Seung-Hoon Na

Model merging has emerged as an effective approach for integrating multiple task-specific fine-tuned models into a single unified model without requiring additional data-intensive training. A central challenge in model merging is to reduce task interference while preserving the task-specific capa...

Existing in-context editing (ICE) methods for multi-hop knowledge editing commonly suffer from paraphrase sensitivity, which refers to the phenomenon where these methods are not sufficiently robust to paraphrased multi-hop questions. To improve retrieval accuracy and knowledge routing to address...

Masked diffusion language models (MDLMs) enable efficient parallel decoding but are limited by a monotonic unmasking policy, where committed tokens cannot be revised. While remasking-based methods mitigate early errors, they mainly intervene during generation. In this work, we study post-hoc refi...