Title: ManiCoG: Training-Free Improvement for GUI Grounding via Manipulation Chains
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: GUI Grounding, Test-Time Scaling, GUI Agent

Abstract: GUI grounding is a critical capability for enabling GUI agents to execute tasks such as clicking and dragging. 
However, in complex scenarios like the ScreenSpot-Pro benchmark, existing models often suffer from suboptimal performance. 
Utilizing the proposed Masked Prediction Distribution (MPD) attribution method, we identify that the primary sources of errors are twofold: 
high image resolution (leading to precision bias) and intricate interface elements (resulting in ambiguity bias). 
To address these challenges, we introduce the Manipulation-based Chain of GUI Grounding (ManiCoG), which incorporates two key manipulations, coarse-to-fine focus and candidate selection, to effectively mitigate these biases. 
Our extensive experimental results demonstrate that ManiCoG significantly enhances the accuracy of various GUI grounding models in a training-free setting. 
For instance, applying our method to the TianXi-Action-7B model boosts its accuracy on the ScreenSpot-Pro benchmark from 51.9\% to 57.8\%. 
Furthermore, ablation studies confirm the robustness of the ManiCoG approach across diverse parameter configurations, highlighting its stability and effectiveness.