Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation

• URL: http://arxiv.org/abs/2605.24647v1
• Date: Sat, 23 May 2026 16:28:40 GMT
• Title: Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation
• Abstract summary: We propose PUMA, a framework that formulates personalization as decision-making under partial observability.<n>Puma maintains a belief over the user's hidden state, refines the user state model for observation generation and action-conditioned state transition.<n>Experiments show that PUMA improves long-horizon dialogue outcomes while maintaining strong response quality.
• Score: 40.40960192827234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalized dialogue requires more than recalling explicit user histories: systems also need to infer hidden user states that evolve through interaction and shape appropriate response strategies. Existing memory- and profile-based methods primarily reuse observable user information, offering limited support for modeling user-state dynamics or selecting actions based on how they shape future user states. We propose PUMA (Prospective User-state Modeling for Action selection), a framework grounded in the Free Energy Principle (FEP) that formulates personalization as decision-making under partial observability, centered on an explicit user state model that captures latent user states and their action-conditioned dynamics. At each turn, PUMA maintains a belief over the user's hidden state, refines the user state model for observation generation and action-conditioned state transition, and selects dialogue actions by minimizing expected free energy, balancing epistemic and pragmatic objectives under a unified criterion. This formulation shifts personalization from passive memory retrieval to model-based decision-making over user evolution. We instantiate PUMA on healthcare-oriented counseling and motivational interviewing benchmarks with latent state annotations for rigorous evaluation. Experiments show that PUMA improves long-horizon dialogue outcomes while maintaining strong response quality, and a cross-dataset study demonstrates more reliable user-state estimation and next-state prediction.

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