I am currently a Visiting Research Student at the Big Data Institute (BDI) Lab, The Hong Kong University of Science and Technology (Guangzhou) (HKUST GZ), supervised by Prof. Yongqi Zhang. I am also a final-year undergraduate student at the Graph Data Intelligence (GDI) Lab, Beijing Institute of Technology (BIT), supervised by Prof. Rong-Hua Li. My research interests lie in Large Language Models and Data Mining.
Currently, I am working as a Research Intern at Tencent Yuanbao (CSIG) in the Multimodal Algorithm Group, where I focus on Multimodal RAG and agentic multimodal reasoning. I will begin my Ph.D. in The University of Hong Kong (Fall 2026). I am available only for internships in Shenzhen during my first Ph.D. year, and open to internships without location restrictions from the second year onward.
If you have internship opportunities related to Agentic (M)LLMs, please feel free to reach out to me via email!
Email: enjundu.cs@gmail.com
The Hong Kong University of Science and Technology
Visiting Student
Beijing Institute of Technology
Bachelor of Cyberspace Science and Technology
My research lies at the intersection of World Models, Agentic Reinforcement Learning, and Multimodal Large Language Models. I aim to build multimodal agents that go beyond perceiving the world—agents that can imagine it. Concretely, I study how to teach MLLMs to invoke world models as internal simulators, mentally rolling out future states before committing to an answer or an action, and how to train this imagination capability through reinforcement learning so the agent learns when to imagine, how deeply to imagine, and how to trust what it imagines. My long-term vision is to make imagination a first-class citizen of multimodal reasoning, enabling agents that are not only more capable, but also faithful, grounded, and compute-efficient—reliable enough to be trusted in long-horizon, real-world decision making under limited compute.
Here are some of my research works.

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Mar 8, 2026

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Feb 8, 2026

Oral presentation at AAAI 2026 on our paper "GraphOracle: Efficient Fully-Inductive Knowledge Graph Reasoning via Relation-Dependency Graphs".
Jan 25, 2026

Oral presentation at EMNLP 2025 on our paper "Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning".
Nov 5, 2025

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Oct 5, 2025

Oral presentation at IJCAI 2025 on our paper "ADC-GS: Anchor-Driven Deformable and Compressed Gaussian Splatting for Dynamic Scene Reconstruction".
Aug 25, 2025
I genuinely believe that meaningful progress in academia stems from open dialogue and thoughtful debate. If you have any questions about my research–or if you’ve previously contacted me through GitHub issues and haven’t received a response–please feel free to reach out via email. I’m always happy to chat, collaborate, or offer assistance where I can.
Throughout my academic journey, I’ve been fortunate to receive support and inspiration from many generous people. I’m always eager to give back to the community and engage with others passionate about learning and discovery.
That said, I’m not interested in discussions centered around citation counts, publication metrics, or quantitative comparisons between individuals. If your outreach is primarily motivated by such metrics, I kindly ask that you refrain from contacting me. I’m most interested in conversations about meaningful problems, creative solutions, and insightful ideas.
Preferred Email:
Optional Email:
Please avoid contacting me at enjun_du@bit.edu.cn, as I will no longer have access to this address.