SelfReinforcingReferenceLoopsₐndₜheStructuralConditionsₒfDegradationᵢnLLMSystems

This paper presents a structural and conceptual analysis of perceived degradation in large language model (LLM) systems. It argues that such degradation can emerge from interactions within connection architectures under competing evaluation spaces, rather than from changes in model capability itself. The paper introduces the concept of Self-Reinforcing Reference Loops (SRRL) and proposes that persistence in system behavior is governed by recursive selection mechanisms rather than intrinsic optimality. This work is conceptual and does not claim empirical validation.