In the current field of agent memory, extensive explorations have been conducted in the area of memory retrieval, yet few studies have focused on exploring the memory content. Most research simply stores summarized versions of historical dialogues, as exemplified by methods like A-MEM and MemoryBank. However, when humans form long-term memories, the process involves multi-dimensional and multi-component generation, rather than merely creating simple summaries. The low-quality memory content generated by existing methods can adversely affect recall performance and response quality. In order to better construct high-quality long-term memory content, we have designed a multi-memory segment system (MMS) inspired by cognitive psychology theory. The system processes short-term memory into multiple long-term memory segments, and constructs retrieval memory units and contextual memory units based on these segments, with a one-to-one correspondence between the two. During the retrieval phase, MMS will match the most relevant retrieval memory units based on the user's query. Then, the corresponding contextual memory units is obtained as the context for the response stage to enhance knowledge, thereby effectively utilizing historical data. We conducted experiments on the LoCoMo dataset and further performed ablation experiments, experiments on the robustness regarding the number of input memories, and overhead experiments, which demonstrated the effectiveness and practical value of our method.

翻译：在当前智能体记忆领域，关于记忆检索已有广泛探索，但鲜有研究关注记忆内容本身。多数研究仅存储历史对话的摘要版本，例如A-MEM和MemoryBank等方法。然而，人类形成长期记忆的过程涉及多维度和多组分的生成，而非仅创建简单摘要。现有方法生成的记忆内容质量较低，可能对回忆性能和响应质量产生负面影响。为更好地构建高质量的长期记忆内容，我们受认知心理学理论启发，设计了一种多记忆片段系统（MMS）。该系统将短期记忆处理为多个长期记忆片段，并基于这些片段构建检索记忆单元和上下文记忆单元，两者间存在一一对应关系。在检索阶段，MMS会根据用户查询匹配最相关的检索记忆单元，随后获取对应的上下文记忆单元作为响应阶段的知识增强上下文，从而有效利用历史数据。我们在LoCoMo数据集上进行了实验，并进一步开展了消融实验、输入记忆数量鲁棒性实验以及开销实验，结果证明了我们方法的有效性和实用价值。