• Computer Science > Artificial Intelligence [Submitted on 18 Feb 2026] Title:Learning Personalized Agents from Human Feedback View PDF HTML (experimental)Abstract:Modern AI agents are powerful but often fail to align with the idiosyncratic, evolving preferences of individual users. • Prior approaches typically rely on static datasets, either training implicit preference models on interaction history or encoding user profiles in external memory. • However, these approaches struggle with new users and with preferences that change over time. • We introduce Personalized Agents from Human Feedback (PAHF), a framework for continual personalization in which agents learn online from live interaction using explicit per-user memory. • PAHF operationalizes a three-step loop: (1) seeking pre-action clarification to resolve ambiguity, (2) grounding actions in preferences retrieved from memory, and (3) integrating post-action feedback to update memory when preferences drift. • To evaluate this capability, we develop a four-phase protocol and two benchmarks in embodied manipulation and online shopping.
Article Summaries:
- Computer Science > Artificial Intelligence [Submitted on 18 Feb 2026] Title:Learning Personalized Agents from Human Feedback View PDF HTML (experimental)Abstract:Modern AI agents are powerful but often fail to align with the idiosyncratic, evolving preferences of individual users. Prior approaches typically rely on static datasets, either training implicit preference models on interaction history or encoding user profiles in external memory. However, these approaches struggle with new users and with preferences that change over time. We introduce Personalized Agents from Human Feedback (PAHF),
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