@inproceedings{zhang-etal-2026-personaagent,
    title = "{P}ersona{A}gent: Bridging Memory and Action for Personalized {LLM} Agents",
    author = "Zhang, Weizhi  and
      Zhang, Xinyang  and
      Zhang, Chenwei  and
      Yang, Liangwei  and
      Shang, Jingbo  and
      Wei, Zhepei  and
      Zou, Henry Peng  and
      Huang, Zijie  and
      Wang, Zhengyang  and
      Gao, Yifan  and
      Pan, Xiaoman  and
      Xiong, Lian  and
      Liu, Jingguo  and
      Yu, Philip S.  and
      Li, Xian",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1315/",
    pages = "26421--26439",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Model (LLM) empowered agents have recently emerged as advanced paradigms that exhibit impressive capabilities in a wide range of domains and tasks. Despite their potential, current LLM agents often adopt a one-size-fits-all approach, lacking the flexibility to respond to users' varying needs and preferences. This limitation motivates us to develop PersonaAgent, the first personalized LLM agent framework designed to address versatile personalization tasks. Specifically, PersonaAgent integrates two complementary components: a personalized memory module that includes episodic and semantic memory mechanisms; a personalized action module that enables the agent to perform tool actions tailored to the user. At the core, the persona (defined as unique system prompt for each user) functions as an intermediary: it leverages insights from personalized memory to control agent actions, while the outcomes of these actions in turn refine the memory. Based on the framework, we propose a test-time user-preference alignment strategy that simulate the latest $n$ interactions to optimize the persona prompt, ensuring real-time user preference alignment through textual loss feedback between simulated and ground-truth responses. Experimental evaluations demonstrate that PersonaAgent significantly outperforms other baseline methods by not only personalizing the action space effectively but also scaling during test-time real-world applications. These results underscore the feasibility and potential of our approach in delivering tailored, dynamic user experiences."
}