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

Abstract

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.