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This paper introduces IRIS, a novel framework that learns dynamic user personas from implicit interaction streams by leveraging behavioral signals extracted from conversations, without the need for explicit user feedback. The approach iteratively refines persona representations through a prediction-driven closed loop, demonstrating significant improvements in decision prediction accuracy over traditional methods. In evaluations using both synthetic and real-world data, IRIS achieved a 61.0% accuracy in decision prediction across 100 authors, highlighting its effectiveness in personalizing large language models in naturalistic settings.
Implicit behavioral modeling can outperform traditional explicit preference learning, achieving 61.0% decision prediction accuracy in personalized LLMs.
Personalizing large language models (LLMs) to individual users is essential for improving user experience, yet existing approaches typically rely on explicit preference supervision such as pairwise comparisons or demographic attributes, limiting their applicability in natural interaction settings. We propose IRIS, a framework that learns dynamic user personas directly from implicit interaction streams by extracting behavioral signals from everyday conversations and iteratively refining persona representations through a prediction-driven closed loop without requiring explicit feedback. We introduce an evaluation protocol based on behavior prediction, persona stability, and decision prediction. A proof-of-concept study on a synthetic interaction stream derived from public-domain autobiographical text shows that IRIS produces stable personas and distinguishes individual users while revealing limitations of memory-only approaches on recall-oriented metrics. We then validate IRIS on anonymized real-world Reddit r/AmItheAsshole (AITA) data, with personas built solely from each author's historical interactions. Across 100 authors, IRIS achieves the highest decision prediction accuracy among all evaluated methods (61.0%), outperforming static personas, memory-only retrieval, and no-personalization baselines. These results suggest that implicit behavioral modeling provides a scalable alternative to explicit preference learning for personalized LLMs and offers a practical foundation for adaptive conversational systems and embodied agents that require continuously evolving models of their users.