Title Reversible Correlation–Disparity Mesh (RCDM):A Logarithmic Distributed Learning Substrate with Exact Invertibility and Dual Correlation–Disparity Dynamics Overview This manuscript introduces the Reversible Correlation–Disparity Mesh (RCDM), a conceptual framework for a distributed learning substrate designed around principles of exact invertibility, logarithmic structural organization, and dual correlation–disparity dynamics. The work explores whether alternative computational substrates can provide new pathways for optimizing machine learning systems beyond the conventional paradigm of irreversible information compression. Rather than presenting an incremental modification of existing architectures, the document proposes a structured model intended to stimulate discussion around reversible learning processes and information-preserving computation. The framework is developed through a multidisciplinary lens, integrating ideas drawn from several domains of theoretical and computational research. Multidisciplinary Scope The architecture discussed in this paper draws upon concepts from multiple research areas, including: Machine Learning ArchitectureInvestigation of alternative learning substrates that preserve information during training and inference rather than discarding intermediate state information. Reversible ComputationExploration of computational systems in which transformations remain invertible, enabling the reconstruction of prior states and potentially improving transparency and energy efficiency. Distributed Systems and Graph StructuresConsideration of learning processes operating across distributed computational nodes organized through graph-based relationships. Spectral and Graph-Theoretic MethodsUse of correlation and disparity relationships within structured networks to analyze learning dynamics and information flow. Dynamical Systems and Mathematical ModelingConceptual parallels with dynamical systems frameworks in which system evolution is treated as a trajectory through a structured state space. Together, these perspectives form the basis of a research-oriented exploration into how reversible information structures might influence future learning architectures. Purpose of the Document This paper is presented as a preprint intended to introduce the RCDM framework and invite constructive discussion among researchers interested in computational theory, machine learning systems, and interdisciplinary approaches to algorithmic design. The goal is not to claim a finished engineering solution but rather to outline a coherent theoretical structure that may serve as a foundation for further investigation. About the Author Lance Thomas Davidson is an independent researcher based in Bali, Indonesia. His work focuses on multidisciplinary theoretical exploration spanning computational architectures, mathematical modeling, and emerging concepts in distributed learning systems. Many of the author’s research materials have been developed over an extended period but were not previously published in formal academic channels. As these works are now being digitized and organized for public release, several manuscripts may appear in close succession. This timing reflects the process of documenting and uploading existing research rather than the simultaneous creation of new material. The present document represents the most current version of the RCDM framework made available for broader academic review and discussion.
Lance Thomas Davidson (Sat,) studied this question.