BAMI: Training-Free Bias Mitigation in GUI Grounding
Borui Zhang, Bo Zhang, Bo Wang, Wenzhao Zheng, Yuhao Cheng, Liang Tang, Yiqiang Yan, Jie Zhou
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 Bias-Aware Manipulation Inference (BAMI), which incorporates two key manipulations, coarse-to-fine focus and candidate selection, to effectively mitigate these biases. Our extensive experimental results demonstrate that BAMI 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 BAMI approach across diverse parameter configurations, highlighting its stability and effectiveness.
BibTeX
@inproceedings{cvpr2026_bamitrainingfree,
title = {BAMI: Training-Free Bias Mitigation in GUI Grounding},
author = {Borui Zhang and Bo Zhang and Bo Wang and Wenzhao Zheng and Yuhao Cheng and Liang Tang and Yiqiang Yan and Jie Zhou and Jiwen Lu},
booktitle = {CVPR 2026},
year = {2026}
}