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.
By Yangyue Wang, Harshvardhan Sikka, Yash Mathur, Tony Zhou, Jinu Nyachhyon, Pranav Guruprasad
arXiv:2608. 11191v1 Announce Type: cross Abstract: GUI Visual Grounding is a fundamental capability for GUI agents.
By Shiyu Xuan, Zechao Li
arXiv:2608. 03270v1 Announce Type: cross Abstract: GUI grounding maps natural-language instructions to click locations and is essential for reliable GUI agents.
By Zichuan Fu, Shirong Wang, Wenlin Zhang, Guojing Li, Yimin Deng, Jingtong Gao, Junjia Qi, Hanyu Yan, Yefeng Zheng, Xiaopeng Li, Wanyu Wang, Xian Wu, Xiangyu Zhao
arXiv:2606. 13156v2 Announce Type: replace-cross Abstract: Letting a vision-language model (VLM) think longer at test time has driven much recent progress.
By Animesh Tripathy, Aswanth Krishnan
arXiv:2601. 14232v2 Announce Type: replace-cross Abstract: Pixel-based reinforcement learning agents often fail under purely visual distribution shift even when latent dynamics and rewards are unchanged, but existing benchmarks entangle multiple sources of shift and hinder systematic analysis.
By Egor Cherepanov, Daniil Zelezetsky, Alexey K. Kovalev, Aleksandr I. Panov
arXiv:2606. 18101v1 Announce Type: new Abstract: Graphical user interface (GUI) grounding requires vision-language models (VLMs) to identify small target elements in high-resolution screenshots and predict precise screen coordinates.
By Jingyuan Huang, Zuming Huang, Yucheng Shi, Tianze Yang, Xiaoming Zhai, Wei Chu, Ninghao Liu