AI RESEARCH
Zoom to Essence: Trainless GUI Grounding by Inferring upon Interface Elements
arXiv CS.LG
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ArXi:2603.14448v1 Announce Type: new Multimodal Large Language Model (MLLM)-based Graphical User Interface (GUI) agents develop rapidly, with visual grounding that maps natural language instructions to target UI elements serving as the core capability. Existing GUI agents typically fine-tune MLLM on massive datasets to handle challenges in understanding instructions and UI interfaces, which not only incurs high data annotation costs but also makes performance dependent on data quality and distribution. To avoid such cumbersome yet ineffective.