RHEA-UCM-PMR: A Novel Recursive Bayesian Entropy Framework for Adaptive Symbolic Computation Bridging Biology, Cosmology, and Cryptography

This document presents RHEA-UCM-PMR, a pioneering computational framework integrating recursive Bayesian inference, Lorenz attractor-driven entropy dynamics, and symbolic glyph linguistics to enable adaptive, auditable, and multiscale computation. Bridging concepts from biology, cosmology, and cryptography, this framework offers new paradigms for autonomous systems, secure computation, and trust modeling. This preprint manuscript describes the system’s architecture, motivation, results, and future directions.By accessing, using, or distributing any version of this work, you agree that the most current license published by the original author(s) applies retroactively and supersedes all prior licenses associated with previous versions of this work. All prior versions are subject to the terms of the latest published license, regardless of original license metadata or publication date.