Skip to content

Author

Fei Tang

We have 5 of 16 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Preprint Sep 2026

HybridCUA: Learning to Orchestrate GUI and CLI for Computer-Use Agents

Computer use agents (CUAs) have demonstrated strong capabilities in completing digital tasks. However, existing CUAs either rely solely on graphical user interface (GUI) interactions, which are often inefficient and error prone, or augment GUI interactions with application specific APIs or tools, which require substant...

Tong-I Chen, Jun-Bo Niu, Zhengxi Lu et al. · 0 citations
Preprint Sep 2026

Learning from Reliable Negatives: Confidence-Anchored Test-Time Adaptation for GUI Grounding

Graphical User Interface (GUI) grounding is essential for autonomous agents to map natural language instructions to precise screen coordinates. However, existing supervised fine-tuning and reinforcement learning methods are constrained by the high cost of annotation, creating a scalability bottleneck. In this paper, we...

Yi-Zhou Liu, Fei Tang, Yuchen Yan et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Reflect, Revise, Reuse: Training-Free Skill Evolution for GUI Agents

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. Recent agent-skill frameworks encapsulate reusable procedural knowledge to mitigate this, yet existing skill designs are largely developed wi...

Bo-Fan Chen, Bo-Xuan Zhang, Fei Tang et al. · 1 citation
Review Aug 2026

BrowserForge: Scaling Web Episode via Parallel Browser Sandboxes

BrowserForge is presented, a framework that generates web interaction data at scale by driving many browser sandboxes in parallel over the open web, and fine-tuning a compact multimodal model on this corpus raises its success rate on the live Online-Mind2Web from 25.66% to 33.33% and consistently improves step accuracy...

Fei Tang, Hua-Wen Shen, Zhiqiong Lu et al. · 0 citations
Conference Open access 2026

Experience-driven Multi-turn Reinforcement Learning for GUI Agents

EMPO achieves substantial gains over the base model and achieves competitive performance against strong baselines such as UI-TARS-7B and GPT-4o, demonstrating better generalization than prior single-turn RL approaches.

Zhengxi Lu, Jiabo Ye, Fei Tang et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.