{"ID":2840622,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.13410","arxiv_id":"2511.13410","title":"Mem-PAL: Towards Memory-based Personalized Dialogue Assistants for Long-term User-Agent Interaction","abstract":"With the rise of smart personal devices, service-oriented human-agent interactions have become increasingly prevalent. This trend highlights the need for personalized dialogue assistants that can understand user-specific traits to accurately interpret requirements and tailor responses to individual preferences. However, existing approaches often overlook the complexities of long-term interactions and fail to capture users' subjective characteristics. To address these gaps, we present PAL-Bench, a new benchmark designed to evaluate the personalization capabilities of service-oriented assistants in long-term user-agent interactions. In the absence of available real-world data, we develop a multi-step LLM-based synthesis pipeline, which is further verified and refined by human annotators. This process yields PAL-Set, the first Chinese dataset comprising multi-session user logs and dialogue histories, which serves as the foundation for PAL-Bench. Furthermore, to improve personalized service-oriented interactions, we propose H$^2$Memory, a hierarchical and heterogeneous memory framework that incorporates retrieval-augmented generation to improve personalized response generation. Comprehensive experiments on both our PAL-Bench and an external dataset demonstrate the effectiveness of the proposed memory framework.","short_abstract":"With the rise of smart personal devices, service-oriented human-agent interactions have become increasingly prevalent. This trend highlights the need for personalized dialogue assistants that can understand user-specific traits to accurately interpret requirements and tailor responses to individual preferences. However...","url_abs":"https://arxiv.org/abs/2511.13410","url_pdf":"https://arxiv.org/pdf/2511.13410v2","authors":"[\"Zhaopei Huang\",\"Qifeng Dai\",\"Guozheng Wu\",\"Xiaopeng Wu\",\"Kehan Chen\",\"Chuan Yu\",\"Xubin Li\",\"Tiezheng Ge\",\"Wenxuan Wang\",\"Qin Jin\"]","published":"2025-11-17T14:22:32Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"RAG\",\"Large Language Model\"]","has_code":false}