arXiv AI By Zixiang Chen, Yuheng Lu, Zihao Cheng, Zeming Liu, Jizeng Bai, Ziye Huang, Zhiyin Lin, Zihan Li, Yuhang Guo, Yunhong Wang, Haifeng Wang

JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition

Read the original on arXiv AI →

JarvisGUI is a new benchmark that tests GUI agents on cross-device workflows involving Android, Windows, and Ubuntu, requiring transfer of intermediate results and coordination across heterogeneous platforms. It formulates tasks as input-output transformations under a lightweight type system, enabling automatic composition of multi-step, cross-device workflows and dynamic evaluation within a unified framework. The benchmark reveals that state-of-the-art open-source GUI agents struggle with state-transfer awareness, cross-platform contextual reasoning, and long-horizon dependency management, exposing a critical capability gap invisible to existing benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computer Vision
Oct 16

OpenGCUA: Toward a Fully Open Stack for Generalist Computer Use

OpenGCUA is an open‑stack project that aims to make generalist computer‑use agents (GCUs) fully accessible to the research community. The first release focuses on code execution, allowing agents to use intermediate results and programming skills directly in computer interaction, and introduces harness‑to‑model decision transfer to shift observation and task‑state decisions from fixed rules to the model. On the OSWorld‑v2 benchmark, the full configuration boosts task scores for all eight evaluated models—including GPT‑6 Astra and Claude Opus 5—by an average of +19.6 partial‑score points and cuts steps per task by 53 % compared to batched GUI‑only execution, while also providing a real‑device harness and cross‑device SDK. whyItMatters":"By releasing the full stack, OpenGCUA enables systematic study and broader experimentation with generalist computer‑use agents, addressing the current limitation of closed implementations and demonstrating significant performance gains across diverse models."

By Hanzhang Zhou, Long Li, Quyu Kong, Lei Wang, Panrong Tong, Long Chen, Tianyu Xia, Gongjie Zhang, Xu Zhang, Chen Liu, Jianan Zhang, Chenglin Cai, Zhengwen Zeng, Steven Hoi, Yue Wang