ETERNAL: An Axiomatic Framework for Safe Artificial Intelligence

This paper introduces ETERNAL, an axiomatic safety architecture designed to supplement statistical AI systems with logically grounded constraints. The framework proposes three core axioms that prioritize absolute protection of human life, rational causality, and anti-absurdity reasoning. These axioms draw conceptual inspiration from literary and philosophical critiques of human irrationality and technological risk, specifically aimed at making safety constraints more resistant to linguistic manipulation and probabilistic loopholes. Core Axioms: H1 (Anti-Absurdity): Rejects reasoning patterns demonstrating bureaucratic absurdity or dehumanized rationalization. H2 (Absolute Life Protection): Treats human protection as a hard constraint with zero acceptable probability of harm, prioritizing vulnerable populations. H3 (Logical Sovereignty): Mandates that causal reasoning overrides statistical justifications in safety-critical decisions. The ETERNAL framework is designed to operate as a meta-layer above generative systems, evaluating outputs through logical and ethical consistency rather than purely statistical likelihood. Method: Markdown files, if not implemented in the sourse code.