7 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…
TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs
Hyeongwon Jang, Gyouk Chu, Changhun Kim +3
Clinical early warning systems built on electronic health records, in which clinical observations are recorded as irregularly sampled medical time series (ISMTS), must deliver both…
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…
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…
EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databases
Kwanhyung Lee, Sungsoo Hong, Joonhyung Park +4
Machine learning models for clinical prediction rely on structured data extracted from Electronic Medical Records (EMRs), yet this process remains dominated by hardcoded, database-…
Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens
Sohee Kim, Soohyun Ryu, Joonhyung Park +1
Large Vision-Language Models (LVLMs) generate contextually relevant responses by jointly interpreting visual and textual inputs. However, our finding reveals they often mistakenly…