WinDeskGround: A Benchmark for Robust GUI Grounding in Complex Multi-Window Desktop Environments
Quick Take
WinDeskGround introduces a benchmark for evaluating GUI grounding robustness in complex desktop environments.
Key Points
- Addresses robustness challenges in multi-window desktop settings.
- Generates diverse scenarios with window occlusion and layout density.
- Evaluates five leading MLLMs against real-world workflow distributions.
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~2 min readAbstract:Multimodal Large Language Models (MLLMs) have revolutionized GUI automation, yet their efficacy is largely established on idealized, single-layer interfaces. This paper identifies a critical reliability gap: state-of-the-art agents face distinct robustness challenges in real-world desktop environments characterized by multi-window stacking, occlusion, and visual clutter. To address this, we introduce WinDeskGround, a novel benchmark and synthesis framework tailored for evaluating GUI grounding robustness. Unlike static datasets, our framework parametrically generates complex desktop scenarios by controlling window occlusion, layout density, and semantic similarity, thereby simulating the distribution shifts of authentic workflows. We construct a diverse meta-dataset of 1,356 high-fidelity instruction-target pairs and conduct comprehensive evaluations of five leading MLLMs. Our results demonstrate that while top-tier agents excel in simplified settings, their accuracy declines under partial occlusion. WinDeskGround provides a valuable benchmark to facilitate the assessment and advancement of GUI agent robustness in realistic environments. The code is available at this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2605.16402 [cs.CV] |
| (or arXiv:2605.16402v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16402 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Haoren Zhao [view email]
[v1]
Wed, 13 May 2026 02:48:52 UTC (10,750 KB)
— Originally published at arxiv.org
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