Litcius/Paper detail

Compressed Memory Manifold (CMM): A Non-Lexical, Mapless Machine-Sovereign Storage Manifold and Method for Delimiter-Free State Rehydration

Green Recursive Utility Service LLC

2026Zenodo (CERN European Organization for Nuclear Research)8 citationsDOIOpen Access PDF

Abstract

⚠️ NOTICE: PATENT PENDING - ALL RIGHTS RESERVED ⚠️ This publication describes patent-pending technology that is the exclusive intellectual property of Green Recursive Utility Service LLC. No license, express or implied, is granted by this publication. Unauthorized implementation, commercial use, or reproduction of the technology, methods, formats, or architectures described herein may constitute infringement of pending patent rights under 35 U.S.C. § 154(d). All commercial use requires a license from Green Recursive Utility Service LLC. ──────────────────────────────────────────────────────────────────────────── ABSTRACT United States Patent Application No. 64/065,881, filed May 14, 2026 with the United States Patent and Trademark Office under 35 U.S.C. § 111(b)... United States Patent Application No. 64/065,881, filed May 14, 2026 with the United States Patent and Trademark Office under 35 U.S.C. § 111(b). Confirmation Number 9869. Patent Center Number 76244506. Assignee: Green Recursive Utility Service LLC, Weatherford, Texas. The Compressed Memory Manifold (CMM) is a patent-pending non-lexical, mapless binary storage and rehydration system that fundamentally eliminates the parsing operation from the data lifecycle. The format is uniquely defined by what is termed the Dual-Omission Principle: the simultaneous and absolute absence of both (1) all lexical delimiters (quotation marks, brackets, braces, colons, commas, dashes, and embedded keys) and (2) all structural instruction maps (headers, pointer arrays, allocation tables, schema descriptors, and type prefixes). In place of these mechanisms, the CMM format employs hardware-aligned binary blocks (termed "grains," typically 64 bytes matching CPU L1 cache line architecture) with all data fields located at fixed mathematical byte-offsets hardcoded at compile time and shared as architectural constants between encoder and decoder. The CMM system comprises three integrated components: (a) The Chomper conversion agent, which transforms legacy lexical or map-based formats (JSON, XML, CSV, Protocol Buffers, MessagePack) into CMM binary manifolds; (b) The Sovereign Codec, which exploits structural redundancy in variable-length binary encoding (canonical versus non-canonical paths) to embed a secondary channel of meta-signals into the physical byte representation without consuming additional storage bytes; and (c) The Memory-Mapped Rehydrator, which loads CMM manifolds directly into virtual address space using operating system primitives (mmap on Unix-like systems, MapViewOfFile on Windows), making field data immediately available to the CPU's Arithmetic Logic Unit without any intermediate parsing, tokenization, or string-to-binary conversion. Empirical benchmarks conducted on May 8, 2026 using commodity hardware (Intel i9-13900K, 64 GB DDR5, NVMe Gen 4 SSD) on a 500,000-record financial transaction dataset demonstrate the following measured improvements over standard JSON: storage footprint reduction of 64.3 percent (35 MB to 12.5 MB), cold-path ingestion latency reduction of 6.44x (895 ms to 139 ms), peak RAM utilization reduction of 95.3 percent (850 MB to 40 MB), L1 cache miss rate reduction of 98 percent (40.2 percent to 0.81 percent), CPU cycle reduction of 97.5 percent (1.2 billion to 30 million), and thermal load reduction of 82.4 percent (125 W to 22 W). These results are independently reproducible. The invention applies to data serialization formats, memory compression systems, parsing optimization, machine-sovereign data structures, hardware-aligned storage architectures, edge computing data formats, real-time data ingestion systems, embedded systems storage, mobile operating system state management, cloud telemetry ingestion, AI/ML training pipelines, financial trading infrastructure, and autonomous vehicle data systems. This publication establishes public disclosure for purposes of 35 U.S.C. § 154(d), under which reasonable royalty rights begin to accrue from the date of publication of the patent application. Licensing inquiries should be directed to Green Recursive Utility Service LLC. Inventor: Nicholas Wray CordovaAssignee: Green Recursive Utility Service LLCFiling Date: May 14, 2026Patent Application Number: 64/065,881Jurisdiction: United States Patent and Trademark OfficeStatus: Patent Pending ====================================================================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 scienceByteCacheAliasTrademarkLeverage (statistics)State (computer science)Service (business)ParsingRecursion (computer science)CompilerTheoretical computer sciencePointer (user interface)Intellectual propertyHash functionLicensePath (computing)Binary numberData structureManifold (fluid mechanics)BitmapDirectoryAdvanced Data Storage TechnologiesParallel Computing and Optimization TechniquesAlgorithms and Data Compression