Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions
arXiv:2602.22680v1 Announce Type: new 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 exami
arXiv:2602.22680v1 Announce Type: new 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.
Executive Summary
This article provides a comprehensive survey on personalized Large Language Model (LLM)-powered agents, which are designed to adapt behavior to individual users and maintain continuity across time. The authors organize the literature around four interdependent components: profile modeling, memory, planning, and action execution. They synthesize representative methods, analyze cross-component interactions, and examine evaluation metrics and benchmarks tailored to personalized agents. The survey charts a roadmap toward more user-aligned, adaptive, robust, and deployable agentic systems, accelerating progress from prototype personalization to scalable real-world assistants. The article offers a structured framework for understanding and designing personalized LLM-powered agents, highlighting the importance of personalization in long-term, user-dependent settings.
Key Points
- ▸ Personalized LLM-powered agents adapt behavior to individual users and maintain continuity across time.
- ▸ The authors organize the literature around four interdependent components: profile modeling, memory, planning, and action execution.
- ▸ The survey charts a roadmap toward more user-aligned, adaptive, robust, and deployable agentic systems.
Merits
Comprehensive Literature Review
The article provides a thorough and structured review of the literature on personalized LLM-powered agents, highlighting both strengths and limitations.
Taxonomy of Personalized Agents
The authors propose a novel taxonomy of personalized agents, which provides a clear and organized framework for understanding and designing personalized LLM-powered agents.
Future Directions
The article outlines future directions for research and deployment, highlighting opportunities for advancing the field of personalized LLM-powered agents.
Demerits
Methodological Limitations
The survey focuses primarily on existing literature, which may not capture the full range of methods and approaches in the field.
Lack of Experimental Evaluation
The article does not provide experimental evaluations of the methods and approaches discussed, which may limit the generalizability of the findings.
Overemphasis on Technical Aspects
The article may focus too heavily on the technical aspects of personalized LLM-powered agents, potentially neglecting the social and ethical implications of these systems.
Expert Commentary
This article provides a comprehensive and thought-provoking survey of the field of personalized LLM-powered agents. The authors offer a novel taxonomy and highlight the importance of personalization in long-term, user-dependent settings. However, the article may benefit from a more nuanced discussion of the social and ethical implications of these systems. Furthermore, the lack of experimental evaluation and overemphasis on technical aspects may limit the generalizability of the findings. Despite these limitations, the article provides a valuable contribution to the field and offers a roadmap for future research and development.
Recommendations
- ✓ Future research should focus on developing more robust and scalable methods for personalized LLM-powered agents.
- ✓ Experimental evaluations of the methods and approaches discussed in the article should be conducted to increase the generalizability of the findings.
- ✓ A more nuanced discussion of the social and ethical implications of personalized LLM-powered agents should be included in future research and development.
