Yijun Tian

I am a Professor in the School of Software Engineering at Xi'an Jiaotong University. Before that, I was an AI Researcher at Amazon AWS AI Lab. I received my Ph.D. from the University of Notre Dame, working with Nitesh V. Chawla (ACM/IEEE/AAAS/AAAI Fellow). I obtained my M.S. from New York University and my B.E. from Shandong University.

My research centers on algorithmic innovations that make models more capable, efficient, and controllable. In particular, I work on LLMs, Post-training, Agents, AI Safety, and Data Mining.

田一君，博士，国家级青年人才，西安交通大学软件学院教授，博士生导师，智能网络与网络安全教育部重点实验室成员。依托管晓宏院士、王伟院长团队开展大模型研究，主要方向包括后训练、智能体、AI 安全与数据挖掘。发表相关领域 CCF-A 类会议和期刊等论文 60 余篇。

现招收 2027 Fall 入学的硕士生（7 名）、博士生（1 名），诚挚欢迎有意向从事前沿 AI 研究的同学与我联系。

Email: meetyijun@gmail.com / Google Scholar

News

[Jun 2026] One paper was accepted to TMLR: OPQE.
[May 2026] One paper was accepted to TMLR: VERITAS.
[Apr 2026] Three papers were accepted to ACL 2026: ALDEN (Oral), SAGE (Oral) and ANN.
[Apr 2026] One paper was accepted to ICML 2026: G-Substrate.
[Apr 2026] One paper was accepted to SIGIR 2026: LACONIC.
[Jan 2026] One paper was accepted to ICLR 2026: RMT.
[Nov 2025] Two papers were accepted to AAAI 2026: HGKD (Oral) and ACE-GSL.
[Mar 2025] Two papers were accepted to ACM Computing Surveys: KD on Graphs and CILG.
[Oct 2024] One paper was accepted to WSDM 2025: TinyLLM (Oral).

Selected Publications (Google Scholar)

	ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents Tianyu Yang, Terry Ruas, Yijun Tian, Jan Philip Wahle, Daniel Kurzawe, Bela Gipp ACL 2026 (Oral) Paper / Code
	Reinforcement Learning for Self-Improving Agent with Skill Library Jiongxiao Wang, Qiaojing Yan, Yawei Wang, Yijun Tian, Soumya Smruti Mishra, Zhichao Xu, Megha Gandhi, Panpan Xu, Lin Lee Cheong ACL 2026 (Oral) Paper / Code
	Graph is a Substrate Across Data Modalities Ziming Li, Xiaoming Wu, Zehong Wang, Jiazheng Li, Yijun Tian, Jinhe Bi, Yunpu Ma, Yanfang Ye, Chuxu Zhang ICML 2026 Paper / Code
	Rethinking On-policy Optimization for Query Augmentation Zhichao Xu, Shengyao Zhuang, Xueguang Ma, Bingsen Chen, Yijun Tian, Fengran Mo, Tao Li, Jie Cao, Vivek Srikumar TMLR 2026 Paper / Code
	Reinforcement Mid-Training Yijun Tian, Shaoyu Chen, Zhichao Xu, Yawei Wang, Jinhe Bi, Peng Han, Wei Wang ICLR 2026 Paper / Code
	Knowledge Distillation on Graphs: A Survey Yijun Tian, Shichao Pei, Xiangliang Zhang, Chuxu Zhang, Nitesh V. Chawla ACM Computing Surveys Paper
	Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillation Yijun Tian, Yikun Han, Xiusi Chen, Wei Wang, Nitesh V. Chawla WSDM 2025 (Oral) Paper / Code
	G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, Bryan Hooi NeurIPS 2024 Paper / Code
	Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, Zheyuan Liu, Bing Yin, Meng Jiang EMNLP 2024 Paper / Code
	Towards Safer Large Language Models through Machine Unlearning Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, Yijun Tian, Meng Jiang ACL 2024 Paper / Code
	S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning Guancheng Wan, Yijun Tian, Wenke Huang, Nitesh V. Chawla, Mang Ye ICML 2024 Paper / Code
	Learning to Predict Mutational Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning Lirong Wu, Yijun Tian, Haitao Lin, Yufei Huang, Siyuan Li, Nitesh V. Chawla, Stan Z. Li ICML 2024 Paper / Code
	MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding Lirong Wu, Yijun Tian, Yufei Huang, Siyuan Li, Haitao Lin, Nitesh V. Chawla, Stan Z. Li ICLR 2024 (Spotlight) Paper / Code
	Mitigating Emergent Robustness Degradation while Scaling Graph Learning Xiangchi Yuan, Chunhui Zhang, Yijun Tian, Yanfang Ye, Chuxu Zhang ICLR 2024 Paper / Code
	Graph Neural Prompting with Large Language Models Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V. Chawla, Panpan Xu AAAI 2024 Paper / Code
	Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization Chunhui Zhang, Yijun Tian, Mingxuan Ju, Zheyuan Liu, Yanfang Ye, Nitesh V. Chawla, Chuxu Zhang ICLR 2023 Paper / Code
	Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla ICLR 2023 (Spotlight) Paper / Code
	Heterogeneous Graph Masked Autoencoders Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla AAAI 2023 (Oral) Paper / Code

Last updated: June 2026