arXiv AI By Yangyue Wang, Harshvardhan Sikka, Yash Mathur, Tony Zhou, Jinu Nyachhyon, Pranav Guruprasad

GUI-Perturbed: Domain Randomization Reveals Systematic Brittleness in GUI Grounding Models

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arXiv:2604. 14262v2 Announce Type: replace-cross Abstract: GUI grounding models report over 85% accuracy on standard benchmarks, yet drop 27-56 percentage points when instructions require spatial reasoning rather than direct element naming.

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arXiv AI
Jun 15

VISTA: View-Consistent Self-Verified Training for GUI Grounding

arXiv:2606. 14579v1 Announce Type: new Abstract: When applying Group Relative Policy Optimization (GRPO) for GUI Grounding, rollouts are sampled from a single screenshot view; groups often become either all failures on difficult instances or all successes on easy ones, yielding no useful relative advantage.

By Xinyu Qiu, Yunzhu Zhang, Heng Jia, Shuheng Shen, Changhua Meng, Linchao Zhu
arXiv Computation and Language
Sep 17

RankGround: Efficient High-Resolution GUI Grounding via Lightweight Reranker-Guided Crop Selection

RankGround is a two‑stage framework for GUI grounding that uses a single Vision‑Language Model call per query. It introduces GroundRanker, a lightweight multimodal reranker that selects the most promising crop from a dense candidate set, trained with a two‑stage curriculum on ranking supervision data derived from existing grounding datasets. Experiments show RankGround outperforms strong baselines, achieving 1.4× faster inference and a 5.5% average improvement in localization accuracy over the second‑best method across all backbones and screen scales.

By Liyang Fan, Xinping Bi, Yitai Li, Shuaimin Li, Hui Li, Min Yang