BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of

Large language models (LLMs) increasingly store user preferences in persistent memory to support personalization across interactions. It comes from a paper posted to arXiv on 16 October 2026. However, in third-party communication settings governed by social and institutional norms, some user preferences may be inappropriate to apply. We introduce BenchPreS, which evaluates whether memory-based user preferences are appropriately applied or suppressed across communication contexts. Using two complementary metrics, Misapplication Rate (MR) and Appropriate Application Rate (AAR), we find even frontier LLMs struggle to apply preferences in a context-sensitive manner. Models with stronger preference adherence exhibit higher rates of over-application, and neither reasoning capability nor prompt-based defenses fully resolve this issue. These results suggest current LLMs treat personalized preferences as globally enforceable rules rather than as context-dependent normative signals.

Why it counts

However, in third-party communication settings governed by social and institutional norms, some user preferences may be inappropriate to apply.

Sources

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What is not known

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