Personalization in LLMs doesn't just change what users see—it fundamentally alters the reasoning path the model takes to reach answers, creating a measurable failure mode that current mitigation techniques only partially address.
This paper introduces DRIFTLENS, a framework to measure how personalized language models change their reasoning process when given user information, even when final answers stay the same.