Monitoring and Attributing Implicit Personalization in Conversational Agents

Language models implicitly infer user attributes that shape their responses in ways users often neither expect nor endorse. This implicit personalization mediates concern- ing behaviors such as sycophancy, deception, and demographic bias. We investigate implicit user modeling through mechanistic interpretability, focusing on three goals: measuring the prevalence of personalized behaviors driven by implicit cues, analyzing how implicit user information is represented internally, and evaluating whether such rep- resentations can be controlled via editing and steering techniques. We construct realistic narrative biographies using a structured interview protocol conditioned on AgentBank synthetic persona templates, then generate implicit question–answer pairs answerable from each biography. Behaviorally, we compare how templated and narrative attributes elicit different levels of personalized behavior; mechanistically, we test whether latent representations of personas obtained via various steering methods preserve meaning- ful individual differences. To facilitate such work, we develop a visual interface for interacting with predefined personas and evaluating steering and probing methods. We found steering via ActAdd to be weak compared to in-context conditioning, with only the education axis showing consistent directional transfer to the no-context implicit setting at pilot scale. Moreover we found a significant difference in both in-context learn- ing and LoRA-based steering when using templates attributes vs biographies: models conditioned on narrative biographies consistently achieve higher accuracy on both ex- plicit and implicit persona questions, with the gap largest on explicit questions (81.4% vs. 75.2% for Qwen3-4B in-context) and persisting, though narrowing, on implicit ones.