MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy. - Gihun Lee; SangMook Kim; Joonkee Kim; Se-Young Yun (Cell Segmentation Challenge @ NeurIPS, 2022)

Risk Perspective Exploration in Distributional Reinforcement Learning. - Jihwan Oh; Joonkee Kim; Se-Young Yun (CoRR, 2022)

The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions. - Mingyu Kim 002; Jihwan Oh; Yongsik Lee; Joonkee Kim; Seonghwan Kim 004; Song Chong; Se-Young Yun (CoRR, 2022)

Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning. - Jihwan Oh; Joonkee Kim; Minchan Jeong; Se-Young Yun (AAMAS, 2023)

HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning. - Yongjin Yang; Joonkee Kim; Yujin Kim; Namgyu Ho; James Thorne; Se-Young Yun (EMNLP (Findings), 2023)

PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning. - Hojoon Lee; Hanseul Cho 002; Hyunseung Kim; Daehoon Gwak; Joonkee Kim; Jaegul Choo; Se-Young Yun; Chulhee Yun (NeurIPS, 2023)

The StarCraft Multi-Agent Exploration Challenges: Learning Multi-Stage Tasks and Environmental Factors Without Precise Reward Functions. - Mingyu Kim 002; Jihwan Oh; Yongsik Lee; Joonkee Kim; Seonghwan Kim 004; Song Chong; Seyoung Yun (IEEE Access, 2023)

Enhancing Generalization and Plasticity for Sample Efficient Reinforcement Learning. - Hojoon Lee; Hanseul Cho 002; Hyunseung Kim; Daehoon Gwak; Joonkee Kim; Jaegul Choo; Se-Young Yun; Chulhee Yun (CoRR, 2023)

The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions. - Jun Ma 016; Ronald Xie; Shamini Ayyadhury; Cheng Ge; Anubha Gupta; Ritu Gupta; Song Gu; Yao Zhang 010; Gihun Lee; Joonkee Kim; Wei Lou; Haofeng Li; et al. (CoRR, 2023)

Distort, Distract, Decode: Instruction-Tuned Model Can Refine its Response from Noisy Instructions. - Taehyeon Kim 001; Joonkee Kim; Gihun Lee; Se-Young Yun (CoRR, 2023)

Re3val: Reinforced and Reranked Generative Retrieval. - EuiYul Song; Sangryul Kim; Haeju Lee; Joonkee Kim; James Thorne (EACL (Findings), 2024)

Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy Instructions. - Taehyeon Kim 001; Joonkee Kim; Gihun Lee; Se-Young Yun (ICLR, 2024)

EXAONE 3.0 7.8B Instruction Tuned Language Model. - Soyoung An; Kyunghoon Bae; Eunbi Choi; Stanley Jungkyu Choi; Yemuk Choi; Seokhee Hong; Yeonjung Hong; Junwon Hwang; Hyojin Jeon; Gerrard Jeongwon Jo; Hyunjik Jo; Jiyeon Jung; et al. (CoRR, 2024)

EXAONE 3.5: Series of Large Language Models for Real-world Use Cases. - LG AI Research; Soyoung An; Kyunghoon Bae; Eunbi Choi; Kibong Choi; Stanley Jungkyu Choi; Seokhee Hong; Junwon Hwang; Hyojin Jeon; Gerrard Jeongwon Jo; Hyunjik Jo; Jiyeon Jung; et al. (CoRR, 2024)

EXAONE Deep: Reasoning Enhanced Language Models. - LG AI Research; Kyunghoon Bae; Eunbi Choi; Kibong Choi; Stanley Jungkyu Choi; Yemuk Choi; Seokhee Hong; Junwon Hwang; Hyojin Jeon; Kijeong Jeon; Gerrard Jeongwon Jo; Hyunjik Jo; et al. (CoRR, 2025)

AdaSTaR: Adaptive Data Sampling for Training Self-Taught Reasoners. - Woosung Koh; Wonbeen Oh; Jaein Jang; Minhyung Lee; Hyeongjin Kim; Ah Yeon Kim; Joonkee Kim; Junghyun Lee; Taehyeon Kim 001; Se-Young Yun (CoRR, 2025)

EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modes. - Kyunghoon Bae; Eunbi Choi; Kibong Choi; Stanley Jungkyu Choi; Yemuk Choi; Kyubeen Han; Seokhee Hong; Junwon Hwang; Taewan Hwang; Joonwon Jang; Hyojin Jeon; Kijeong Jeon; et al. (CoRR, 2025)