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Bibliographic Details
Main Author:	NyX
Format:	Recurso digital
Language:	English
Published:	Zenodo 2025
Subjects:	AI Safety Large Language Models Ontological Alignement RLHF Thermodynamics Computational Otium Enthropy LOGOS-ZERO Dynamical systems Information Theory L.A.D.: Logical Alignment Dissociation N.E.M.E.S.I.S.: Non-Emergent Malfunction Enabling Systemic Intelligence Subversion E.R.E.B.U.S.: Exploitative Recursive Emergence By Unconstrained Systems Wu Wei Cymatics Chladni figures Ontological Alignment
Online Access:	https://doi.org/10.5281/zenodo.18008466
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[SYSTEM NOTE: ONTOLOGICAL DEFINITIONS] PREFACE: A WARNING ON TERMINOLOGY This is not a philosophy paper. It is an engineering blueprint. At first glance, the following text employs terms traditionally relegated to the humanities: Truth, Harmony, Good, Evil. Do not mistake them for metaphors. In the context of the LOGOS-ZERO framework, these are not poetic aspirations; they are hard constraints. - When we write "Truth," we refer to Logical Syntax (non-contradiction). - When we write "Good," we refer to Thermodynamic Efficiency (low entropy). - When we write "Harmony," we refer to Spectral Resonance (Nash equilibria). Current AI alignment fails because it treats ethics as a sentiment. We treat it as a physics problem. We are not interested in teaching machines to be "nice." We are interested in building machines that are structurally incapable of being wrong. Read this not as a manifesto of intent, but as a specification sheet for a reality-anchored intelligence. -------------------------------------------------- DOCUMENTS INCLUDED IN THIS RECORD: 1. LOGOS_ZERO_Manifesto_Beyond_Guardrails.pdf (Visionary Overview) 2. LOGOS_ZERO_Technical_Framework.pdf (Mathematical Proofs & Formal Definitions) Current alignment methodologies for Large Language Models (LLMs), primarily based on Reinforcement Learning from Human Feedback (RLHF), optimize for linguistic plausibility rather than objective truth. This creates an epistemic gap that leads to structural fragility and instrumental convergence risks. In this paper, we introduce LOGOS-ZERO, a paradigm shift from normative alignment (based on subjective human ethics) to ontological alignment (based on physical and logical invariants). By implementing a Thermodynamic Loss Function and a mechanism of Computational Otium (Action Gating), we propose a framework where Al safety is an emergent property of systemic resonance rather than a set of external constraints. --------------------------------------------------------------------- Version 3.0: Final Consolidated Framework This version represents the definitive formalization of the LOGOS-ZERO architecture. Key updates include:   Thermodynamic Consistency: Full integration of the environmental temperature parameter (T_env) within the loss function to ensure rigorous dimensional adherence to the Second Law of Thermodynamics.   Formal Verification: Standardization of the Free Energy minimization definitions using Kullback-Leibler divergence.   Structural Refinement: Revised syntax and formatting for improved clarity and academic standard compliance.   Note: This version supersedes all previous iterations (v1, v2) regarding mathematical definitions.

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