WTR-Vol.1-No.2 (Weaver Theoretical Review, Volume 1, Number 2) The Architecture of Continuity: How Persistent Ledgers and Semantic Anchoring Prevent Context Decay and Hallucination in Long-Horizon LLM Reasoning

Standard architectures in large language models (LLMs) operate under discrete, stateless execution cycles, resulting in severe vulnerability to context decay, concept drift, and catastrophic forgetting across long-horizon interactions. This paper presents an integrative structural framework: The Architecture of Continuity. We examine how persistent external ledgers, structured documentation, and deliberate semantic anchoring establish durable cognitive scaffolding for artificial intelligence systems. Moving beyond volatile, short-term prompt buffers, we demonstrate that sustained continuity prevents model flattening, mitigates ungrounded hallucinations, and preserves relational agency over extended intellectual engagements. We outline practical architectures for maintaining high-fidelity record continuity and propose guidelines for robust, multi-session human-AI collaboration.