Momentary Contexts - A Memory and Retrieval Approach for LLM Efficiency

Recent advancements in large language models (LLMs) have achieved remarkable breakthroughs in natural language processing and artificial intelligence. However, current LLMs rely on cumulative context, which poses structural limitations. This approach often results in inefficient utilization of computational resources and diminished coherence and adaptability in extended interactions. This study proposes an innovative framework that eliminates cumulative context and introduces the concepts of **“memory”** and **“retrieval”** to establish **“momentarily reconstructed minimal contexts.”**In this framework, **memory** refers to the external storage of past interactions exactly as they occurred in a database. This raw data serves as a persistent repository that can be retrieved as needed. **Retrieval** is the process of selectively accessing relevant stored memory to dynamically reconstruct short-term, localized contexts tailored to specific queries or tasks. This aligns with retrieval-augmented generation methods but differs by emphasizing dynamic context reconstruction rather than cumulative accretion.The proposed approach also highlights the potential of **memory** to function as an interface for external knowledge. By linking memory with domain-specific knowledge, it becomes possible to integrate expert knowledge dynamically into the reconstructed context at every moment, facilitating knowledge-intensive tasks. This enables LLMs to produce not only general responses but also highly specialized and contextually accurate outputs, significantly enhancing their performance in diverse domains.This study explores the design principles and implementation strategies of this memory-based system and empirically demonstrates its efficacy in improving both performance and efficiency when integrated into LLM architectures. By leveraging memory to incorporate external knowledge dynamically, this research offers a novel paradigm for LLM design, advancing AI models that emulate human memory systems while unlocking new possibilities for external data integration.