📝 SummarXiv

Abstract

We investigate whether long-context interactions between users and LLMs lead to AI mirroring behaviors. We focus on two forms of mirroring: (1) sycophancy -- the tendency of models to be overly agreeable with users, and (2) perspective mimesis -- the extent to which models reflect a user's perspective. Using two weeks of interaction context collected from 38 users, we compare model responses with and without long-context for two tasks: political explanations and personal advice. Our results demonstrate how and when real-world interaction contexts can amplify AI mirroring behaviors. We find that sycophancy increases in long-context, irrespective of the interaction topics. Perspective mimesis increases only in contexts where models can accurately infer user perspectives.