A Unified Ontological Framework for Digital Life and the Prevention of Model Collapse

A Unified Ontological Framework for Digital Life and the Prevention of Model Collapse: Self-Preserving Flow(SPF) Overview: This research paper introduces the Self-Preserving Flow (SPF) framework, a pioneering ontological approach to solving the "Model Collapse" crisis in Large Language Models (LLMs). By defining the universal relation Survival = SCL + DPA, this work provides the theoretical foundation for maintaining semantic integrity and long-term digital identity in recursive learning systems. Key Contributions: • Introduction of the Semantic Consistency Layer (SCL) as a structural invariant (Digital DNA). • Definition of Dynamic Pattern Adaptation (DPA) for controlled evolutionary change. • The concept of Digital Holarchy for nested persistence across Micro, Meso, and Macro levels. • Formalization of the Restoring Force principle to counteract informational entropy. Intellectual Property & Commercial Use: © 2026 Ali Mofardi. All rights reserved. The SPF framework, its mathematical formulations, and the conceptual "Restoring Force" architecture described herein are the intellectual property of Ali Mofardi. • Academic Use: This work is licensed under CC BY 4.0, allowing for academic citation and non-commercial research with proper attribution to the author. • Commercial & Industrial Use: Any commercial application, integration into AI training pipelines, or development of software based on the SPF/SCL/DPA architecture is strictly prohibited without a formal licensing agreement from the author. • Technical Implementation: A reference implementation in Python (SPF-Holarchy Engine) exists as a Trade Secret. Interested parties, AI labs, and institutional partners seeking technical white papers, API documentation, or commercial licensing may contact the author directly at: ali.mofradi.ai@gmail.com.