通过持久记忆和用户画像实现基于LLM的智能体中的个性化长期交互
Enabling Personalized Long-term Interactions in LLM-based Agents through Persistent Memory and User Profiles
AI总结：
针对LLM智能体个性化交互不足的问题，提出集成持久记忆、动态协调、自我验证和动态用户画像的框架，经公共数据集和五天用户研究验证，可提升适应性和感知个性化。
摘要：
大型语言模型（LLMs）日益成为AI智能体的中央控制单元，然而当前方法在提供个性化交互方面的能力仍然有限。尽管检索增强生成通过改善上下文感知增强了LLM的能力，但它缺乏将上下文信息与用户特定数据相结合的机制。尽管个性化已在人机交互或认知科学等领域得到研究，但现有观点大多停留在概念层面，对技术实现的关注有限。为弥补这些不足，我们以个性化的统一定义作为概念基础，推导出自适应、以用户为中心的基于LLM的智能体的技术要求。结合已确立的智能体AI模式，如多智能体协作或多源检索，我们提出了一个集成持久记忆、动态协调、自我验证和动态用户画像的框架，以实现个性化长期交互。我们使用三个公共数据集，通过检索准确率、响应正确性或BertScore等指标评估了我们的方法。我们还进行了为期五天的试点用户研究，以补充这些结果，提供关于用户对感知个性化反馈的初步见解。该研究提供了早期迹象，指导未来工作，并强调了集成持久记忆和用户画像在提高基于LLM的智能体适应性和感知个性化方面的潜力。
英文摘要：
Large language models (LLMs) increasingly serve as the central control unit of AI agents, yet current approaches remain limited in their ability to deliver personalized interactions. While Retrieval Augmented Generation enhances LLM capabilities by improving context-awareness, it lacks mechanisms to combine contextual information with user-specific data. Although personalization has been studied in fields such as human-computer interaction or cognitive science, existing perspectives largely remain conceptual, with limited focus on technical implementation. To address these gaps, we build on a unified definition of personalization as a conceptual foundation to derive technical requirements for adaptive, user-centered LLM-based agents. Combined with established agentic AI patterns such as multi-agent collaboration or multi-source retrieval, we present a framework that integrates persistent memory, dynamic coordination, self-validation, and evolving user profiles to enable personalized long-term interactions. We evaluate our approach on three public datasets using metrics such as retrieval accuracy, response correctness, or BertScore. We complement these results with a five-day pilot user study providing initial insights into user feedback on perceived personalization. The study provides early indications that guide future work and highlights the potential of integrating persistent memory and user profiles to improve the adaptivity and perceived personalization of LLM-based agents.
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。
