2 citations · 3 across the 7 of their papers we have counts for
9 papers
UI-KOBE: Knowledge-Oriented Behavior Exploration for Lightweight Graph-Guided GUI Agents
Yuxiang Chai, Han Xiao, Xinyu Fu +3
Recent advances in mobile GUI agents have shown strong potential for automating mobile tasks, but most effective systems still depend on large vision-language models for screenshot…
OmniInteract: Benchmarking Real-World Streaming Interaction for Real-Time Omnimodal Assistants
Xudong Lu, Xueying Li, Annan Wang +8
We introduce OmniInteract, a streaming benchmark for real-time omnimodal large language models evaluated through native online inference over audio-visual streams. Unlike offline v…
Rethinking VLM Representation for VLA Initialization
Weifeng Lin, Siyuan Huang, Hao Li +5
Vision-Language-Action (VLA) models widely adopt pretrained Vision-Language Models (VLMs) as policy backbones, yet it remains unclear what kind of pretrained VLM representation is…
PIRA-Bench: A Transition from Reactive GUI Agents to GUI-based Proactive Intent Recommendation Agents
Yuxiang Chai, Shunye Tang, Han Xiao +2
Current Graphical User Interface (GUI) agents operate primarily under a reactive paradigm: a user must provide an explicit instruction for the agent to execute a task. However, an…
UI-Mem: Self-Evolving Experience Memory for Online Reinforcement Learning in Mobile GUI Agents
Han Xiao, Guozhi Wang, Hao Wang +7
Online Reinforcement Learning (RL) offers a promising paradigm for enhancing GUI agents through direct environment interaction. However, its effectiveness is severely hindered by i…
UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents
Han Xiao, Guozhi Wang, Yuxiang Chai +12
In this paper, we introduce UI-Genie, a self-improving framework addressing two key challenges in GUI agents: verification of trajectory outcome is challenging and high-quality tra…
LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects
Guangyi Liu, Pengxiang Zhao, Yaozhen Liang +17
With the rapid rise of large language models (LLMs), phone automation has undergone transformative changes. This paper systematically reviews LLM-driven phone GUI agents, highlight…
UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning
Zhengxi Lu, Yuxiang Chai, Yaxuan Guo +7
The recent DeepSeek-R1 has showcased the emergence of reasoning capabilities in LLMs through reinforcement learning (RL) with rule-based rewards. Despite its success in language mo…
A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation
Yuxiang Chai, Shunye Tang, Han Xiao +12
The advancement of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has catalyzed the development of mobile graphic user interface (GUI) AI agents, which i…