A MULTIDIMENSIONAL VECTOR-BASED MODEL FOR QUANTIFYING CONSCIOUSNESS INSPIRED BY LLM ARCHITECTURES

Marius BODEA

Abstract


This paper presents a mathematical framework for modeling consciousness using a multidimensional vector space, inspired by large language model (LLM) architectures. Each axis represents a fundamental feature of consciousness, with normalized measurement protocols enabling comparisons across biological and synthetic agents. An interaction matrix models cross-axis influences, and the Consciousness Score (CS) is computed as a composite measure. Example calculations are performed for three agents: a human adult, GPT-like AI, and a simple robot. Results are visualized with a heatmap of inter-axis interaction and Pareto analasys charts.

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