Fang Wu

According to our database, Fang Wu authored at least 35 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2025

From Supervision to Exploration: What Does Protein Language Model Learn During Reinforcement Learning?

Hanqun Cao, Hongrui Zhang, Junde Xu, Zhou Zhang, Lingdong Shen, Minghao Sun, Ge Liu, Jinbo Xu, Wu-Jun Li, Jinren Ni, Cesar de la Fuente-Nunez, Tianfan Fu, Yejin Choi, Pheng-Ann Heng, Fang Wu

CoRR, October, 2025

DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search.

Fang Wu, Weihao Xuan, Heli Qi, Ximing Lu, Aaron Tu, Li Erran Li, Yejin Choi

CoRR, September, 2025

Diagnose, Localize, Align: A Full-Stack Framework for Reliable LLM Multi-Agent Systems under Instruction Conflicts.

Guancheng Wan, Leixin Sun, Longxu Dou, Zitong Shi, Fang Wu, Eric Hanchen Jiang, Wenke Huang, Guibin Zhang, Hejia Geng, Xiangru Tang, Zhenfei Yin, Yizhou Sun, Wei Wang

CoRR, September, 2025

Multiplayer Nash Preference Optimization.

Fang Wu, Xu Huang, Weihao Xuan, Zhiwei Zhang, Yijia Xiao, Guancheng Wan, Xiaomin Li, Bing Hu, Peng Xia, Jure Leskovec, Yejin Choi

CoRR, September, 2025

Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards.

Aaron Tu, Weihao Xuan, Heli Qi, Xu Huang, Qingcheng Zeng, Shayan Talaei, Yijia Xiao, Peng Xia, Xiangru Tang, Yuchen Zhuang, Bing Hu, Hanqun Cao, Wenqi Shi, Tianang Leng, Rui Yang, Yingjian Chen, Ziqi Wang, Irene Li, Nan Liu, Huaxiu Yao, Li Erran Li, Ge Liu, Amin Saberi, Naoto Yokoya, Jure Leskovec, Yejin Choi, Fang Wu

CoRR, September, 2025

A deep reinforcement learning platform for antibiotic discovery.

Hanqun Cao, Marcelo Der Torossian Torres, Jingjie Zhang, Zijun Gao, Fang Wu, Chunbin Gu, Jure Leskovec, Yejin Choi, Cesar de la Fuente-Nunez, Guangyong Chen, Pheng-Ann Heng

CoRR, September, 2025

The Invisible Leash: Why RLVR May Not Escape Its Origin.

Fang Wu, Weihao Xuan, Ximing Lu, Zaïd Harchaoui, Yejin Choi

CoRR, July, 2025

Predicting mutational effects on protein binding from folding energy.

Arthur Deng, Karsten D. Householder, Fang Wu, Sebastian Thrun, K. Christopher Garcia, Brian L. Trippe

CoRR, July, 2025

PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking.

Yize Jiang, Xinze Li, Yuanyuan Zhang, Jin Han, Youjun Xu, Ayush Pandit, Zaixi Zhang, Mengdi Wang, Mengyang Wang, Chong Liu, Guang Yang, Yejin Choi, Wu-Jun Li, Tianfan Fu, Fang Wu, Junhong Liu

CoRR, May, 2025

Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification.

Zicheng Liu, Siyuan Li, Zhiyuan Chen, Lei Xin, Fang Wu, Chang Yu, Qirong Yang, Yucheng Guo, Yujie Yang, Stan Z. Li

CoRR, February, 2025

Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling.

Fang Wu, Stan Z. Li

Trans. Mach. Learn. Res., 2025

Surface-based Molecular Design with Multi-modal Flow Matching.

Fang Wu, Zhengyuan Zhou, Shuting Jin, Xiangxiang Zeng, Jure Leskovec, Jinbo Xu

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.2, 2025

Predicting mutational effects on protein binding from folding energy.

Arthur Deng, Karsten D. Householder, Fang Wu, K. Christopher Garcia, Brian L. Trippe

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Large Language Models are Good Relational Learners.

Fang Wu, Vijay Prakash Dwivedi, Jure Leskovec

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

Generalized Implicit Neural Representations for Dynamic Molecular Surface Modeling.

Fang Wu, Bozhen Hu, Stan Z. Li

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

Discovering the Representation Bottleneck of Graph Neural Networks.

Fang Wu, Siyuan Li, Stan Z. Li

IEEE Trans. Knowl. Data Eng., December, 2024

Instructor-inspired Machine Learning for Robust Molecular Property Prediction.

Fang Wu, Shuting Jin, Siyuan Li, Stan Z. Li

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Surface-VQMAE: Vector-quantized Masked Auto-encoders on Molecular Surfaces.

Fang Wu, Stan Z. Li

Proceedings of the Forty-first International Conference on Machine Learning, 2024

SemiReward: A General Reward Model for Semi-supervised Learning.

Siyuan Li, Weiyang Jin, Zedong Wang, Fang Wu, Zicheng Liu, Cheng Tan, Stan Z. Li

Proceedings of the Twelfth International Conference on Learning Representations, 2024

InsertGNN: A Hierarchical Graph Neural Network for the TOEFL Sentence Insertion Problem.

Fang Wu, Stan Z. Li

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

2023

Improving molecular representation learning with metric learning-enhanced optimal transport.

Fang Wu, Nicolas Courty, Shuting Jin, Stan Z. Li

Patterns, April, 2023

InstructBio: A Large-scale Semi-supervised Learning Paradigm for Biochemical Problems.

Fang Wu, Huiling Qin, Siyuan Li, Stan Z. Li, Xianyuan Zhan, Jinbo Xu

CoRR, 2023

Explaining Graph Neural Networks via Non-parametric Subgraph Matching.

Fang Wu, Siyuan Li, Lirong Wu, Dragomir Radev, Yinghui Jiang, Xurui Jin, Zhangming Niu, Stan Z. Li

CoRR, 2023

A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design.

Fang Wu, Stan Z. Li

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching.

Fang Wu, Siyuan Li, Xurui Jin, Yinghui Jiang, Dragomir Radev, Zhangming Niu, Stan Z. Li

Proceedings of the International Conference on Machine Learning, 2023

Architecture-Agnostic Masked Image Modeling - From ViT back to CNN.

Siyuan Li, Di Wu, Fang Wu, Zelin Zang, Stan Z. Li

Proceedings of the International Conference on Machine Learning, 2023

Molformer: Motif-Based Transformer on 3D Heterogeneous Molecular Graphs.

Fang Wu, Dragomir Radev, Stan Z. Li

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations.

Fang Wu, Stan Z. Li

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

When Geometric Deep Learning Meets Pretrained Protein Language Models.

Fang Wu, Tao Yu, Dragomir Radev, Jinbo Xu

CoRR, 2022

Architecture-Agnostic Masked Image Modeling - From ViT back to CNN.

Siyuan Li, Di Wu, Fang Wu, Zelin Zang, Kai Wang, Lei Shang, Baigui Sun, Hao Li, Stan Z. Li

CoRR, 2022

Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions.

Fang Wu, Siyuan Li, Lirong Wu, Dragomir R. Radev, Qiang Zhang, Stan Z. Li

CoRR, 2022

A Score-based Geometric Model for Molecular Dynamics Simulations.

Fang Wu, Qiang Zhang, Xurui Jin, Yinghui Jiang, Stan Z. Li

CoRR, 2022

Pre-training of Deep Protein Models with Molecular Dynamics Simulations for Drug Binding.

Fang Wu, Qiang Zhang, Dragomir R. Radev, Yuyang Wang, Xurui Jin, Yinghui Jiang, Zhangming Niu, Stan Z. Li

CoRR, 2022

Metric Learning-enhanced Optimal Transport for Biochemical Regression Domain Adaptation.

Fang Wu, Nicolas Courty, Zhang Qiang, Jiyu Cui, Ziqing Li

CoRR, 2022

2021

3D-Transformer: Molecular Representation with Transformer in 3D Space.

Fang Wu, Qiang Zhang, Dragomir R. Radev, Jiyu Cui, Wen Zhang, Huabin Xing, Ningyu Zhang, Huajun Chen

CoRR, 2021