Sangeun Park, Minhae Kwon, "Multi^2: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments," International Conference on Machine Learning (ICML), July 2026. (BK21+ IF: 4, Acceptance rate: 26.6%) [Project Page]
Sangeun Park, Chanin Eom, Minhae Kwon, "Offline-to-Online Reinforcement Learning for Driving Style-aware Policy Adaptation in Mixed Traffic," IEEE Transactions on Intelligent Transportation Systems, under review. (Rating: Q1, IF 10.4, Rank: 5/183, Top 2.5%)
Sangeun Park*, Guhyeon Kang*, Minhae Kwon, Anonymous submission–multi-agent LLM, Top AI Conference, under review. (*equal contribution) (BK21+ IF: 4)
Sangeun Park, Hyunggon Park*, Minhae Kwon*, "Autonomous Vehicle Leadership as Networked AI: A Stackelberg Game-Theoretic Analysis in Mixed Traffic," IEEE Journal of Selected Topics in Signal Processing, under review. (*co-corresponding authors) (Rating: Q1, IF: 11.6, Rank: 3/122, Top 2.5%)
Sangeun Park, Minhae Kwon, “Drift-Aware Coordination for Long-Horizon LLM-Based Multi-Agent Systems,” International Conference on Machine Learning (ICML) WiML Workshop, July 2026.
Hongki Kim, Sangeun Park, Minhae Kwon, “Adaptive Action Chunking Strategy from World Feedback in Mixed Traffic,” International Conference on Machine Learning (ICML) RLxF: Reinforcement Learning from World Feedback Workshop, July 2026.
Hongki Kim, Sangeun Park, Minhae Kwon, “UA2C: Uncertainty-Aware Adaptive Action Chunking for Offline-to-Online Decision-Making in Mixed Traffic,” International Conference on Machine Learning (ICML) Workshop on Decision-Making from Offline Datasets to Online Adaptation: Black-Box Optimization to Reinforcement Learning, July 2026.
Sangeun Park*, Guhyeon Kang*, Minhae Kwon, Improving Multi-Agent Coordination with a Drift-Aware RL Objective, International Conference on Machine Learning (ICML) Workshop on Decision-Making from Offline Datasets to Online Adaptation: Black-Box Optimization to Reinforcement Learning, July 2026. (*equal contribution)
Sangeun Park, Minhae Kwon, "Multi-Agent LLMs with Offline Reinforcement Learning for Hierarchical Multi-turn Decision-Making," Conference on Neural Information Processing Systems (NeurIPS) Efficient Reasoning Workshop, December 2025.
Sangeun Park, Minhae Kwon, "Hierarchical Decision-making via Multi-turn Reinforcement Learning," Conference on Neural Information Processing Systems (NeurIPS) Women in Machine Learning Workshop, December 2025. (Contributed Talk) [Video]
Sangeun Park, Chanin Eom, Jaehwi Lee, Dongsu Lee, Minhae Kwon, "Enhancing Traffic Flow in On-ramp Merging Through Autonomous Vehicles Based on Deep Reinforcement Learning," IEEE International Conference on Robotics and Automation (IEEE ICRA) HRI Workshop, May 2024. (Spotlight Talk) [Video] [Media]
Sangeun Park, Chanin Eom, Minhae Kwon, "xLSTM-based Noise-robust Driving Characteristic Inference Network for V2I Systems," Transactions of the Korean Society of Automotive Engineers (T-KSAE), vol.33, no.07, pp.487-502, July 2025. [Media]
Sangeun Park, Chanin Eom, Minhae Kwon, "Inference on Driving Characteristic Based on Time-series Partial Observation of Vehicle," The Journal of Korean Institute of Communications and Information Sciences (J-KICS), vol.50, no.06, pp.858-874, June 2025. (Best Paper Award) [Media]
Hongki Kim, Sangeun Park, Minhae Kwon, "Decision-Making Based on Decision Transformer in Space Reward Environment," Joint Conference on Communications and Information (JCCI), April 2026.
Sangeun Park, Chanin Eom, Minhae Kwon, "V2I-Based Driving Characteristics Inference Model," Korean Society of Automotive Engineers (KSAE) Fall Conference, November 2024. (Best Paper Award) [Media]
Sangeun Park, Chanin Eom, Minhae Kwon, "Inference on Driving Characteristics with Partially Observed Trajectory Datasets," Korean Society of Automotive Engineers (KSAE) Fall Conference, November 2024. [Video]
Sangeun Park, Chanin Eom, Dongsu Lee, Minhae Kwon, "Enhancing Traffic Flow in On-ramp Merging Through DDPG-based Autonomous Vehicle," Joint Conference on Communications and Information (JCCI), April 2024. [Video]