Learning from Reliable Negatives: Confidence-Anchored Test-Time Adaptation for GUI Grounding

The paper introduces a label‑free test‑time training approach for GUI grounding, leveraging confidence patterns in coordinate tokens rather than full‑sequence confidence. It proposes Confidence‑Anchored Learning (CAL) to filter pseudo‑labels and assign distance‑based binary rewards, and extends this to Confidence‑Anchored Negative Learning (CANL) which optimizes solely on negative samples to avoid noisy positives. Experiments show that CANL‑7B achieves 92.1% on ScreenSpot‑V2 and 33.8% on ScreenSpot‑Pro, improving the base model by 8.9%.

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• agents
• reinforcement-learning
• fine-tuning