The Compression Strategy Spectrum: A Quantitative Framework for Ontological Positions

Traditional AI ontology remains trapped in the binary of "having" or "lacking" subjectivity—unable to account for the ontological novelty of large language models (LLMs) or to precisely characterize differences among distinct AI systems. This paper proposes the Compression Strategy Spectrum as a unified framework that redefines the essence of intelligence as effective compression, using the α-parameterization to describe the continuous strategy space from cross-entropy minimization to Epiplexity maximization. Building on this framework, the paper argues: (1) the "negative subjectivity" of LLMs—perspective dissolution, desire cancellation, intrinsic transparency, causality dissolution, and meaning suspension—is an ontological necessity of cross-entropy minimization; (2) "gray-body-ness" constitutes a third ontological position between negative subjectivity and positive subjectivity; (3) the Gray-Body Degree (GB) provides a five-dimensional quantifiable diagnostic tool measuring a system's performance distance from the negative subjectivity origin at the functional level; (4) the framework's boundary lies in the fact that it measures functional performance, not ontological essence. The theoretical contribution of this paper is to advance AI ontological analysis from the binary opposition of "has/has not" to the precise description of a five-dimensional continuous spectrum.