7 papers
In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration
Youngbin Choi, Minjong Lee, Saemi Moon +4
LLM-generated drafts often contain subtle factual or logical errors, yet prior work shows that models struggle to reliably integrate multi-turn feedback aimed at fixing them. We pr…
Long Live the Librarian! A Persistent Search Sub-Agent for Energy-Efficient Multi-Agent Software Engineering Systems
Seunghyuk Cho, Sunghyun Choi, Jaeseung Heo +4
Multi-agent systems (MAS) have substantially advanced autonomous software engineering (SWE), but their growing inference energy demands raise sustainability concerns. In this paper…
Influence Functions for Edge Edits in Non-Convex Graph Neural Networks
Jaeseung Heo, Kyeongheung Yun, Seokwon Yoon +3
Understanding how individual edges influence the behavior of graph neural networks (GNNs) is essential for improving their interpretability and robustness. Graph influence function…
Posterior Label Smoothing for Node Classification
Jaeseung Heo, Moonjeong Park, Dongwoo Kim
Label smoothing is a widely studied regularization technique in machine learning. However, its potential for node classification in graph-structured data, spanning homophilic to he…
CoPL: Collaborative Preference Learning for Personalizing LLMs
Youngbin Choi, Seunghyuk Cho, Minjong Lee +4
Personalizing large language models (LLMs) is important for aligning outputs with diverse user preferences, yet existing methods struggle with flexibility and generalization. We pr…
The Oversmoothing Fallacy: A Misguided Narrative in GNN Research
MoonJeong Park, Sunghyun Choi, Jaeseung Heo +2
Oversmoothing has been recognized as a main obstacle to building deep Graph Neural Networks (GNNs), limiting the performance. This position paper argues that the influence of overs…