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[2602.22680] Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions
Summary
This survey paper explores the development of personalized LLM-powered agents, focusing on their foundations, evaluation metrics, and future research directions.
Why It Matters
As AI continues to evolve, the need for personalized interactions in LLM-powered agents becomes crucial for enhancing user experience. This paper provides a structured framework that can guide future research and development, making it relevant for both academics and industry practitioners looking to implement personalized AI solutions.
Key Takeaways
- Personalization is essential for LLM-powered agents to adapt to individual user needs.
- The paper categorizes personalized agents into four components: profile modeling, memory, planning, and action execution.
- Evaluation metrics and benchmarks specific to personalized agents are discussed, highlighting their importance in measuring effectiveness.
- The survey outlines various application scenarios, from general assistance to specialized domains.
- Future research directions are proposed to enhance the robustness and deployability of personalized agents.
Computer Science > Artificial Intelligence arXiv:2602.22680 (cs) [Submitted on 26 Feb 2026] Title:Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions Authors:Yue Xu, Qian Chen, Zizhan Ma, Dongrui Liu, Wenxuan Wang, Xiting Wang, Li Xiong, Wenjie Wang View a PDF of the paper titled Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions, by Yue Xu and 7 other authors View PDF HTML (experimental) Abstract:Large language models have enabled agents that reason, plan, and interact with tools and environments to accomplish complex tasks. As these agents operate over extended interaction horizons, their effectiveness increasingly depends on adapting behavior to individual users and maintaining continuity across time, giving rise to personalized LLM-powered agents. In such long-term, user-dependent settings, personalization permeates the entire decision pipeline rather than remaining confined to surface-level generation. This survey provides a capability-oriented review of personalized LLM-powered agents. We organize the literature around four interdependent components: profile modeling, memory, planning, and action execution. Using this taxonomy, we synthesize representative methods and analyze how user signals are represented, propagated, and utilized, highlighting cross-component interactions and recurring design trade-offs. We further examine evaluation metrics and benchmarks tailored to personalized agents, summ...
