MGRetrieval: Memory-Guided Reflective Retrieval for Long-Term Dialogue Agents 事件

MGRetrieval: Memory-Guided Reflective Retrieval for Long-Term Dialogue Agents arXiv:2605.27437v1 Announce Type: cross Abstract: Large Language Models (LLMs) have made significant progress in dialogue, yet redundant memory contexts severely limit their effectiveness in long-term dialogue agents. External memory systems have been proposed to improve memory maintenance. However, these systems mainly rely on one-shot retrieval, which limits their ability to retrieve sufficient and relevant evidence