Yuheng Lu
Author directoryOther people with similar names: Yuheng Lu, Yuheng Lu (Waseda)
Unverified author pages with similar names: Yuheng Lu
2026
JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition
Zixiang Chen | Yuheng Lu | Zihao Cheng | Zeming Liu | Jizeng Bai | Ziye Huang | Zhiyin Lin | Zihan Li | Yuhang Guo | Yunhong Wang | Haifeng Wang
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Zixiang Chen | Yuheng Lu | Zihao Cheng | Zeming Liu | Jizeng Bai | Ziye Huang | Zhiyin Lin | Zihan Li | Yuhang Guo | Yunhong Wang | Haifeng Wang
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Real-world GUI usage frequently involves workflows that span multiple devices and platforms, requiring the transfer of intermediate results, maintenance of shared state, and coordination across heterogeneous environments. However, existing GUI benchmarks overwhelmingly evaluate agents on single-device, statically defined tasks, thus leaving such cross-device capabilities largely unexamined, resulting in an overly optimistic assessment of agents’ readiness for real-world usage. We introduce JarvisGUI, a dynamic benchmark that evaluates GUI agents on cross-device workflows requiring coordinated interaction across heterogeneous platforms—including Android, Windows, and Ubuntu. Specifically, JarvisGUI formulates GUI tasks as input–output transformations under a lightweight type system, which allows us to automatically compose multi-step, cross-device workflows and dynamically evaluate agent performance within a unified framework. By evaluating agents in virtual environments spanning multiple operating systems, JarvisGUI reveals that state-of-the-art open-source GUI agents struggle with the state-transfer awareness, cross-platform contextual reasoning, and long-horizon dependency management required for real-world workflows, exposing a critical capability gap invisible to existing benchmarks.
2025
TransBench: Breaking Barriers for Transferable Graphical User Interface Agents in Dynamic Digital Environments
Yuheng Lu | Qian Yu | Hongru Wang | Zeming Liu | Wei Su | Yanping Liu | Yuhang Guo | Maocheng Liang | Yunhong Wang | Haifeng Wang
Findings of the Association for Computational Linguistics: ACL 2025
Yuheng Lu | Qian Yu | Hongru Wang | Zeming Liu | Wei Su | Yanping Liu | Yuhang Guo | Maocheng Liang | Yunhong Wang | Haifeng Wang
Findings of the Association for Computational Linguistics: ACL 2025
Graphical User Interface (GUI) agents, which autonomously operate on digital interfaces through natural language instructions, hold transformative potential for accessibility, automation, and user experience. A critical aspect of their functionality is grounding — the ability to map linguistic intents to visual and structural interface elements. However, existing GUI agents often struggle to adapt to the dynamic and interconnected nature of real-world digital environments, where tasks frequently span multiple platforms and applications while also being impacted by version updates. To address this, we introduce TransBench, the first benchmark designed to systematically evaluate and enhance the transferability of GUI agents across three key dimensions: cross-version transferability (adapting to version updates), cross-platform transferability (generalizing across platforms like iOS, Android, and Web), and cross-application transferability (handling tasks spanning functionally distinct apps). TransBench includes 15 app categories with diverse functionalities, capturing essential pages across versions and platforms to enable robust evaluation. Our experiments demonstrate significant improvements in grounding accuracy, showcasing the practical utility of GUI agents in dynamic, real-world environments. Our code and data will be publicly available at GitHub.