AI RESEARCH
GUIDE: Interpretable GUI Agent Evaluation via Hierarchical Diagnosis
arXiv CS.AI
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ArXi:2604.04399v1 Announce Type: new Evaluating GUI agents presents a distinct challenge: trajectories are long, visually grounded, and open-ended, yet evaluation must be both accurate and interpretable. Existing approaches typically apply a single holistic judgment over the entire action-observation sequence-a strategy that proves unreliable on long-horizon tasks and yields binary verdicts offering no insight into where or why an agent fails. This opacity limits the utility of evaluation as a diagnostic tool for agent development. We.