1 citations · 1 across the 13 of their papers we have counts for
16 papers
HybridCUA: Learning to Orchestrate GUI and CLI for Computer-Use Agents
Tongbo Chen, Junbo Niu, Zhengxi Lu +8
Computer use agents (CUAs) have demonstrated strong capabilities in completing digital tasks. However, existing CUAs either rely solely on graphical user interface (GUI) interactio…
Reflect, Revise, Reuse: Training-Free Skill Evolution for GUI Agents
Bofan Chen, Boxuan Zhang, Fei Tang +7
GUI agents execute long-horizon tasks on dynamic graphical user interfaces, where pop-ups, delayed loads, and relocated widgets routinely invalidate plans fixed before execution. R…
Learning from Reliable Negatives: Confidence-Anchored Test-Time Adaptation for GUI Grounding
Yizhou Liu, Fei Tang, Yuchen Yan +8
Graphical User Interface (GUI) grounding is essential for autonomous agents to map natural language instructions to precise screen coordinates. However, existing supervised fine-tu…
BrowserForge: Scaling Web Episode via Parallel Browser Sandboxes
Fei Tang, Huawen Shen, Zhiqiong Lu +7
Web agents that act from rendered pixels avoid the fragility and heavy token cost of reading a page's HTML or accessibility tree, but training them depends on large amounts of high…
PhoneWorld: From Real-App Trajectories to Dynamic and Verifiable Environments for Phone-Use Agents
Yuxuan Liu, Xin Lai, Junyi Li +21
Real applications provide the training setting closest to phone-agent deployment, but are difficult to reset, scale safely, and verify programmatically. Static screenshots and inte…
UI-Zoomer: Uncertainty-Driven Adaptive Zoom-In for GUI Grounding
Fei Tang, Bofan Chen, Zhengxi Lu +8
GUI grounding, which localizes interface elements from screenshots given natural language queries, remains challenging for small icons and dense layouts. Test-time zoom-in methods…
UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization
Zhengxi Lu, Fei Tang, Guangyi Liu +8
MLLM-based GUI agents have demonstrated strong capabilities in complex user interface interaction tasks. However, long-horizon scenarios remain challenging, as these agents are bur…
ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents
Fei Tang, Zhiqiong Lu, Boxuan Zhang +4
GUI agents drive applications through their visual interfaces instead of programmatic APIs, interacting with arbitrary software via taps, swipes, and keystrokes, reaching a long ta…
KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation
Tongbo Chen, Zhengxi Lu, Zhan Xu +13
Personalized mobile agents that infer user preferences and calibrate proactive assistance hold great promise as everyday digital assistants, yet existing benchmarks fail to capture…
UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning
Zhengxi Lu, Jiabo Ye, Fei Tang +8
Graphical User Interface (GUI) agents have demonstrated remarkable progress in automating complex user interface interactions through reinforcement learning. However, current appro…
Test-Time Reinforcement Learning for GUI Grounding via Region Consistency
Yong Du, Yuchen Yan, Fei Tang +5
Graphical User Interface (GUI) grounding, the task of mapping natural language instructions to precise screen coordinates, is fundamental to autonomous GUI agents. While existing m…
GUI-G: Gaussian Reward Modeling for GUI Grounding
Fei Tang, Zhangxuan Gu, Zhengxi Lu +9
Graphical User Interface (GUI) grounding maps natural language instructions to precise interface locations for autonomous interaction. Current reinforcement learning approaches use…
SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation
Siqi Chen, Xinyu Dong, Haolei Xu +10
Large Language Models (LLMs) and Multimodal LLMs have shown promising capabilities for SVG processing, yet existing benchmarks suffer from limited real-world coverage, lack of comp…
A Survey on (M)LLM-Based GUI Agents
Fei Tang, Haolei Xu, Hang Zhang +12
Graphical User Interface (GUI) Agents have emerged as a transformative paradigm in human-computer interaction, evolving from rule-based automation scripts to sophisticated AI-drive…
Think Twice, Click Once: Enhancing GUI Grounding via Fast and Slow Systems
Fei Tang, Yongliang Shen, Hang Zhang +7
Humans can flexibly switch between different modes of thinking based on task complexity: from rapid intuitive judgments to in-depth analytical understanding. However, current Graph…
GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation
Fei Tang, Yongliang Shen, Hang Zhang +6
Large language model-based explainable recommendation (LLM-based ER) systems show promise in generating human-like explanations for recommendations. However, they face challenges i…