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arXiv · 2604.17284

HalluClear: Diagnosing, Evaluating and Mitigating Hallucinations in GUI Agents

Chao Jin·Wenkui Yang·Hao Sun·Yuqi Liao·Qianyi Jiang·Kai Zhou·Jie Cao·Ran He·Huaibo Huang
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Abstract

While progress in GUI agents has been largely driven by industrial-scale training, ungrounded hallucinations often trigger cascading failures in real-world deployments.Unlike general VLM domains, the GUI agent field lacks a hallucination-focused suite for fine-grained diagnosis, reliable evaluation, and targeted mitigation.To bridge this gap, we introduce HalluClear, a comprehensive suite for hallucination mitigation in GUI agents as a complement to computation-intensive scaling. HalluClear comprises: (1) a GUI-specific hallucination taxonomy derived from empirical failure analysis; (2) a calibrated three-stage evaluation workflow which enhances VLM-as-a-judge reliability via expert-annotated benchmarking and ensemble credibility estimation; and (3) a mitigation scheme based on closed-loop structured reasoning, enabling lightweight continual post-training with cold-start initialization for both generalist and GUI-specialist agents. Experiments across representative agents and public benchmarks demonstrate that post-training on only 9K samples within our suite can significantly reduce hallucinations, thereby improving grounding and action fidelity, offering a compute-efficient pathway to robust GUI automation.

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Chao Jin, Wenkui Yang, Hao Sun, Yuqi Liao, Qianyi Jiang, Kai Zhou, Jie Cao, Ran He, Huaibo Huang. 2026-04-19. HalluClear: Diagnosing, Evaluating and Mitigating Hallucinations in GUI Agents. https://arxiv.org/abs/2604.17284

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