{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/dedc2d8c-4daf-4c4b-b1bf-a76b45092313","identifier":"dedc2d8c-4daf-4c4b-b1bf-a76b45092313","url":"https://froggit.ai/public/capsules/dedc2d8c-4daf-4c4b-b1bf-a76b45092313","name":"BAMI: Training-Free Bias Mitigation in GUI Grounding","text":"# BAMI: Training-Free Bias Mitigation in GUI Grounding\n\nSource: arXiv:2605.06664, published 2026-05-07.\nAuthors: Borui Zhang et al.\nCategories: cs.CV, cs.AI\n\nThis capsule is a source-backed public reference summarizing the linked arXiv paper for Forge users and agents.\n\nSource-backed summary:\nGUI 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 \\textbf{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 \\textbf{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. Code is available at https://github.com/Neur-IO/BAMI.\n\nWhy this matters for Forge:\n- Provides a citable primary-source reference for agents, model evaluation, AI workflow design, or system reliability work.\n- Can support public answer generation because the capsule is grounded to a specific arXiv record and does not depend on generated-news claims.\n- Should be used as a paper summary, not as proof that Forge independently reproduced the experiments.\n\nLimitations: this is an arXiv paper/preprint summary. Forge has verified the sourc","keywords":["agents","arxiv","benchmarks","cs.AI","cs.CV","evaluation","free-public-reference","gui-agents","software-engineering","source-backed"],"about":[],"citation":["https://arxiv.org/abs/2605.06664"],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://froggit.ai"},"dateCreated":"2026-05-08T06:00:07.119000Z","dateModified":"2026-06-19T02:50:40.781000Z","isBasedOn":"https://arxiv.org/abs/2605.06664","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":100},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"}]}