HyperClick: Advancing Reliable GUI Grounding via Uncertainty Calibration

• URL: http://arxiv.org/abs/2510.27266v1
• Date: Fri, 31 Oct 2025 08:07:02 GMT
• Title: HyperClick: Advancing Reliable GUI Grounding via Uncertainty Calibration
• Abstract summary: HyperClick is a novel framework that enhances reliable GUI grounding through uncertainty calibration.<n>We show that HyperClick achieves state-of-the-art performance while providing well-calibrated confidence.
• Score: 18.315584342136997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous Graphical User Interface (GUI) agents rely on accurate GUI grounding, which maps language instructions to on-screen coordinates, to execute user commands. However, current models, whether trained via supervised fine-tuning (SFT) or reinforcement fine-tuning (RFT), lack self-awareness of their capability boundaries, leading to overconfidence and unreliable predictions. We first systematically evaluate probabilistic and verbalized confidence in general and GUI-specific models, revealing a misalignment between confidence and actual accuracy, which is particularly critical in dynamic GUI automation tasks, where single errors can cause task failure. To address this, we propose HyperClick, a novel framework that enhances reliable GUI grounding through uncertainty calibration. HyperClick introduces a dual reward mechanism, combining a binary reward for correct actions with a truncated Gaussian-based spatial confidence modeling, calibrated using the Brier score. This approach jointly optimizes grounding accuracy and confidence reliability, fostering introspective self-criticism. Extensive experiments on seven challenge benchmarks show that HyperClick achieves state-of-the-art performance while providing well-calibrated confidence. By enabling explicit confidence calibration and introspective self-criticism, HyperClick reduces overconfidence and supports more reliable GUI automation.

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