PIPA: Profile-Inferred Personalization Adaptation for Proactive Agent

Proactive agents must decide whether and how to assist before users explicitly ask for help. This requires inferring how a particular user tends to respond to assistance from limited interaction history. We introduce PIPA (Profile-Inferred Personalization Adaptation), which represents each user through compact interaction skills that pair recurring contexts and intervention patterns with behavioral evidence of receptivity. PIPA maintains a probabilistic belief over these preferences and uses profile-conditioned simulation to choose among remaining silent, asking, suggesting, or acting. Across three LLM backbones, PIPA improves proactive timing, and on two of the three it substantially improves intention prediction. The reconstructed profiles are user-specific and predictive of held-out behavior: replacing a user's profile with another user's reduces proactive-timing AUROC from $0.891$ to $0.492$. Controlled ablations separately test the contributions of user-specific representation, posterior uncertainty, and uncertainty-sensitive intervention selection. In particular, we compare posterior sampling against a compute-matched posterior-mean variant and isolate the effect of the uncertainty penalty while holding the user representation and simulator fixed. Attached to five agent harnesses on $\pi$-Bench, PIPA improves all $30$ harness–backbone–metric comparisons, with mean gains of $+3.55$ PROC and $+3.79$ COMP.