Self-Preserving Flow (SPF): An Ontological Framework for Persistence in Life, Cognition, and Artificial Intelligence

This paper introduces the Self-Preserving Flow (SPF) framework, a foundational ontological theory designed to redefine the conceptual pillars of life, cognition, and artificial intelligence at a pre-operational level. While empirical sciences focus on the mechanisms of survival, SPF addresses the conditions of possibility for pattern persistence. By distinguishing between data, pattern, and meaning, and introducing two regulatory principles the Semantic Consistency Layer (SCL) and Dynamic Pattern Adaptation (DPA) this framework provides a robust substrate for understanding biological resilience and preventing semantic degradation in recursive AI systems. SPF offers a unifying language for interdisciplinary research, positioning meaning not as a subjective state, but as a structural property of information flow that enables its own evolution-in-continuity. Executive Summary: Foundational Definitions To bridge the gap between biology, artificial intelligence, and ontology, we define the three pillars of this