5 citations · 5 across the 4 of their papers we have counts for
6 papers
Conversation Is a Two-Body Problem: Dyadic Evaluation of Full-Duplex Dialogue Models
Sungnyun Kim, Sungwoo Cho, Jihwan Oh +1
Full-duplex spoken dialogue models listen and speak at the same time, enabling voice agents to have natural, low-latency interactions that turn-based systems cannot offer. However,…
Efficient Parametric SVD of Koopman Operator for Stochastic Dynamical Systems
Minchan Jeong, J. Jon Ryu, Se-Young Yun +1
The Koopman operator provides a principled framework for analyzing nonlinear dynamical systems through linear operator theory. Recent advances in dynamic mode decomposition (DMD) h…
From Belief Entrenchment to Robust Reasoning in LLM Agents
Jihwan Oh, Minchan Jeong, Jongwoo Ko +1
Multi-Agent Debate (MAD) has emerged as a promising inference scaling method for Large Language Model (LLM) reasoning. However, it frequently suffers from belief entrenchment, wher…
FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning
Seongyoon Kim, Minchan Jeong, Sungnyun Kim +3
Federated Learning (FL) has emerged as a pivotal framework for the development of effective global models (global FL) or personalized models (personalized FL) across clients with h…
Preference Alignment with Flow Matching
Minu Kim, Yongsik Lee, Sehyeok Kang +3
We present Preference Flow Matching (PFM), a new framework for preference-based reinforcement learning (PbRL) that streamlines the integration of preferences into an arbitrary clas…
Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning
Haeju Lee, Minchan Jeong, Se-Young Yun +1
Prompt tuning, in which prompts are optimized to adapt large-scale pre-trained language models to downstream tasks instead of fine-tuning the full model parameters, has been shown…