MasterI am a master's student majoring in Electronic Information (Artificial Intelligence) at South China University of Technology. Previously, I participated in a non-degree research program at the NUS (Chongqing) Research Institute, where I focused on reinforcement learning and multimodal large models.
My research interests center on Large Language Models and Agents. I have been fortunate to work on cutting-edge research projects, including AI-generated content detection and representation for multimodal generative tasks, with related work published in top-tier conferences such as AAAI and CVPR.
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South China University of TechnologyFuture Technology Institute
Master in Electronic Information (Artificial Intelligence)Sep. 2025 - present -
NUS (Chongqing) Research InstituteResearch Student in ECE (Non-degree)Sep. 2024 - Jun. 2025
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South China University of TechnologyFuture Technology Institute
Bachelor in Artificial IntelligenceSep. 2021 - Jun. 2025
Experience
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TENCENT - LIGHTSPEED STUDIOSNLP Research InternMay. 2025 - August. 2025
Honors & Awards
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First Prize in the "Greater Bay Area Cup" Guangdong-Hong Kong-Macao Financial Mathematics Modeling Competition2023
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Second Prize in the Asia and Pacific Mathematical Contest in Modeling2023
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University Third-Class Scholarships2022 - 2024
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Excellence Group Third Class Scholarship2022
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EVE Energy Third Class Scholarship2023
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Hongping Evergreen Scholarship2024
News
Selected Publications
Clarify Before Executing: A Self-Evolving Agent for Resolving Intent Asymmetry in 3D Tool Orchestration
Xiaoye Zhu, Weixin Li, Junan Huo, Bozhong Wang, Jia Zeng, Yi Yang, Cen Chen, Qi Liu
ACM International Conference on Multimedia (ACM MM) 2026 CCF-A
We introduce CLARE, a clarification-aware and evolutionary 3D agent that resolves underspecified instructions through strategic dialogue before orchestrating 3D tools.
Clarify Before Executing: A Self-Evolving Agent for Resolving Intent Asymmetry in 3D Tool Orchestration
Xiaoye Zhu, Weixin Li, Junan Huo, Bozhong Wang, Jia Zeng, Yi Yang, Cen Chen, Qi Liu
ACM International Conference on Multimedia (ACM MM) 2026 CCF-A
We introduce CLARE, a clarification-aware and evolutionary 3D agent that resolves underspecified instructions through strategic dialogue before orchestrating 3D tools.
Symbolic Representation for Any-to-Any Generative Tasks
Jiaqi Chen*, Xiaoye Zhu*, Yue Wang*, Tianyang Liu, Xinhui Chen, Ying Chen, Chak Tou Leong, Yifei Ke, Joseph Liu, Yiwen Yuan, Julian McAuley, Li-jia Li (* equal contribution)
IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2025 CCF-A
We propose a symbolic generative task description language and inference engine, capable of representing arbitrary multimodal tasks as symbolic flows.
Symbolic Representation for Any-to-Any Generative Tasks
Jiaqi Chen*, Xiaoye Zhu*, Yue Wang*, Tianyang Liu, Xinhui Chen, Ying Chen, Chak Tou Leong, Yifei Ke, Joseph Liu, Yiwen Yuan, Julian McAuley, Li-jia Li (* equal contribution)
IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2025 CCF-A
We propose a symbolic generative task description language and inference engine, capable of representing arbitrary multimodal tasks as symbolic flows.
Imitate Before Detect: Aligning Machine Stylistic Preference for Machine-Revised Text Detection
Jiaqi Chen*, Xiaoye Zhu*, Tianyang Liu*, Ying Chen, Xinhui Chen, Yiwen Yuan, Chak Tou Leong, Zuchao Li, Tang Long, Lei Zhang, Chenyu Yan, Guanghao Mei, Jie Zhang, Lefei Zhang (* equal contribution)
Association for the Advancement of Artificial Intelligence (AAAI) 2025 Oral CCF-A
Large Language Models (LLMs) have revolutionized text generation, making detecting machine-generated text increasingly challenging. Learn more
Imitate Before Detect: Aligning Machine Stylistic Preference for Machine-Revised Text Detection
Jiaqi Chen*, Xiaoye Zhu*, Tianyang Liu*, Ying Chen, Xinhui Chen, Yiwen Yuan, Chak Tou Leong, Zuchao Li, Tang Long, Lei Zhang, Chenyu Yan, Guanghao Mei, Jie Zhang, Lefei Zhang (* equal contribution)
Association for the Advancement of Artificial Intelligence (AAAI) 2025 Oral CCF-A
Large Language Models (LLMs) have revolutionized text generation, making detecting machine-generated text increasingly challenging. Learn more