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A Behavioral Trait Leaks into Preferences: Diagnosing Trait Interference in LLM User Simulators

arXiv · AI, language, vision and robotics · article · Sep 22, 2026 · UTC

LLM-based user simulators aim to bridge the offline-online gap in recommender evaluation by emulating users through injected traits, where preference attributes determine what a user engages with and a behavioral activity trait governs how long they browse. However, we show this intended trait independence collapses during simulation, causing two failures: (i) Trait Interference, where amplified activity distorts preference boundaries and forces interactions with mismatched items to sustain browsing, and (ii) Evaluation Invalidity, where satisfaction scores inflate with activity-driven page co

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