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Woongyeong Yeo

M.S. Student
Graduate School of AI
KAIST


Hello, 안녕하세요 👋🏻

I am Woongyeong “Charles” Yeo, a master’s student at the Kim Jaechul Graduate School of AI, KAIST, where I am advised by Prof. Sung Ju Hwang in the MLAI Lab. Prior to this, I received a B.S. in Computer Science and Mathematical Sciences from KAIST.

My research interests lie in the development of multimodal large language models (MLLMs) with a strong focus on their practical applications in real-world scenarios. Previous work includes video understanding for out-of-distribution (OOD) scenarios and multimodal retrieval-augmented generation (RAG). Recently, I am working on memory agents, enabling MLLMs to store, retrieve, and reason over ultra-long context sequences. In short, my research areas include, but are not limited to:

Please refer to my Curriculum Vitae for more details.

Bio

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Woongyeong Yeo is a master's student at the Graduate School of AI, KAIST, advised by Prof. Sung Ju Hwang in the MLAI Lab. He received a B.S. in Computer Science and Mathematical Sciences from KAIST. His research focuses on developing multimodal large language models (MLLMs) with emphasis on practical real-world applications, particularly in LLM memory, video understanding, and autonomous reasoning across temporal and semantic challenges.

News

[Sep. 2025]Joining MLAI Lab at KAIST as a master’s student. 👨🏻‍🎓
[Apr. 2025]Our new preprint, UniversalRAG, is out on arXiv. 🔥
[Feb. 2025]VideoICL is accepted to CVPR 2025! 🎉

Selected Publications

  1. UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities
    Woongyeong Yeo*, Kangsan Kim*, Soyeong Jeong, Jinheon Baek, Sung Ju Hwang
    arXiv preprint, 2025.
  2. VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding
    Kangsan Kim*, Geon Park*, Youngwan Lee, Woongyeong Yeo, Sung Ju Hwang
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.

BibTeX

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