, completely bypassing traditional operating system page-cache buffering. Empirical physical validation within a heterogeneous multi-OS node ecosystem (macOS/Windows) demonstrates that ingesting 1.0 PB of structured binary streams requires a static, non-fluctuating peak Random Access Memory (RAM) envelope of exactly 0.41 MB. This establishes the world's first proven constant space complexity (O(1)) materialization at the PB boundary. Furthermore, topological isomorphism ensures 100% deterministic data integrity post-materialization, authenticated by SHA-256 lattice verification. The thermodynamic and economic implications of decoupling storage capacity from transient memory are immense, offering an immediate trajectory to zero-RAM resilient frameworks for terrestrial generative AI supercomputers and antenna-less satellite infrastructure alike. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Software Spatiotemporal Coordinate Resonance Constant Space Complexity Topological Mapping Zero- RAM Footprint J.M. Resonance Function Phase-Locking Materialization Antenna-less Satellite Communication Full Text Additional Declarations There is NO Competing Interest. Supplementary Files TECHNICALADDENDUMANTIGRAVITYCOMPUTATIONALVERIFICATIONREPORTv5.pdf Antigravity Computational Verification Report for HSKG v5.0 SoftwareAccessInstructions.txt HSKG Native Engine V5.0 Software Access Instructions 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. 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