6 papers
TRIAGE: Dialectical LLM Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series
Hyeongwon Jang, Gyouk Chu, Changhun Kim +4
Clinical early warning systems built on irregularly sampled medical time series (ISMTS) from electronic health records must deliver continuous risk scores for patient triage as wel…
Learning to Select Source-Traceable Evidence for Language-Model Prediction from Irregular Clinical Time Series
Kwanhyung Lee, Juhwan Choi, Jongheon Kim +5
Numerical time-series models effectively process irregular electronic health record (EHR) trajectories, but do not expose which temporal patterns support each prediction as readabl…
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…
Delta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring
Changhun Kim, Yechan Mun, Hyeongwon Jang +3
Explaining online time series monitoring models is crucial across sensitive domains such as healthcare and finance, where temporal and contextual prediction dynamics underpin criti…
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…
TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation
Hyeongwon Jang, Changhun Kim, Eunho Yang
Recent explainable artificial intelligence (XAI) methods for time series primarily estimate point-wise attribution magnitudes, while overlooking the directional impact on predictio…