(*=equal contribution)

Preprint

• Jiacheng Lin, Zhongruo Wang, Kun Qian, Tian Wang, Arvind Srinivasan, Hansi Zeng, Ruocheng Jiao, Xie Zhou, Jiri Gesi, Dakuo Wang, Yufan Guo, Kai Zhong, Weiqi Zhang, Sujay Sanghavi, Changyou Chen, Hyokun Yun, Lihong Li, SFT Doesn’t Always Hurt General Capabilities: Revisiting Domain-Specific Fine-Tuning in LLMs, preprint, 2024. [PDF]
• Jiacheng Lin, Hanwen Xu, Zifeng Wang, Sheng Wang, Jimeng Sun, Panacea: A foundation model for clinical trial search, summarization, design, and recruitment, preprint, 2024. [PDF][CODE]
• Kaiyuan Gao, Sunan He, Zhenyu He, Jiacheng Lin (All authors contribute equally), QiZhi Pei, Jie Shao, Wei Zhang, Examining User-Friendly and Open-Sourced Large GPT Models: A Survey on Language, Multimodal, and Scientific GPT Models, preprint, 2023. [PDF][Repo]

Journals

• Jiacheng Lin, Tian Wang, Kun Qian, Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning, TMLR, 2025. [PDF][CODE]
• Hanwen Xu*, Jiacheng Lin∗, Addie Woicik, Zixuan Liu, Jianzhu Ma, Sheng Zhang, Hoifung Poon, Liewei Wang, Sheng Wang, Pisces: A multi-modal data augmentation approach for drug combination synergy prediction, Cell Genomics, 2025. [PDF][CODE]
• Xiaoyang Chen, Keyi Li, Xuejian Cui, Zian Wang, Qun Jiang, Jiacheng Lin, Zhen Li, Zijing Gao, Rui Jiang, EpiAgent: Foundation model for single-cell epigenomic data, Nature Methods, 2025. [PDF]
• Jiacheng Lin, Lijun Wu, Jinhua Zhu, Xiaobo Liang, Yingce Xia, Shufang Xie, Tao Qin and Tie-Yan Liu, R²-DDI: Relation-aware Feature Refinement for Drug-Drug Interaction Prediction, Briefings in Bioinformatics, Volume 24, Issue 1, 2023. [PDF][CODE]
• Jiacheng Lin, Jialin Zhu, Huangang Wang and Tao Zhang, Learning to branch with Tree-aware Branching Transformers, Knowledge-Based Systems, Volume 252, 2022, 109455, ISSN 0950-7051. [PDF][CODE]

Conferences

• Pengcheng Jiang*, Jiacheng Lin∗, Lang Cao, Runchu Tian, SeongKu Kang, Zifeng Wang, Jimeng Sun, Jiawei Han, DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning, COLM, 2025. [PDF][CODE]
• Ziyi Chen, Xiaocong Yang, Jiacheng Lin, Chenkai Sun, Kevin Chen-Chuan Chang, and Jie Huang, Cascade Speculative Drafting for Even Faster LLM Inference, NeurIPS, 2024. [PDF]
• Chenlin Ming, Jiacheng Lin, Pangkit Fong, Han Wang, Xiaoming Duan and Jianping He, HiCRISP: An LLM-Based Hierarchical Closed-Loop Robotic Intelligent Self-Correction Planner, CAC, 2024. [PDF][CODE]
• Pengcheng Jiang, Jiacheng Lin, Zifeng Wang, Jimeng Sun, and Jiawei Han, GENRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models, NAACL, 2024. [PDF]
• Jiacheng Lin∗, Meng Xu*, Zhihua Xiong, and Huangang Wang. CAMBranch: Contrastive Learning with Augmented MILPs for Branching, ICLR, 2024. [PDF]
• Jiacheng Lin∗, Hanwen Xu∗, Addie Woicik, Jianzhu Ma and Sheng Wang, Pisces: A cross-modal contrastive learning approach to synergistic drug combination prediction, RECOMB, 2023. [PDF][CODE]
• Jiacheng Lin, Kun Qian, Haoyu Han, Nurendra Choudhary, Tianxin Wei, Zhongruo Wang, Sahika Genc, Edward W Huang, Sheng Wang, Karthik Subbian, Danai Koutra, Jimeng Sun, GT2VEC: Large Language Models as Multi-Modal Encoders for Text and Graph-Structured Data, KDD 2025 Workshop SKnow-LLM, 2025. [PDF]