VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation
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Computer Science > Computation and Language
arXiv:2604.21375 (cs)
[Submitted on 23 Apr 2026 (v1), last revised 24 Sep 2026 (this version, v3)]
Title:VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation
Authors:Qijun Han, Haoqin Tu, Zijun Wang, Haoyue Dai, Yiyang Zhou, Nancy Lau, Alvaro A. Cardenas, Yuhui Xu, Ran Xu, Caiming Xiong, Zeyu Zheng, Huaxiu Yao, Yuyin Zhou, Cihang Xie
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Abstract:Autonomous GUI agents face two fundamental challenges: early stopping, where agents prematurely declare success without verifiable evidence, and repetitive loops, where agents cycle through the same failing actions without recovery. We present VLAA-GUI, a modular GUI agentic framework built around three integrated components that guide the system on when to Stop, Recover, and Search. First, a mandatory Completeness Verifier enforces UI-observable success criteria and verification at every finish step -- with an agent-level verifier that cross-examines completion claims with decision rules, rejecting those lacking direct visual evidence. Second, a mandatory Loop Breaker provides multi-tier filtering: switching interaction mode after repeated failures, forcing strategy changes after persistent screen-state recurrence, and binding reflection signals to strategy shifts. Third, an on-demand Search Agent searches online for unfamiliar workflows by directly querying a capable LLM with search ability, returning results as plain text. We additionally integrate a Coding Agent for code-intensive actions and a Grounding Agent for precise action grounding, both invoked on demand when required. We evaluate VLAA-GUI across five top-tier backbones, including Opus 4.5, 4.6 and Gemini 3.1 Pro, on two benchmarks with Linux and Windows tasks, achieving top performance on both (77.5% on OSWorld and 61.0% on WindowsAgentArena). Notably, three of the five backbones surpass human performance (72.4%) on OSWorld in a single pass. Ablation studies show that all three proposed components consistently improve a strong backbone, while a weaker backbone benefits more from these tools when the step budget is sufficient. Further analysis also shows that the Loop Breaker nearly halves wasted steps for loop-prone models.
| Comments: | The first two authors contribute equally |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) |
| Cite as: | arXiv:2604.21375 [cs.CL] |
| (or arXiv:2604.21375v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2604.21375
arXiv-issued DOI via DataCite
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Submission history
From: Qijun Han [view email][v1] Thu, 23 Apr 2026 07:42:37 UTC (4,133 KB)
[v2] Fri, 24 Apr 2026 17:01:08 UTC (4,133 KB)
[v3] Thu, 24 Sep 2026 23:58:02 UTC (4,099 KB)
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