2026

1. Machine Learning Hamiltonians are Accurate Energy-Force Predictors

   Seongsu Kim, Chanhui Lee, Yoonho Kim, Seongjun Yun, Honghui Kim, Nayoung Kim, Changyoung Park, Sehui Han, Sungbin Lim, and Sungsoo Ahn

   [arxiv / code]
2. Boltz is a Strong Baseline for Atom-level Representation Learning

   Hyosoon Jang, Hyunjin Seo, Yunhui Jang, Seonghyun Park, and Sungsoo Ahn

   [arxiv]
3. Riemannian MeanFlow

   Dongyeop Woo, Marta Skreta, Seonghyun Park, Kirill Neklyudov, and Sungsoo Ahn

   [arxiv]
4. Progressive Multi-Agent Reasoning for Biological Perturbation Prediction

   Hyomin Kim, Sang-Yeon Hwang, Jaechang Lim, Yinhua Piao, Yunhak Oh, Woo Youn Kim, Chanyoung Park, Sungsoo Ahn, and Junhyeok Jeon

   [arxiv]
5. AtomMOF: All-Atom Flow Matching for MOF-Adsorbate Structure Prediction

   Nayoung Kim, Honghui Kim, Sihyun Yu, Minkyu Kim, Seongsu Kim, and Sungsoo Ahn

   [arxiv]
6. CatFlow: Co-generation of Slab-Adsorbate Systems via Flow Matching

   Minkyu Kim, Nayoung Kim, Honghui Kim, and Sungsoo Ahn

   [arxiv]
7. INDIBATOR: Diverse and Fact-Grounded Individuality for Multi-Agent Debate in Molecular Discovery

   Yunhui Jang, Seonghyun Park, Jaehyung Kim, and Sungsoo Ahn

   [arxiv]
8. Latent Veracity Inference for Identifying Errors in Stepwise Reasoning (ICLR 2026)

   Minsu Kim, Jean-Pierre R. Falet, Oliver Ethan Richardson, Xiaoyin Chen, Moksh Jain, Sungjin Ahn, Sungsoo Ahn, and Yoshua Bengio

   [arxiv]
9. Learning Flexible Forward Trajectories for Masked Molecular Diffusion (ICLR 2026)

   Hyunjin Seo^*, Taewon Kim^*, Sihyun Yu, and Sungsoo Ahn

   [arxiv / project]
10. Learning Collective Variables from BioEmu with Time-Lagged Generation (ICLR 2026)

    Seonghyun Park, Kiyoung Seong, Soojung Yang, Rafael Gomez-Bombarelli, and Sungsoo Ahn

    [arxiv]
11. DNACHUNKER: Learnable Tokenization for DNA Language Models

    Taewon Kim, Jihwan Shin, Hyomin Kim, Youngmok Jung, Jonhoon Lee, Won-Chul Lee, Insu Han^†, and Sungsoo Ahn^†

    [arxiv]

2025

1. Energy-based Generator Matching: A Neural Sampler for General State Space (NeurIPS 2025)

   Dongyeop Woo, Minsu Kim, Minkyu Kim, Kiyoung Seong, and Sungsoo Ahn

   [arxiv / code]
2. On Scalable and Efficient Training of Diffusion Samplers (NeurIPS 2025)

   Minkyu Kim, Kiyoung Seong, Dongyeop Woo, Sungsoo Ahn, and Minsu Kim

   [arxiv / code]
3. Flexible MOF Generation with Torsion-Aware Flow Matching (NeurIPS 2025)

   Nayoung Kim, Seongsu Kim, and Sungsoo Ahn

   [arxiv / code]
4. High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Prediction (NeurIPS 2025)

   Seongsu Kim, Nayoung Kim, Dongwoo Kim, and Sungsoo Ahn

   [arxiv / code]
5. Self-Training Large Language Models with Confident Reasoning (EMNLP 2025)

   Hyosoon Jang, Yunhui Jang, Sungjae Lee, Jungseul Ok, and Sungsoo Ahn

   [arxiv]
6. MT-Mol: Multi Agent System with Tool-based Reasoning for Molecular Optimization (EMNLP 2025)

   Hyomin Kim, Yunhui Jang, and Sungsoo Ahn

   [arxiv]
7. Improving Chemical Understanding of LLMs via SMILES Parsing (EMNLP 2025)

   Yunhui Jang, Jaehyung Kim, and Sungsoo Ahn

   [arxiv / code]
8. Enhancing LLM Agent Safety via Causal Influence Prompting (ACL 2025)

   Dongyoon Hahm, Woogyeol Jin, June Suk Choi, Sungsoo Ahn, and Kimin Lee

   [arxiv / code]
9. Structural Reasoning Improves Molecular Understanding of LLM (ACL 2025)

   Yunhui Jang, Jaehyung Kim, and Sungsoo Ahn

   [arxiv / code]
10. Generative Flows on Synthetic Pathway for Drug Design (ICLR 2025)

    Seonghwan Seo, Minsu Kim, Tony Shen, Martin Ester, Jinkyoo Park, Sungsoo Ahn, and Woo Youn Kim

    [arxiv]
11. MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks (ICLR 2025)

    Nayoung Kim, Seongsu Kim, Minsu Kim, Jinkyoo Park, and Sungsoo Ahn

    [arxiv / code]
12. Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers (ICLR 2025)

    Kiyoung Seong, Seonghyun Park, Seonghwan Kim, Woo Youn Kim, and Sungsoo Ahn

    [arxiv / code / project]
13. ReBind: Enhancing Ground-state Molecular Conformation Prediction via Force-Based Graph Rewiring (ICLR 2025)

    Taewon Kim^*, Hyunjin Seo^*, Sungsoo Ahn, and Eunho Yang

    [arxiv / code]
14. Adaptive Teachers for Amortized Samplers (ICLR 2025)

    Minsu Kim, Sanghyeok Choi, Taeyoung Yun, Emmanuel Bengio, Leo Feng, Jarrid Rector-Brooks, Sungsoo Ahn, Jinkyoo Park, Nikolay Malkin, and Yoshua Bengio

    [arxiv]

2024

1. Decoupled Sequence and Structure Generation for Realistic Antibody Design (TMLR 2024)

   Nayoung Kim, Minsu Kim, Sungsoo Ahn, and Jinkyoo Park

   [paper / arxiv / code]
2. Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity

   Hyosoon Jang, Yunhui Jang, Jaehyung Kim, and Sungsoo Ahn

   [arxiv]
3. Iterated Energy-based Flow Matching for Sampling from Boltzmann Densities

   Dongyeop Woo and Sungsoo Ahn

   [arxiv]
4. Non-backtracking Graph Neural Networks (TMLR 2024)

   Seonghyun Park^*, Narae Ryu^*, Gahee Kim, Dongyeop Woo, Se-Young Yun^†, and Sungsoo Ahn^†

   [paper / arxiv / code]
5. Pessimistic Backward Policy for GFlowNets (NeurIPS 2024)

   Hyosoon Jang, Yunhui Jang, Minsu Kim, Jinkyoo Park, and Sungsoo Ahn

   [paper / arxiv / code]
6. Hybrid Neural Representation for Spherical Data (ICML 2024)

   Hyomin Kim, Yunhui Jang, Jaeho Lee, and Sungsoo Ahn

   [paper / arxiv]
7. Gaussian Plane-Wave Neural Operator for Electron Density Estimation (ICML 2024)

   Seongsu Kim and Sungsoo Ahn

   [paper / arxiv / code]
8. Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization (ICML 2024)

   Nayeong Kim, Juwon Kang, Sungsoo Ahn, Jungseul Ok, and Suha Kwak

   [paper / code]
9. Enhancing Sample Efficiency in Black-box Combinatorial Optimization via Symmetric Replay Training (ICML 2024)

   Hyeonah Kim, Minsu Kim, Sungsoo Ahn, and Jinkyoo Park

   [arxiv / code]
10. Tackling Complex Conditions in Unsupervised Combinatorial Optimization (ICML 2024)

    Fanchen Bu, Hyeonsoo Jo, Soo Yong Lee, Sungsoo Ahn, and Kijung Shin

    [paper / arxiv / code]
11. Breadth-First Exploration in Adaptive Grid-based Reinforcement Learning (ICML 2024)

    Youngsik Yoon, Gangbok Lee, Sungsoo Ahn, and Jungseul Ok

    [paper / code / project]
12. Holistic Molecular Representation Learning via Multi-view Fragmentation (TMLR 2024)

    Seojin Kim, Jaehyun Nam, Junsu Kim, Hankook Lee, Sungsoo Ahn, and Jinwoo Shin

    [paper / code]
13. EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost (IJCAI 2024)

    Jaeseung Heo, Seungbeom Lee, Sungsoo Ahn, and Dongwoo Kim

    [paper / arxiv]
14. Learning Energy Decompositions for Partial Inference in GFlowNets (ICLR 2024)

    Hyosoon Jang, Minsu Kim, and Sungsoo Ahn

    [paper / arxiv / code]
15. A Simple and Scalable Representation for Graph Generation (ICLR 2024)

    Yunhui Jang, Seul Lee, and Sungsoo Ahn

    [paper / arxiv / code]
16. Graph Generation with K^2 Trees (ICLR 2024)

    Yunhui Jang, Dongwoo Kim, and Sungsoo Ahn

    [paper / arxiv / code]
17. Local Search GFlowNets (ICLR 2024)

    Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, and Jinkyoo Park

    [paper / arxiv / code]

2023

1. Multi-resolution Spectral Coherence for Graph Generation with Score-based Diffusion (NeurIPS 2023)

   Hyuna Cho, Minjae Jeong, Sooyeon Jeon, Sungsoo Ahn, and Won Hwa Kim

   [paper]
2. Diffusion Probabilistic Models for Structured Node Classification (NeurIPS 2023)

   Hyosoon Jang, Seonghyun Park, Sangwoo Mo, and Sungsoo Ahn

   [paper / arxiv / code]
3. Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences (NeurIPS 2023)

   Minsu Kim, Federico Berto, Sungsoo Ahn, and Jinkyoo Park

   [paper / arxiv / code]
4. A Closer Look at the Intervention Procedure of Concept Bottleneck Models (ICML 2023)

   Sungbin Shin, Yohan Jo, Sungsoo Ahn, and Namhoon Lee

   [paper / arxiv / code]
5. Imitating Graph-Based Planning with Goal-Conditioned Policies (ICLR 2023)

   Junsu Kim, Younggyo Seo, Sungsoo Ahn, Kyunghwan Son, and Jinwoo Shin

   [paper / arxiv / code]

2022

1. Learning Debiased Classifier with Biased Committee (NeurIPS 2022)

   Nayeong Kim, Sehyun Hwang, Sungsoo Ahn, Jaesik Park, and Suha Kwak

   [paper / arxiv / code]
2. Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning (ICML 2022)

   Kyunghwan Son, Junsu Kim, Sungsoo Ahn, Roben Delos Reyes, Yung Yi, and Jinwoo Shin

   [paper]
3. What Makes Better Augmentation Strategies? Augment Difficult but Not Too Different (ICLR 2022)

   Jaehyung Kim, Dongyeop Kang, Sungsoo Ahn, and Jinwoo Shin

   [paper / code]
4. Spanning Tree-based Graph Generation for Molecules (ICLR 2022)

   Sungsoo Ahn, Binghong Chen, Tianzhe Wang, and Le Song

   [paper]

2021

1. RoMA: Robust Model Adaptation for Offline Model-based Optimization (NeurIPS 2021)

   Sihyun Yu, Sungsoo Ahn, Le Song, and Jinwoo Shin

   [arxiv / code]
2. Self-Improved Retrosynthetic Planning (ICML 2021)

   Junsu Kim, Sungsoo Ahn, Hankook Lee, and Jinwoo Shin

   [paper / arxiv / code]
3. RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning (IJCAI 2021)

   Hankook Lee, Sungsoo Ahn, Seung-Woo Seo, You Young Song, Eunho Yang, Sung Ju Hwang, and Jinwoo Shin

   [paper / arxiv / code]
4. Layer-adaptive sparsity for the Magnitude-based Pruning (ICLR 2021)

   Jaeho Lee, Sejun Park, Sangwoo Mo, Sungsoo Ahn, and Jinwoo Shin

   [paper / arxiv / code]

2020

1. Learning from Failure: Training Debiased Classifier from Biased Classifier (NeurIPS 2020)

   Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, and Jinwoo Shin

   [paper / arxiv / code]
2. Guiding Deep Molecular Optimization with Genetic Exploration (NeurIPS 2020)

   Sungsoo Ahn, Junsu Kim, Hankook Lee, and Jinwoo Shin

   [paper / arxiv / code]
3. Learning What to Defer for Maximum Independent Sets (ICML 2020)

   Sungsoo Ahn, Younggyo Seo, and Jinwoo Shin

   [paper / arxiv / code]

2019

1. Variational Information Distillation for Knowledge Transfer (CVPR 2019)

   Sungsoo Ahn, Shell Hu, Andreas Damianou, Neil Lawrence, and Zhenwen Dai

   [paper / arxiv / code]
2. Bucket-Renormalization for Approximate Inference (JSTAT 2019)

   Sungsoo Ahn, Michael Chertkov, Adrian Weller, and Jinwoo Shin

   [paper / arxiv / code]
3. Gauging Variational Inference (JSTAT 2019)

   Sungsoo Ahn, Michael Chertkov, and Jinwoo Shin

   [paper / arxiv]
4. Gauging Variational Inference (NeurIPS 2019)

   Sungsoo Ahn, Michael Chertkov, and Jinwoo Shin

   [paper / arxiv]

2018

1. Bucket-Renormalization for Approximate Inference (ICML 2018)

   Sungsoo Ahn, Michael Chertkov, Adrian Weller, and Jinwoo Shin

   [paper / arxiv / code]
2. Gauged Mini-Bucket Elimination for Approximate Inference (AISTATS 2018)

   Sungsoo Ahn, Michael Chertkov, Jinwoo Shin, and Adrian Weller

   [paper / arxiv / code]
3. Maximum Weight Matching using Odd-sized Cycles: Max-Product Belief Propagation and Half-Integrality (IEEE TIT 2018)

   Sungsoo Ahn, Michael Chertkov, Andrew E. Gelfand, Sejun Park, and Jinwoo Shin

   [paper]

2016

1. Synthesis of MCMC and Belief Propagation (NeurIPS 2016)

   Sungsoo Ahn, Michael Chertkov, and Jinwoo Shin

   [paper / arxiv]

2015

1. Minimum Weight Perfect Matching via Blossom Belief Propagation (NeurIPS 2015)

   Sungsoo Ahn, Sejun Park, Michael Chertkov, and Jinwoo Shin

   [paper / arxiv]