Author

Sehwan Kim

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#machine learning Preprint Sep 2026

Continual Data Unlearning in Diffusion Models via Transition-based Regularization

A continual data unlearning framework that uses completed deletion transitions as directional references to regularize future updates and achieves a better balance between deletion persistence and generative utility than existing unlearning baselines as requests accumulate.

Sunbeom Jeong, Sehwan Kim, Sang-Woo Hong et al. · 0 citations

Preprint Aug 2026

Function-Level Execution Feedback for Code Preference Optimization

STEP-KTODER is proposed, a framework for code preference optimization that defines steps as module-level functions in decomposed multi-function programs and assigns binary correctness labels via automatically generated unit tests and shows that execution-based labels are essential.

Idris Nechnech, Sehwan Kim, Jimin Seo et al. · 0 citations