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Seohui Bae
배서희
I'm a research scientist at LG AI Research in South Korea.
At LG AI Research, I'm working on AI systems that model the world, make decisions, and adapt under uncertainty. I completed my bachelor's and master's studies at KAIST, where I was fortunate to be advised by Prof. Eunho Yang.
Email /
Google Scholar /
LinkedIn
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News
- (26.05) I will attend ICML 2026 in Seoul 🇰🇷
- (26.02) 1 paper accepted to CVPR 2026. See you all in Denver 🇺🇸
- (26.01) 1 paper accepted to ICASSP 2026.
- (25.07) I will be attending ICML 2025 this July. See you in Vancouver 🇨🇦
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Research
My research focuses on building AI agents that can model, decide, and adapt in complex environments. I study how agents can use executable dynamics, grounded environments, and model-based exploration to make robust decisions under uncertainty. My work spans three complementary directions:
- Agent Decision-Making: RL and planning methods for intelligent agents in dynamic environments, with a focus on exploration and information-seeking behavior. [P2, P3, C1]
- World Models & Agent Environments: Building grounded, scalable environments and learning predictive models of dynamics for simulation and model-based reasoning. [P3, P4]
- Robust Learning under Distribution Shift: Learning algorithms for reliable generalization under changing distribution. [P1]
I regularly contribute to academic publications and collaborative research projects.
I’m especially interested in bridging industrial challenges with generalizable solutions in: RL post-training, world models, and environment scaling.
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Selected Publications
(* equal contribution; † corresponding author). For the full list, see Google Scholar.
[C#] conferences; [P#] preprints/under review
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[P4] PCBWorld: A Benchmark Environment for Engine-Grounded PCB Design Automation
[project]
Hyungseok Song*, Junseok Park*, Won-Seok Choi*, Seohui Bae, Han-Seul Jeong, Youngjoon Park†, Soonyoung Lee†
under review
KDD Workshop on Evaluation and Trustworthiness of Agentic AI, 2026
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[P3] Code-to-Explore: Verifiable Programs for OOD Exploration in LLM Agents
[pdf]
Seohui Bae, Whiyoung Jung, Youngjoon Park, Soonyoung Lee
under review
ICML Workshop on Reinforcement Learning on World Feedback, 2026
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[P2] Align as Act: Innovations-Based Reward Decomposition for LLM Agents
[pdf]
Sojeong Rhee*, Seohui Bae*, Whiyoung Jung, Woohyung Lim, Soonyoung Lee, Youngchul Sung
preprint, 2026
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[C1] Align While Search: Belief-Guided Exploratory Inference for World-Grounded Embodied Agents
[pdf]
Seohui Bae, Jeonghye Kim, Youngchul Sung, Woohyung Lim
Conference on Computer Vision and Pattern Recognition (CVPR), 2026
ICML Workshop on Exploration in AI Today, 2025
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[P1] Geometry-Aware Normalization for Imbalanced Time-series Forecasting
[pdf]
Seohui Bae, Junhyeok Kang, Jun Seo, Soyeon Park, Wonbin Ahn, Soonyoung Lee
under review (journal), 2025
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Projects
Current @ LG AI Research
- RL for Foundation Model Agents
- Post-training methods for LLM-based agents in industrial decision settings.
- Industrial Optimization
- RL for sequential decision-making in manufacturing.
- Agent training and benchmark for electronic design automation.
Past @ LG AI Research
- EXAONE-Futurecast
- Demand Forecasting
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Academic Service
Conference Reviewer
- Main: ICML, NeurIPS, ICLR, AAAI, AISTATS
- Workshops: AAAI, ICLR, ICML
Journal Reviewer
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Last date of update: 2026-05-16 / template
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