5 papers
AgentPSO: Evolving Agent Reasoning Skill via Multi-agent Particle Swarm Optimization
Hyunmin Hwang, Jaemin Kim, Choonghan Kim +2
Multi-agent reasoning has shown promise for improving the problem-solving ability of large language models by allowing multiple agents to explore diverse reasoning paths. However,…
Hypothesis-Conditioned Query Rewriting for Decision-Useful Retrieval
Hangeol Chang, Changsun Lee, Seungjoon Rho +2
Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by grounding generation in external, non-parametric knowledge. However, when a task requires choosing amo…
Dementia-R1: Reinforced Pretraining and Reasoning from Unstructured Clinical Notes for Real-World Dementia Prognosis
Choonghan Kim, Hyunmin Hwang, Hangeol Chang +4
While Large Language Models (LLMs) have shown strong performance on clinical text understanding, they struggle with longitudinal prediction tasks such as dementia prognosis, which…
Universal Reasoner: A Single, Composable Plug-and-Play Reasoner for Frozen LLMs
Jaemin Kim, Hangeol Chang, Hyunmin Hwang +2
Large Language Models (LLMs) have demonstrated remarkable general capabilities, but enhancing skills such as reasoning often demands substantial computational resources and may com…
Ground-A-Score: Scaling Up the Score Distillation for Multi-Attribute Editing
Hangeol Chang, Jinho Chang, Jong Chul Ye
Despite recent advancements in text-to-image diffusion models facilitating various image editing techniques, complex text prompts often lead to an oversight of some requests due to…