  While large language models (LLMs) have made notable advancements in natural
language processing, they continue to struggle with processing extensive text.
Memory mechanism offers a flexible solution for managing long contexts,
utilizing techniques such as compression, summarization, and structuring to
facilitate nuanced and efficient handling of large volumes of text. However,
existing techniques face challenges with static knowledge integration, leading
to insufficient adaptation to task-specific needs and missing
multi-segmentation relationships, which hinders the dynamic reorganization and
logical combination of relevant segments during the response process. To
address these issues, we introduce a novel strategy, Question then Reflection
Memory Mechanism (QRMeM), incorporating a dual-structured memory pool. This
pool synergizes static textual content with structured graph guidance,
fostering a reflective trial-and-error approach for navigating and identifying
relevant segments. Our evaluation across multiple-choice questions (MCQ) and
multi-document question answering (Multi-doc QA) benchmarks showcases QRMeM
enhanced performance compared to existing approaches.
