Learning Personalized Agents from Human Feedback

This research introduces a framework for continually personalizing LLM agents by utilizing a streamlined memory system that learns from two types of human feedback. The system combines pre-action queries, which clarify ambiguous requests before they are executed, with post-action feedback to correct errors when an agent makes an incorrect assumption. This dual approach allows the agent to build a clean database of user preferences and effectively adapt when those preferences change over time, a phenomenon known as preference drift. Evaluated through online shopping and embodied agent scenarios, the method ensures agents do not remain "confidently wrong" but instead refine their behavior through a detect–summarize–integrate pipeline. Ultimately, the study demonstrates that integrating both reactive and proactive feedback channels significantly improves the accuracy and scalability of personalized artificial intelligence.