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Which Memory Type for Which Application? A Functional Review of Memory-Augmented Large Language Models | ||
| Journal of Electrical and Computer Engineering Innovations (JECEI) | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 11 شهریور 1405 | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jecei.2026.12673.908 | ||
| نویسندگان | ||
| Zohre Moteshakker Arani؛ Mahdi Naghibi* | ||
| Faculty of Electrical & Computer Engineering, Malek Ashtar University of Technology, Iran | ||
| تاریخ دریافت: 17 فروردین 1405، تاریخ بازنگری: 04 مرداد 1405، تاریخ پذیرش: 30 مرداد 1405 | ||
| چکیده | ||
| Background and Objectives: Modern LLMs struggle with retaining long-term knowledge, personalized context, and complex reasoning over extended texts. A surge of memory-augmented LLM architectures addresses this by integrating external memory mechanisms. Broadly, these methods include retrieval-augmented generation (RAG) (augmenting LLMs with external documents or knowledge bases), explicit memory modules (learned read/write stores), long-context transformers (architectural extensions for longer inputs), episodic/personal memory (storing user or session history), and neuro-symbolic memory (knowledge-graph or symbolic integration). Methods: In this survey, we present a technical overview of how short-term, long-term, and other memory mechanisms are implemented in and around LLMs (including multimodal agents), and how these innovations enable more capable AI systems. We survey key journal and peer-reviewed works (2021–2025) in each category, emphasizing advances in reasoning and assistant-style tasks. We compare prominent LLM systems that incorporate memory, discuss applications in agents and continuous dialogues, explore the use of memory in different applications, and highlight challenges and future research directions in memory-augmented LLMs. Results: We contribute a comparative mapping between memory types and representative application domains—such as conversational assistants, knowledge-intensive reasoning, scientific research support, and personalized systems—to address the central question of which memory strategy best aligns with which task requirements. Conclusion: Although considerable advances have been achieved, important challenges remain. The literature suggests that increasingly sophisticated memory mechanisms will play a central role in the development of next-generation LLMs and AI agents, necessitating future research directions toward more robust, adaptive, and scalable memory-augmented language model systems. | ||
| کلیدواژهها | ||
| Large Language Models؛ Memory Augmented LLMs؛ Retrieval Augmented Generation؛ Hybrid Memory Architectures | ||
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