MultiUS : Unsupervised Persona Mixture and Norm-Bounded Latent Steering for User Simulation

Obtaining a faithful User Simulator is a precondition for effective evaluation and training of multi-turn conversational systems. Prompt-based user simulators, however, reproduce human utterances and behavior poorly, while fine-tuning approaches require substantial computational resources. Recently, steering vectors have emerged as a promising and cost-effective mechanism for inducing target behaviors in Large Language Models, and have been successfully applied to persona and behavior control. However, computing steering vectors typically relies on curated contrastive samples and therefore requires prior knowledge of the relevant behavioral dimensions and their intensities. In this work, we propose to infer the distribution of user behaviors directly from real conversations, using unsupervised Gaussian-Mixture models in a latent behavioral space. Samples from these distributions are mapped to steering vectors through lightweight adapter layers applied to a frozen Language Model. The method allows to learn a single User Simulator, trained once, and subsequently generate $K$ distinct user behaviors by sampling from the learned distributions, without requiring explicit persona labels or retraining. To preserve the base model's activation geometry, we constrain the steering operation so that, at every token, the steered activation norm remains within (1±$\tau$) of the original norm. We study two adapter families: a per-channel affine map in the spirit of AdaLN-Zero, and a parameter-efficient variant operating entirely in a low-dimensional subspace. On WildChat, a single simulator adding 7.9–76.3M trainable parameters (0.2–1.8% of the frozen Qwen3.5-4B) writes far more human-like messages than a fully fine-tuned 8B user simulator and than prompted open-weight and frontier models (measured through relative MAUVE scores), is closest to human register, and stops conversations when humans do. On PRISM, a population it was never trained on, it is about as close to real users as real WildChat users are. We name our approach MultiUS.