Latent Memory Dynamics (LMD): An Architectural Framework for Long-Term Memory in Large Language Models Based on Dynamic Particle Fields

Large Language Models (LLMs) suffer from a structural inability to maintain consistent long-term memoryacross conversations, since their weights are frozen after training. Prevailing approaches, chiefly RetrievalAugmented Generation (RAG), treat memory as static text records retrieved by semantic similarity — anapproach that loses implicit context and offers no native mechanism for abstraction, gradual forgetting, orrepresenting conflict between facts that change over time.This paper proposes Latent Memory Dynamics (LMD), an architectural framework that redefines memoryas a continuous, dynamic particle field to be observed rather than a text store to be retrieved. LMD strictlyseparates abstract, stable knowledge (Fact) from volatile memory state (Particle), introducing physicalproperties — mass, viscosity, temperature, and entropy — that evolve across two decoupled timescales: aSlow Time governing crystallization, evaporation, and melting, and a Fast Time computing instantaneousactivation in response to a user query. A Memory Observer interprets this field through continuousGaussian activation landscapes, detecting emergent phenomena (peaks, tension zones, saddle points) thatare encoded into a small set of latent tokens injected directly into a frozen LLM's cross-attention layer,with no retraining required. We present the full mathematical and architectural specification of theframework together with an actionable, sprint-based implementation roadmap.