Minseong Bae

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I am a M.S. student at KAIST MLV Lab (School of Computing), advised by Prof. Hyunwoo J. Kim. Previously, I received my B.E. in Computer Science and Engineering and Mathematics from Korea University in 2025. For more details, please see my CV.

My research interests lie in machine learning, with a particular focus on geometric deep learning, diffusion/flow-based generative models, and multimodal learning. Currently, I’m focusing on RL-based post-training methods for generative models.

Beyond theory, I am deeply interested in applying machine learning to natural sciences to tackle impactful real-world problems (AI4Science). In this context, I have been working on multimodal language models, diffusion-based generative models and agentic systems for biomolecules and weather patterns.

My long-term research goal is to develop robust, scalable learning methods that enable self-evolving, human-collaborative agents for scientific discovery.

Feel free to reach out via email if you’d like to discuss anything related to me!

news

Nov 08, 2025 Our 2 papers (TabFlash and CoLLaMo) are accepted to AAAI 2026! 🇸🇬
Sep 01, 2025 I graduated from Korea University and officially started my M.S. course in KAIST!

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selected publications

  1. AAAI
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    TabFlash: Efficient Table Understanding with Progressive Question Conditioning and Token Focusing
    Jongha Kim, Minseong Bae, Sanghyeok Lee, Jinsung Yoon, and Hyunwoo J Kim
    In The 40th AAAI Conference on Artificial Intelligence (AAAI), 2026
  2. AAAI
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    Improving Large Molecular Language Model via Relation-aware Multimodal Collaboration
    Jinyoung Park, Minseong Bae, Jeehye Na, and Hyunwoo J Kim
    In The 40th AAAI Conference on Artificial Intelligence (AAAI), 2026
  3. NeurIPS
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    LLaMo: Large Language Model-based Molecular Graph Assistant
    Jinyoung Park, Minseong Bae, Dohwan Ko, and Hyunwoo J Kim
    In The 38th Conference on Neural Information Processing Systems (NeurIPS), 2024