6 papers
Who Should Lead Decoding Now? Tracking Reliable Trajectories for Ensembling Masked Diffusion Language Models
Heecheol Yun, Joonhyung Park, Joowon Kim +1
Masked Diffusion Language Models (MDLMs) have emerged as a distinct paradigm for sequence generation. As MDLMs become diverse in capabilities and knowledge coverage, an important q…
CollabVR: Collaborative Video Reasoning with Vision-Language and Video Generation Models
Joowon Kim, Seungho Shin, Joonhyung Park +1
Recent "Thinking with Video" approaches use Video Generation Models (VGMs) for visual reasoning by producing temporally coherent Chain-of-Frames as reasoning artifacts. Even strong…
ReviewScore: Misinformed Peer Review Detection with Large Language Models
Hyun Ryu, Doohyuk Jang, Hyemin S. Lee +16
Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes. To reliably detect low-qua…
Progress by Pieces: Test-Time Scaling for Autoregressive Image Generation
Joonhyung Park, Hyeongwon Jang, Joowon Kim +1
Recent visual autoregressive (AR) models have shown promising capabilities in text-to-image generation, operating in a manner similar to large language models. While test-time comp…
Early Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editing
Joowon Kim, Ziseok Lee, Donghyeon Cho +4
Despite recent advances in diffusion models, achieving reliable image generation and editing remains challenging due to the inherent diversity induced by stochastic noise in the sa…
Preserve or Modify? Context-Aware Evaluation for Balancing Preservation and Modification in Text-Guided Image Editing
Yoonjeon Kim, Soohyun Ryu, Yeonsung Jung +5
The development of vision-language and generative models has significantly advanced text-guided image editing, which seeks the preservation of core elements in the source image whi…