Hello! I am a Researcher in Advanced Agent Lab at LG AI Research (Ann Arbor, Michigan), working with Prof. Honglak Lee. I received my PhD in Computer Science at Seoul National University (advisors: Prof. Gunhee Kim and Prof. Hyun Oh Song).

My research interests mainly lie in building capable AI agents for decision-making in challenging, real-world tasks, with language and multimodal models and reinforcement learning.

Selected Professional Experiences

• LG AI Research (Ann Arbor, Michigan) (Aug. 2023 - Present)
  • Researcher in Advanced Agent Lab
  • Manager: Prof. Honglak Lee

Publications (*: equal contribution)

Language and Multimodal Models and Agents

	MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges? Yunxiang Zhang, Muhammad Khalifa, Shitanshu Bhushan, Grant D Murphy, Lajanugen Logeswaran, Jaekyeom Kim, Moontae Lee, Honglak Lee, Lu Wang NeurIPS 2025 Datasets and Benchmarks Track (accepted) [arxiv]
	Scaling Web Agent Training through Automatic Data Generation and Fine-grained Evaluation Lajanugen Logeswaran, Jaekyeom Kim, Sungryull Sohn, Creighton Glasscock, Honglak Lee COLM 2025 [paper]
	Process Reward Models That Think Muhammad Khalifa, Rishabh Agarwal, Lajanugen Logeswaran, Jaekyeom Kim, Hao Peng, Moontae Lee, Honglak Lee, Lu Wang Workshop on Test-time Scaling and Reasoning Models at COLM 2025 [arxiv]
	Beyond Blind Following: Evaluating Robustness of LLM Agents under Imperfect Guidance Yao Fu, Ran Qiu, Xinhe Wang, Jacob Sansom, Sathvika Ayyappa Prabhu, Huijie Tang, Jaekyeom Kim, Sungryull Sohn, Honglak Lee Workshop on AI Agents: Capabilities and Safety at COLM 2025
Our large-scale analysis with our AI agent suggests that ~80% of the popular datasets with commercially permissive licenses are, in fact, not likely commercially viable due to how those datasets were constructed.	Do Not Trust Licenses You See: Dataset Compliance Requires Massive-Scale AI-Powered Lifecycle Tracing Jaekyeom Kim*, Sungryull Sohn*, Gerrard Jeongwon Jo, Jihoon Choi, Kyunghoon Bae, Hwayoung Lee, Yongmin Park, Honglak Lee Preprint [arxiv] [post] [project]
	Interactive and Expressive Code-Augmented Planning with Large Language Models Anthony Z. Liu, Xinhe Wang, Jacob Sansom, Yao Fu, Jongwook Choi, Sungryull Sohn, Jaekyeom Kim, Honglak Lee ACL 2025 [paper] [arxiv]
	AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents Yao Fu*, Dong-Ki Kim*, Jaekyeom Kim, Sungryull Sohn, Lajanugen Logeswaran, Kyunghoon Bae, Honglak Lee NeurIPS 2024 [paper] [arxiv]
	Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents Jaekyeom Kim, Dong-Ki Kim, Lajanugen Logeswaran, Sungryull Sohn, Honglak Lee EMNLP 2024 (Findings) [paper] [arxiv] [poster]
	SkillAct: Using Skill Abstractions Improves LLM Agents Anthony Z. Liu, Jongwook Choi, Sungryull Sohn, Yao Fu, Jaekyeom Kim, Dong-Ki Kim, Xinhe Wang, Jaewon Yoo, Honglak Lee ICML 2024 Workshop on LLMs and Cognition [paper]
	Small Language Models Need Strong Verifiers to Self-Correct Reasoning Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim, Moontae Lee, Honglak Lee, Lu Wang ACL 2024 (Findings) [paper] [arxiv]

Reinforcement Learning and Skill Discovery

	Constrained GPI for Zero-Shot Transfer in Reinforcement Learning Jaekyeom Kim, Seohong Park, Gunhee Kim NeurIPS 2022 [paper] [arxiv] [talk] [code]
	Lipschitz-constrained Unsupervised Skill Discovery Seohong Park, Jongwook Choi*, Jaekyeom Kim*, Honglak Lee, Gunhee Kim ICLR 2022 [paper] [arxiv] [project] [code]
	Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods Seohong Park, Jaekyeom Kim, Gunhee Kim NeurIPS 2021 [paper] [appx] [arxiv] [talk] [code]
	Unsupervised Skill Discovery with Bottleneck Option Learning Jaekyeom Kim*, Seohong Park*, Gunhee Kim ICML 2021 [paper] [appx] [arxiv] [talk] [code]
	EMI: Exploration with Mutual Information Hyoungseok Kim*, Jaekyeom Kim*, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song ICML 2019 (Long talk: top ~4.6%) [paper] [supp] [arxiv] [talk] [code]

Generalization and Robustness

	Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration Jaekyeom Kim, Minjung Kim, Dongyeon Woo, Gunhee Kim ICLR 2021 [paper] [arxiv] [talk] [code]
	Model-Agnostic Boundary-Adversarial Sampling for Test-Time Generalization in Few-Shot Learning Jaekyeom Kim, Hyoungseok Kim, Gunhee Kim ECCV 2020 (Oral: top ~2%) [paper] [appx] [talk] [code]

Honors & Awards

• PhD Dissertation Award (Aug. 2023, Dept. of Computer Science and Engineering, Seoul National University)
• Star Student Researcher Award (Feb. 2023, BK21 Intelligence Computing, Seoul National University)
• Youlchon AI Star Fellowship (Jul. 2022, Youlchon Foundation)
• Naver PhD Fellowship (Dec. 2021, Naver)
• Google PhD Fellowship - Area: Machine Learning (Sep. 2021, Google)
• Samsung Humantech Paper Award - Silver Prize in Signal Processing (Feb. 2021, Samsung Electronics)
• Qualcomm Innovation Fellowship Korea (Dec. 2020, Qualcomm AI Research)
• On-Dream Outstanding Scholar Award (Dec. 2020, Hyundai Motor Chung Mong-Koo Foundation)
• On-Dream Future Talent Graduate Scholarship (Jul. 2020, Hyundai Motor Chung Mong-Koo Foundation)
• Kwanjeong Domestic Scholarship (Apr. 2018, Kwanjeong Educational Foundation)
• Summa Cum Laude Honor (Feb. 2018, Korea Advanced Institute of Science and Technology)

Education

• Seoul National University (SNU) (Mar. 2018 - Aug. 2023)
  • PhD in Computer Science and Engineering
  • Advisors: Prof. Gunhee Kim and Prof. Hyun Oh Song
• Korea Advanced Institute of Science and Technology (KAIST) (Feb. 2010 - Jun. 2017)
  • BS in Computer Science
  • Graduated summa cum laude