4 papers
OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models
Qiushi Sun, Kanzhi Cheng, Yian Wang +20
The paper introduces OSReward, a benchmark for evaluating vision-language model judges that assess computer-using agent trajectories, and presents open reward models (OS‑Shepherd)…
OS-Themis: A Scalable Critic Framework for Generalist GUI Rewards
Zehao Li, Zhenyu Wu, Yibo Zhao +11
Reinforcement Learning (RL) has the potential to improve the robustness of GUI agents in stochastic environments, yet training is highly sensitive to the quality of the reward func…
Global Commander and Local Operative: A Dual-Agent Framework for Scene Navigation
Kaiming Jin, Yuefan Wu, Shengqiong Wu +3
Vision-and-Language Scene navigation is a fundamental capability for embodied human-AI collaboration, requiring agents to follow natural language instructions to execute coherent a…
OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agent
Bowen Yang, Kaiming Jin, Zhenyu Wu +12
While Vision-Language Models (VLMs) have significantly advanced Computer-Using Agents (CUAs), current frameworks struggle with robustness in long-horizon workflows and generalizati…