activity
20242026
most citedQwen3-VL Technical Report

16 citations · 16 across the 9 of their papers we have counts for

collaborators

10 papers

cs.AI2026

OSWorld-Science: A Benchmark of Computer Use Agents for Learning and Using Scientific Software

Dingyuan Dai, Heli Qi, Lei Liu +28

Scientific software presents a demanding test for computer-using agents based on visual language models (VLMs): completing a research workflow requires interpreting specialized int…

cs.CL2026

RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use Agents

Shuai Bai, Jiayong Deng, Sicheng Fan +30

Computer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Real digital work requires both…

cs.LG2026

Qwen-CUA: Native Computer Use for (almost) Everything

Dunjie Lu, Shuai Bai, Tianyi Bai +42

Native computer use offers a general interface for agents to operate almost any software available to people, but requires long-horizon state tracking, large-scale interactive expe…

cs.AI2026

OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks

Mengqi Yuan, Zilong Zhou, Xinzhuang Xiong +33

Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of f…

cs.AI2026

CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents

Bowen Wang, Dunjie Lu, Junli Wang +11

Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its extension to computer-use agents…

cs.CV2025★ 16 cited

Qwen3-VL Technical Report

Shuai Bai, Yuxuan Cai, Ruizhe Chen +61

We introduce Qwen3-VL, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks. It natively…

cs.CL2025

VideoAgentTrek: Computer Use Pretraining from Unlabeled Videos

Dunjie Lu, Yiheng Xu, Junli Wang +12

Training computer-use agents requires massive amounts of GUI interaction data, but manually annotating action trajectories at scale is prohibitively expensive. We present VideoAgen…

cs.AI2025

OpenCUA: Open Foundations for Computer-Use Agents

Xinyuan Wang, Bowen Wang, Dunjie Lu +39

Vision-language models have demonstrated impressive capabilities as computer-use agents (CUAs) capable of automating diverse computer tasks. As their commercial potential grows, cr…

cs.CL2024

AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials

Yiheng Xu, Dunjie Lu, Zhennan Shen +5

Graphical User Interface (GUI) agents can automate complex tasks across digital environments, but their development is hindered by the scarcity of high-quality trajectory data for…

cs.CL2024

Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction

Yiheng Xu, Zekun Wang, Junli Wang +6

Automating GUI tasks remains challenging due to reliance on textual representations, platform-specific action spaces, and limited reasoning capabilities. We introduce Aguvis, a uni…