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Learning from Reliable Negatives: Confidence-Anchored Test-Time Adaptation for GUI Grounding
Graphical User Interface (GUI) grounding is essential for autonomous agents to map natural language instructions to precise screen coordinates. However, existing supervised fine-tuning and reinforcement learning methods are constrained by the high cost of annotation, creating a scalability bottleneck. In this paper, we introduce a label-free test-time training paradigm driven by two key insights: (1) confidence patterns in coordinate tokens are a better indicator than full-sequence confidence, and (2) in sparse GUI coordinate spaces, negative samples offer more reliable learning signals than p
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T09:56:10.000Z
First collected: 2026-09-20T11:41:07.830Z. This is not the publication date.