Mem-PAL: Towards Memory-based Personalized Dialogue Assistants for Long-term User-Agent Interaction

• URL: http://arxiv.org/abs/2511.13410v2
• Date: Wed, 26 Nov 2025 16:51:41 GMT
• Title: Mem-PAL: Towards Memory-based Personalized Dialogue Assistants for Long-term User-Agent Interaction
• Authors: Zhaopei Huang, Qifeng Dai, Guozheng Wu, Xiaopeng Wu, Kehan Chen, Chuan Yu, Xubin Li, Tiezheng Ge, Wenxuan Wang, Qin Jin,
• Abstract summary: We present PAL-Bench, a new benchmark designed to evaluate the personalization capabilities of service-oriented assistants in long-term user-agent interactions.<n>To improve personalized service-oriented interactions, we propose H$2$Memory, a hierarchical and heterogeneous memory framework.
• Score: 55.24448139349266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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