Memory-Augmented Architecture for Long-Term Context Handling in Large Language Models

• URL: http://arxiv.org/abs/2506.18271v1
• Date: Mon, 23 Jun 2025 03:57:25 GMT
• Title: Memory-Augmented Architecture for Long-Term Context Handling in Large Language Models
• Abstract summary: Large Language Models face challenges in maintaining coherent interactions over extended dialogues due to their limited contextual memory.<n>We propose a memory-augmented architecture that dynamically retrieves, updates, and prunes relevant information from past interactions, ensuring effective long-term context handling.
• Score: 0.46040036610482665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models face significant challenges in maintaining coherent interactions over extended dialogues due to their limited contextual memory. This limitation often leads to fragmented exchanges and reduced relevance in responses, diminishing user experience. To address these issues, we propose a memory-augmented architecture that dynamically retrieves, updates, and prunes relevant information from past interactions, ensuring effective long-term context handling. Experimental results demonstrate that our solution significantly improves contextual coherence, reduces memory overhead, and enhances response quality, showcasing its potential for real-time applications in interactive systems.

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