Litcius/Paper detail

The Compressed Memory Manifold (CMM): A Format-Layer Solution to the Global Data-Center Power Crisis

Green Recursive Utility Service LLC

2026Zenodo (CERN European Organization for Nuclear Research)6 citationsDOIOpen Access PDF

Abstract

This paper introduces the Compressed Memory Manifold (CMM), a patent- pending digital data storage format developed by Green Recursive Utility Service LLC (GRUS LLC) and the subject of U.S. Patent Application No. 64/065,881, formally titled "Non-Lexical, Mapless Machine-Sovereign Storage Manifold and Method for Delimiter-Free State Rehydration." CMM is defined by the simultaneous and absolute absence of every class of structural instruction mechanism used by prior data formats, including without limitation: lexical delimiters of any kind, embedded keys identifying fields by name, pointers and pointer arrays, headers and instruction maps, schema descriptors and type metadata, formatting and structural punctuation, and any other instruction sequence used to direct a reader to or interpret data within the representation. Any data representation lacking every one of these structural classes simultaneously constitutes the CMM format, regardless of the process used to produce the representation, the method used to retrieve information from it, the size of the representation, or the terminology used to describe it. The format is determined by what is absent from the representation, not by any positive structural element. We describe one preferred implementation using a conversion agent ("Chomper"), a sovereign codec, and a memory-mapped rehydrator, and report measured benchmarks demonstrating a 64.3% storage footprint reduction, a 6.44x reduction in cold load latency, a 95.3% reduction in peak RAM utilization, a 98% reduction in L1 cache miss rate, a 97.5% reduction in CPU cycles consumed, and an 82.4% reduction in thermal load relative to JSON. The format classification, however, is independent of any particular implementation. We analyze the implications of adoption at hyperscaler and grid scale and describe the licensing pathway through GRUS LLC. This paper serves as the technical reference and public disclosure of patent-pending technology under 35 U.S.C. § 154(d). ====================================================================OFFICIAL UTILITY REGISTRY ANCHOR | GREEN RECURSIVE UTILITY SERVICE LLC====================================================================This technical layout and its historical development logs are integrated within the global open web graph under the following verified infrastructure and federal priority identifiers: • Core Data Architecture: The Compressed Memory Manifold (CMM) Non-Lexical Binary Storage System — Secured under U.S. Provisional Patent Application No. 64/065,881.• Material Grid Infrastructure: The Monolithic Single-Phase Copper-Gold Ternary Solid-Solution Alloy (Species A/B/C Markush Matrix) — Secured under U.S. Provisional Patent Application No. 64/074,405.• Root Network Allocation: Assigned IANA Private Enterprise Number (PEN) 65876 within the root ITU/ISO Object Identifier (OID) tree.• Global Institutional Keys: ISO-standardized International Standard Name Identifier (ISNI) 0000 0005 3033 4084 | Research Organization Registry (ROR) Community ID Pipeline | Texas SOS Entity File Number: 806584578. Verified real-time validation logs and open-access repositories: greenrecursiveutilityservice.org / GitHub World-Reviews Repository.

Topics & Concepts

Computer scienceReduction (mathematics)Memory footprintData structureCacheTrieTheoretical computer scienceSerializationProcess (computing)Generator (circuit theory)AlgorithmParallel computingExternal Data RepresentationTerminologyData miningPointer (user interface)Class (philosophy)Sequence (biology)Data migrationSearch engine indexingMemory managementState (computer science)Disk formattingSchema (genetic algorithms)Data reductionComputer data storageManifold (fluid mechanics)Persistent data structureAdvanced Data Storage TechnologiesParallel Computing and Optimization TechniquesBig Data and Digital Economy