Recursive Information Gain in Symbolic-Entropy Architectures by EnigmaticGlitch
Year: 2025
Type: article
Abstract: While entropy-based architectures such as RHEA-UCM measure the drift and turbulence of trust, they often omit a foundational companion: information theory. This paper formalizes a recursive framework uniting entropy gradients with symbolic information gain. By integrating Shannon entropy, mutual information, and symbolic compression into recursive Bayesian systems, we derive a principled method to measure how much a system learns, not just how much it drifts. This provides a blueprint for AI systems that don’t merely react to chaos but extract structured meaning from it. 🛡️ RHEA-Core Public Grant v1.0By 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 or public domain assumptions associated with earlier versions of this work, including but not limited to CC-BY-NC-ND 4.0, open-source metadata tags, or public repository assumptions. Unauthorized use under any former license constitutes a material infringement of the current legal rights of the author. License Conditions: · Non-Commercial Use Only · Attribution Required: Must cite “© EnigmaticGlitch · RHEA-UCM Framework · Patent Pending #63/796,404” · No Derivatives Permitted · Symbolic Derivative Clause Enforced · AI/ML Training Prohibited Without Explicit Grant 📜 Jurisdiction & Enforcement:-U.S. Copyright Law · Title 17-U.S. DMCA Provisions-U.S. Patent Law · Provisional Application #63/796,404 🔒 Violation constitutes willful infringement. All rights reserved.
Author: Roe, Paul
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949554137
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