Seongheon Park
Contact. seongheon_park [at] cs [dot] wisc [dot] edu | CV
1210 W Dayton St
Madison, WI 53706
Hello! I’m a second-year PhD student in the Computer Sciences department at the University of Wisconsin-Madison, where I am fortunate to be advised by Prof. Sharon Li. Previously, I completed my MS degree at Yonsei University in the Electrical and Electronic Engineering department under the supervision of Prof. Kwanghoon Sohn and Prof. Kibok Lee.
My research focuses on making foundation models (LLMs, LVLMs, and VLAs) safe and reliable in real-world deployment. Specifically, I study why and how these models fail through:
- Detecting and mitigating erroneous generation
- Reasoning about failure modes
- Latent space interpretability
These are crucial for preventing catastrophic failures in human-facing and industrial applications, and for enabling robust agentic systems to stop, replan, and incorporate human intervention in an interpretable way. I am also broadly interested in multimodal models and post-training.
news
| May 18, 2026 | I will be joining Microsoft Research Tokyo this May as a research intern, working on Embodied AI agents |
|---|---|
| Jan 18, 2026 | Excited to share that our paper has been accepted at ICLR 2026! |
| Sep 18, 2025 | Happy to share that two of our papers have been accepted at NeurIPS 2025 |
| Sep 04, 2025 | Our paper HalluEntity: Benchmarking and Understanding Entity-Level Hallucination Detection is accepted by TMLR |
| Jul 02, 2025 | Our paper about LVLM object hallucnation detection is accepted by ICML 2025 Workshop on Reliable and Responsible Foundation Models |
publications
- Preprint
Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and OpportunitiesarXiv:2602.05073, Feb 2026 - Preprint
VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-EvaluationarXiv:2602.21054, Jan 2026 - ICLR
Understanding Language Prior of LVLMs by Contrasting Chain-of-EmbeddingInternational Conference on Learning Representations, Jan 2026 - Preprint
Shaking to Reveal: Perturbation-Based Detection of LLM HallucinationsarXiv:2506.02696, Jun 2025 - NeurIPS
GLSim: Detecting Object Hallucinations in LVLMs via Global-Local SimilarityConference on Neural Information Processing Systems, Jul 2025 - NeurIPS
GeoRanker: Distance-Aware Ranking for Worldwide Image GeolocalizationConference on Neural Information Processing Systems, May 2025 - ICML
Steer LLM Latents for Hallucination DetectionInternational Conference on Machine Learning, May 2025 - ICML
Position: Challenges and Future Directions of Data-Centric AI AlignmentInternational Conference on Machine Learning, May 2025 - TMLR
HalluEntity: Benchmarking and Understanding Entity-Level Hallucination DetectionTransactions on Machine Learning Research, Mar 2025 - CVPR Workshop
Rethinking Open-World Semi-Supervised Learning: Distribution Mismatch and Inductive InferenceCVPR Workshop on Computer Vision in the Wild, Jun 2024 - ICCV
Hierarchical visual primitive experts for compositional zero-shot learningIEEE/CVF International Conference on Computer Vision, Oct 2023 - CVPR
Partmix: Regularization strategy to learn part discovery for visible-infrared person re-identificationIEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2023 - WACV
Normality guided multiple instance learning for weakly supervised video anomaly detectionIEEE/CVF Winter Conference on Applications of Computer Vision, Jan 2023 - WACV
Language-free training for zero-shot video groundingIEEE/CVF Winter Conference on Applications of Computer Vision, Jan 2023