5 papers
VALUEFLOW: Toward Pluralistic and Steerable Value-based Alignment in Large Language Models
Woojin Kim, Sieun Hyeon, Jusang Oh +1
Aligning Large Language Models (LLMs) with the diverse spectrum of human values remains a central challenge: preference-based methods often fail to capture deeper motivational prin…
Dynin-Omni: Omnimodal Unified Large Diffusion Language Model
Jaeik Kim, Woojin Kim, Jihwan Hong +8
We present Dynin-Omni, the first masked-diffusion-based omnimodal foundation model that unifies text, image, and speech understanding and generation, together with video understand…
Don't Let It Fade: Preserving Edits in Diffusion Language Models via Token Timestep Allocation
Woojin Kim, Jaeyoung Do
While diffusion language models (DLMs) enable fine-grained refinement, their practical controllability remains fragile. We identify and formally characterize a central failure mode…
MMPB: It's Time for Multi-Modal Personalization
Jaeik Kim, Woojin Kim, Woohyeon Park +1
Visual personalization is essential in user-facing AI systems such as smart homes and healthcare, where aligning model behavior with user-centric concepts is critical. However, rec…
SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding
Woohyeon Park, Woojin Kim, Jaeik Kim +1
Despite significant advancements in Vision-Language Models (VLMs), the performance of existing VLMs remains hindered by object hallucination, a critical challenge to achieving accu…