Papers by Xueyu Hu
InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection (2026.eacl-long)
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Yuhang Liu, Pengxiang Li, Zishu Wei, Congkai Xie, Xueyu Hu, Xinchen Xu, Shengyu Zhang, Xiaotian Han, Hongxia Yang, Fei Wu
| Challenge: | Existing GUI Agents face challenges in multi-step reasoning and reliance on textual annotations, limiting their effectiveness. |
| Approach: | They propose an MLLM-based GUI Agent with a two-stage supervised fine-tuning pipeline that enhances GUI understanding and grounding. |
| Outcome: | InfiGUIAgent achieves competitive performance on several GUI benchmarks, highlighting the impact of native reasoning skills in enhancing GUI interaction for automation tasks. |
ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation (2025.findings-emnlp)
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| Challenge: | Currently, legal claims are not being used by non-professionals. |
| Approach: | They construct a dataset for Chinese legal claim generation task and then use it to evaluate the generated claims. |
| Outcome: | The proposed dataset is the first for the Chinese legal claim generation task and will be made publicly available. |
DAC-Bench: A Decision-Aware Benchmark for Compositional Mobile GUI Tasks (2026.acl-long)
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| Challenge: | Existing benchmarks focus on short, linear workflows and step-level accuracy, highlighting performance degradations. |
| Approach: | They propose a decision-aware benchmark with compositional tasks comprising 830 episodes and 11,345 action steps across 35 applications on Android and iOS. |
| Outcome: | The proposed benchmarks show performance degradation and branch correctness issues in 7 different GUI agents. |
OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)
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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu
| Challenge: | a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide. |
| Approach: | They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions . |
| Outcome: | The proposed agents are based on operating systems (OS) and operating systems frameworks. |
DOS: Dependency-Oriented Sampler for Masked Diffusion Language Models (2026.findings-acl)
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| Challenge: | Existing decoding strategies for pre-trained MDLMs rely on token-level uncertainty criteria, while largely overlooking sequence-level information and inter-token dependencies. |
| Approach: | They propose a training-free decoding strategy that leverages inter-token dependencies to inform token updates during generation. |
| Outcome: | Empirical results show that the proposed approach consistently achieves superior performance on both code generation and mathematical reasoning tasks. |