Preventing Model Collapse through Self-Preserving Information Flow (SPF)

Model collapse (the progressive degradation of generative AI models trained on synthetically produced data) poses a critical threat to the long-term viability of large-scale artificial intelligence systems. Current mitigation strategies focus primarily on data curation, entropy regularization, or synthetic diversity injection, yet they fail to address the root cause: the erosion of semantic meaning across recursive training generations. This paper proposes a novel theoretical framework based on the Self-Preserving Flow (SPF) theory, which defines life and persistent intelligence as the self-sustaining transmission of meaningful informational patterns against entropic degradation. By introducing a Semantic Consistency Layer (SCL) and Dynamic Pattern Adaptation (DPA) mechanisms, SPF offers a principled approach to preserving meaning during data generation and model training. We demonstrate how SPF-based architectures can prevent informational entropy buildup, maintain pattern coherence over multiple generations, and provide a biologically inspired pathway toward robust, self-correcting AI systems. This framework not only complements existing safety research but also opens new directions for alignment strategies grounded in ontological continuity rather than short-term optimization.