Entropy-Aware GUI Grounding: From Failure Analysis to Improved Localization

Graphical User Interface (GUI) agents have recently emerged as powerful tools for enabling automated operation across diverse platforms, with the potential to alleviate human workload. Despite promising progress, GUI grounding remains a critical bottleneck for achieving precise interaction, as incorrect localization of UI elements can lead to task failure. Therefore, it is crucial to identify when the model is likely to fail. In this work, we demonstrate that the entropy of output tokens is strongly correlated with model failures. Based on this observation, we propose an entropy-aware, training-free method to improve GUI grounding performance. Experiments on two GUI grounding benchmarks show that the proposed method achieves state-of-the-art results.