80 citations · 158 across the 31 of their papers we have counts for
10 papers · 1 filter
How Strongly Should Task State Influence an LLM Agent?
Chenyu Zhang, Wonbin Kweon, Jiawei Han
Long-horizon assigned work requires an LLM agent to track the state of a task: which steps are done, blocked, cancelled, or open to repetition. Agent systems either keep this state…
Personalized and Multi-View Representation for Federated Cold-Start Recommendation
Jaehyung Lim, Wonbin Kweon, Woojoo Kim +3
Federated recommendation (FedRec) enables personalized modeling without centralizing users' interaction histories, but most existing methods assume a fixed item pool and thus overl…
SCoRD: Semantic-Assisted Continual Retriever-Reranker Distillation for LLM-Based Recommendation
Seunghyun Baek, Gyuseok Lee, Seunghan Lee +3
Recommendation systems increasingly adopt a two-stage pipeline, where an ID-based retriever retrieves candidates and an LLM-based reranker refines their rankings. To improve retrie…
From Overlooked to Explored: Recovering Item Relations via Mixture of Perspectives for Sequential Recommendation
Junyoung Kim, Wonbin Kweon, Woojoo Kim +3
Capturing user preference from a user's interaction sequence is the central challenge of Sequential Recommendation (SR). This preference intuitively emerges from inter-item relatio…
MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry
Joey Chan, Wonbin Kweon, Ashley Shin +4
Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representation…
Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization
Gyuseok Lee, Wonbin Kweon, Zhenrui Yue +3
Reward factorization personalizes large language models (LLMs) by decomposing rewards into shared basis functions and user-specific weights. Yet, existing methods estimate user wei…