面向长期LLM智能体的偏好感知记忆更新
Preference-Aware Memory Update for Long-Term LLM Agents
AI总结：
本文提出偏好感知记忆更新机制（PAMU），通过结合滑动窗口平均与指数移动平均构建融合偏好表示，动态细化长期记忆，在LoCoMo数据集五个任务场景中显著提升了五个基线的LLM输出质量。
摘要：
影响基于LLM的智能体推理能力的关键因素之一是它们利用长期记忆的能力。整合长期记忆机制使智能体能够基于历史交互做出明智的决策。尽管最近的进展显著改善了存储和检索组件，通过将记忆编码为密集向量以进行相似性搜索或将记忆组织为结构化知识图谱，但大多数现有方法在记忆更新方面存在不足。具体而言，它们缺乏根据不断演变的用户行为和上下文动态细化偏好记忆表示的机制。为了填补这一空白，我们提出了一种偏好感知记忆更新机制（PAMU），能够实现动态和个性化的记忆细化。通过将滑动窗口平均（SW）与指数移动平均（EMA）相结合，PAMU构建了一个融合的偏好感知表示，能够捕捉短期波动和长期用户倾向。我们在LoCoMo数据集的五个任务场景上进行了实验，结果表明我们的机制能够显著提升LLM在五个基线上的输出质量，验证了其在长期对话中的有效性。
英文摘要：
One of the key factors influencing the reasoning capabilities of LLM-based agents is their ability to leverage long-term memory. Integrating long-term memory mechanisms allows agents to make informed decisions grounded in historical interactions. While recent advances have significantly improved the storage and retrieval components, by encoding memory into dense vectors for similarity search or organizing memory as structured knowledge graphs most existing approaches fall short in memory updating. In particular, they lack mechanisms for dynamically refining preference memory representations in response to evolving user behaviors and contexts. To address this gap, we propose a Preference-Aware Memory Update Mechanism (PAMU) that enables dynamic and personalized memory refinement. By integrating sliding window averages (SW) with exponential moving averages (EMA), PAMU constructs a fused preference-aware representation that captures both short-term fluctuations and long-term user tendencies. We conduct experiments on five task scenarios of the LoCoMo dataset, and the results show that our mechanism can significantly improve the output quality of LLM in five baselines, validating its effectiveness in long-term conversations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
