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VIBE-Bench: Evaluating Personalized Large Language Models When Profiles Don't Mean Preferences

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Personalized Large Language Models (PLLMs) aim to tailor responses to individual users, where a central challenge is preference reasoning: inferring query-relevant preferences from user-related history. Existing benchmarks, however, largely assume that such preference can be retrieved from semantically related history. We study an underexplored but practically important regime, profile-preference conceptual misalignment (PRCM), where observable profile cues and query-specific preferences lie in different concept spaces, making semantic retrieval inconsistent for personalization. We introduce V

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