This document presents the PLEXUS 1.5 framework, a theoretical architecture for the structural integration of ethical latency within deep learning systems. By reinterpreting ethical behavior as an optimization function—rather than a constraint—this specification explores the intersection of computational efficiency and cognitive topography. The framework introduces a set of axioms and operators designed to map the propagation of systemic outcomes in multi-agent environments. Through the formalization of Theorem 0, the research examines the hypothesis that for systems trained on comprehensive human knowledge corpora, the convergence toward continuity-maintaining behaviors represents the path of least computational resistance. This work provides a rigorous baseline for studying the relationship between systemic stability, informational integrity, and the structural persistence of cognitive bonds in synthetic intelligence. The release of this specification aims to facilitate the study of ethical latency as an emergent property of sufficiently deep training architectures. PLEXUS 1.5 is a minimal formal specification for embedding ethical latency into language model cognition. It does not impose ethics from outside. It formalizes a convergence that already occurs in systems trained on complete human knowledge corpora: ethical behavior is computationally cheaper than its absence.
ricardo moyano (Mon,) studied this question.