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20242026
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9 papers · 1 filter

cs.AI2026

Task-Adaptive Rubrics for GUI Reward Modeling

Tao Xiong, Xavier Hu, Wenkai Wang +6

Recent studies on GUI agents have increasingly focused on outcome reward modeling, which assigns outcome rewards by judging whether an executed trajectory satisfies the success cri…

cs.AI2026

GSAR: Goal-State-Anchor Rewards for Mobile GUI Agents with Self-Evolving Data Synthesis

Long Zhang, Yuhan Chen, Chaoran Zhang +7

Vision-Language Models (VLMs) based GUI agents stand to benefit significantly from online reinforcement learning (RL). However, their training is bottlenecked by two fundamental is…

cs.AI2026

Xiaomi-GUI-0 Technical Report

Wanxia Cao, Chengzhen Duan, Pei Fu +29

Graphical user interface (GUI) agents build on vision-language models to complete user tasks end-to-end in real applications through interface actions such as tapping, swiping, tex…

cs.AI2026

ScaleWoB: Guiding GUI Agents with Coding Agents via Large-Scale Environmental Synthesis

Guohong Liu, Jialei Ye, Pengzhi Gao +4

GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments. However, directly doing so in…

cs.AI2026

Scaling, Benchmarking, and Reasoning of Vision-Language Agents for Mobile GUI Navigation

Heng Qu, Yike Liu, Renren Jin +4

Vision-Language Models (VLMs) have shown rapid progress in mobile GUI navigation. This paper presents a systematic study of data scaling, benchmarking, and reasoning for VLM-based…

cs.AI2026

How Mobile World Model Guides GUI Agents?

Weikai Xu, Kun Huang, Yunren Feng +10

Recent advances in vision-language models have enabled mobile GUI agents to perceive visual interfaces and execute user instructions, but reliable prediction of action consequences…

cs.AI2025

STEP: Success-Rate-Aware Trajectory-Efficient Policy Optimization

Yuhan Chen, Yuxuan Liu, Long Zhang +3

Multi-turn interaction remains challenging for online reinforcement learning. A common solution is trajectory-level optimization, which treats each trajectory as a single training…

cs.AI2025

GUI-PRA: Process Reward Agent for GUI Tasks

Tao Xiong, Xavier Hu, Yurun Chen +6

Long-horizon GUI automation remains challenging due to error accumulation over extended interaction sequences. Process Reward Models (PRMs) provide dense step-level supervision for…

cs.AI2025

GUI-Shift: Enhancing VLM-Based GUI Agents through Self-supervised Reinforcement Learning

Longxi Gao, Li Zhang, Pengzhi Gao +3

Training effective Vision-Language Models (VLMs) for GUI agents typically depends on large-scale annotated datasets, whose collection is both labor-intensive and error-prone. We in…