7 papers
MARIC: Multi-Agent Reasoning for Image Classification
Wonduk Seo, Minhyeong Yu, Hyunjin An +1
Image classification has traditionally relied on parameter-intensive model training, requiring large-scale annotated datasets and extensive fine tuning to achieve competitive perfo…
Toward Culturally Aligned LLMs through Ontology-Guided Multi-Agent Reasoning
Wonduk Seo, Wonseok Choi, Junseo Koh +7
Large Language Models (LLMs) increasingly support culturally sensitive decision making, yet often exhibit misalignment due to skewed pretraining data and the absence of structured…
MA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization
Wonduk Seo, Juhyeon Lee, Junseo Koh +6
Prompt optimization has become a practical way to improve the performance of Large Language Models (LLMs) without retraining. However, most existing frameworks treat evaluation as…
Automated Visualization Code Synthesis via Multi-Path Reasoning and Feedback-Driven Optimization
Wonduk Seo, Daye Kang, Hyunjin An +7
Large Language Models (LLMs) have become a cornerstone for automated visualization code generation, enabling users to create charts through natural language instructions. Despite i…
Question-to-Knowledge (Q2K): Multi-Agent Generation of Inspectable Facts for Product Mapping
Wonduk Seo, Taesub Shin, Hyunjin An +2
Identifying whether two product listings refer to the same Stock Keeping Unit (SKU) is a persistent challenge in ecommerce, especially when explicit identifiers are missing and pro…
Better by Comparison: Retrieval-Augmented Contrastive Reasoning for Automatic Prompt Optimization
Juhyeon Lee, Wonduk Seo, Hyunjin An +2
Automatic prompt optimization has recently emerged as a strategy for improving the quality of prompts used in Large Language Models (LLMs), with the goal of generating more accurat…