Hello, I'm Sanghyun Lee
I am an Integrated MS-PhD Student at KAIST CVLAB, advised by Seungryong Kim. I am broadly interested in diffusion models, with a current focus on discrete diffusion models for text generation. On the inference side, I am particularly interested in test-time scaling, efficiently scaling compute at inference time. Separately, on the training side, I am drawn to modeling, designing architectures and formulations that make these capabilities possible.
I received my BS from KAIST in 2025.
Experience
Krafton
Research Intern, Foundation Research Team.
Seoul, Korea
Education
Korea Advanced Institute of Science and Technology (KAIST)
Integrated M.S.-Ph.D., Graduate School of AI. Advised by Seungryong Kim.
Korea Advanced Institute of Science and Technology (KAIST)
B.S. in Electronic Engineering.
Publications
Lookahead Unmasking Elicits Accurate Decoding in Diffusion Language Models
Sanghyun Lee, Seungryong Kim, Jongho Park, Dongmin Park
ICML, 2026
Unifying Masked Diffusion Models with Various Generation Orders and Beyond
Chunsan Hong, Sanghyun Lee, Jong Chul Ye
ICML, 2026 (Spotlight)
Effective Test-Time Scaling of Discrete Diffusion through Iterative Refinement
Sanghyun Lee, Sunwoo Kim, Seungryong Kim, Jongho Park, Dongmin Park
arXiv
Fine-Grained Perturbation Guidance via Attention Head Selection
Donghoon Ahn, Jiwon Kang, Sanghyun Lee, Minjae Kim, Jaewon Min, Wooseok Jang, Saungwu Lee, Sayak Paul, Susung Hong, Seungryong Kim
NeurIPS, 2025
A Noise Is Worth Diffusion Guidance
Donghoon Ahn, Jiwon Kang, Sanghyun Lee, Jaewon Min, Minjae Kim, Wooseok Jang, Hyoungwon Cho, Sayak Paul, SeonHwa Kim, Eunju Cha, Kyong Hwan Jin, Seungryong Kim
ICLR, 2024