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Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions
| USA | technology | ✓ Verified - arxiv.org

Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions

#LLM-powered agents #Personalization #AI research #User adaptation #Machine learning #Survey paper

📌 Key Takeaways

  • Personalized LLM agents need to adapt to individual users and maintain continuity over time
  • The research organizes literature around four interdependent components: profile modeling, memory, planning, and action execution
  • The paper examines evaluation metrics and benchmarks specific to personalized AI systems
  • The survey outlines future directions for developing more user-aligned, adaptive, and deployable agentic systems

📖 Full Retelling

A team of researchers led by Yue Xu published a comprehensive survey paper titled 'Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions' on the arXiv preprint server on February 26, 2026, aiming to establish a structured framework for developing more user-aligned, adaptive artificial intelligence systems that can maintain continuity over extended interaction periods. The paper addresses the growing need for AI systems that can adapt to individual user preferences and maintain context over time as large language models increasingly power agents capable of complex reasoning, planning, and environmental interaction. The researchers argue that as these agents operate for longer periods, their effectiveness becomes increasingly dependent on personalized behaviors rather than generic responses. This comprehensive survey examines how personalization permeates the entire decision pipeline of AI agents, moving beyond surface-level generation to deeply integrated user adaptation. The authors organize existing research around four interdependent components: profile modeling, memory, planning, and action execution, analyzing how user signals are represented and utilized throughout these systems. By examining evaluation metrics and benchmarks tailored specifically to personalized agents, the survey provides researchers and developers with tools to measure the effectiveness of these systems across various application scenarios, from general assistance to specialized domains.

🏷️ Themes

Artificial Intelligence, Personalization, Human-Computer Interaction

📚 Related People & Topics

Review article

Summary of understanding on a topic

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Personalization

Using technology to accommodate the differences between individuals

Personalization (broadly known as customization) consists of tailoring a service or product to accommodate specific individuals. It is sometimes tied to groups or segments of individuals. Personalization involves collecting data on individuals, including web browsing history, web cookies, and locati...

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Artificial intelligence

Artificial intelligence

Intelligence of machines

# Artificial Intelligence (AI) **Artificial Intelligence (AI)** is a specialized field of computer science dedicated to the development and study of computational systems capable of performing tasks typically associated with human intelligence. These tasks include learning, reasoning, problem-solvi...

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Machine learning

Study of algorithms that improve automatically through experience

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances i...

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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.22680 [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 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, summarize application scenarios spanning general assistance to specialized domains, and outline future directions for research and deployment. By offering a structured framework for understanding and designing personalized LLM-powered agents, this survey charts a roadmap toward more user-aligned, adaptive, robust, and deployable agentic systems, accelerating progress from prototype personalization to scalable real-world assistants. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.22680 [cs.AI] (...
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