arXiv AI By Yeji Park, Jaeyun Shim, Taesik Gong

Can Agents Work for Everyone? Cross-User Reliability for Mobile GUI Agents in Personalized User Interfaces

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The paper introduces PAIR, a pipeline that creates user-conditioned application states to evaluate mobile GUI agents across different users, and RePAIR, a reinforcement learning method that incorporates personalization-aware interaction rewards. Experiments with six agents reveal significant variability in task success and subgoal achievement across users, especially when targeting personal content. RePAIR improves success rates over supervised fine-tuning, suggesting that learning from cross-user differences can enhance GUI-agent reliability.

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