面向个性化 LLM 智能体的动态情感记忆管理
Dynamic Affective Memory Management for Personalized LLM Agents
AI总结：
提出受贝叶斯启发的动态情感记忆管理系统，通过最小化全局熵自主更新记忆向量库，并引入 DABench 基准，有效解决了个性化智能体的记忆冗余与陈旧问题。
摘要：
大语言模型的进步正使个性化 AI 智能体成为新的研究焦点。当前智能体系统主要依赖个性化的外部记忆数据库来提供定制化体验，但由于在交互过程中缺乏有效的记忆更新，它们面临记忆冗余、记忆陈旧和记忆-上下文整合不佳等挑战。为解决这些问题，我们提出了一种专为情感场景设计的新记忆管理系统。我们的方法采用受贝叶斯启发的记忆更新算法与记忆熵概念，使智能体能够通过最小化全局熵来自主维护动态更新的记忆向量数据库，从而提供更个性化的服务。为更好地评估系统在此情境下的有效性，我们提出了 DABench，一个专注于对物体的情感表达和情感变化的基准。实验结果表明，我们的系统在个性化、逻辑连贯性和准确性方面取得了卓越的性能。消融研究进一步验证了受贝叶斯启发的更新机制在缓解记忆膨胀方面的有效性。我们的工作为长期记忆系统的设计提供了新见解。
英文摘要：
Advances in large language models are making personalized AI agents a new research focus. While current agent systems primarily rely on personalized external memory databases to deliver customized experiences, they face challenges such as memory redundancy, memory staleness, and poor memory-context integration, largely due to the lack of effective memory updates during interaction. To tackle these issues, we propose a new memory management system designed for affective scenarios. Our approach employs a Bayesian-inspired memory update algorithm with the concept of memory entropy, enabling the agent to autonomously maintain a dynamically updated memory vector database by minimizing global entropy to provide more personalized services. To better evaluate the system's effectiveness in this context, we propose DABench, a benchmark focusing on emotional expression and emotional change toward objects. Experimental results demonstrate that, our system achieves superior performance in personalization, logical coherence, and accuracy. Ablation studies further validate the effectiveness of the Bayesian-inspired update mechanism in alleviating memory bloat. Our work offers new insights into the design of long-term memory systems.
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。
