Personalizing Long-Term Interactions with LLM-Based Agents

Abstract

LLMs increasingly serve as the control unit of AI agents. However, current systems struggle to support personalized interactions, as they lack effective mechanisms to remember and reuse user-specific information across sessions.

To address this, our research is centered around how memory augmentation and agentic decision-making can enable personalization in long-term interactions with LLM-based agents. As a first step, we proposed CAIM, a memory framework inspired by cognitive AI principles that aims to simulate human thought processes to create more adaptable systems. Building on this, we extended CAIM with an agentic workflow and user-specific memory modules to support personalized behavior beyond static retrieval-based approaches. We conducted a user study to evaluate perceived personalization, comparing our memory-based approach to a RAG baseline.

Our ongoing research focuses on how personalization can be measured without user studies, which are costly and hard to scale.

The long-term goal of our research is to establish foundations for designing and evaluating personalized LLM-based systems.