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Xujiang Zhao
I am a researcher at NEC Laboratories America. I received the Ph.D. in Computer Science Department at The University of Texas at Dallas, in 2022. My advisor is Prof. Feng Chen. I also got an MS at the University of Science and Technology of China (USTC) and a bachelor at Chongqing University in China.
Email /
CV /
Google Scholar /
LinkedIn /
Github
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Quote
" The fear of the LORD is the beginning of wisdom " - Psalms 111:10
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Experience
Reseracher at NEC Laboratories America (Princeton, NJ) 2022 summer - Current
Reserach intern at NEC Laboratories America (Princeton, NJ) during 2021 summer
Reserach intern at Alibaba Damo Academy (Seattle, WA) during 2019 summer
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Research Interests
My research centers on reliable agentic intelligence: foundation models that can specialize to new domains, use tools and workflows, adapt over time, and recognize uncertainty in their own decisions. I work across LLM agents, post-training and model specialization, and uncertainty-aware learning for robust behavior under distribution shift.
- Adaptive agentic AI: test-time search and scaling, reinforcement learning, memory-based continual adaptation, and self-evolving skills and workflows.
- Efficient and specialized LLMs: post-training, pruning and routing, code and optimization agents, knowledge routing, graph/time-series reasoning, and vision-language understanding.
- Trustworthy foundation models: uncertainty-aware reasoning, out-of-distribution detection, calibration, fairness, privacy, and robust decision-making.
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Research Publications
†: Corresponding author; *: Equal contribution.
Conference Proceedings
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Yutong Cheng, Haifeng Chen, Wenchao Yu, Xujiang Zhao, Peng Gao, Wei Cheng.
"Escaping Whack-a-Mole: Code Documentation Optimization via Dependency-Guided Bi-level Search."
International Conference on Machine Learning. ICML 2026.
[Agentic AI] [Code Generation]
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Peng Xia, Peng Xia, Jianwen Chen, Hanyang Wang, Jiaqi Liu, Kaide Zeng, Yu Wang, Siwei Han, Yiyang Zhou, Xujiang Zhao, Haifeng Chen, Zeyu Zheng, Cihang Xie, Huaxiu Yao.
"SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning."
ICLR 2026 MemAgents Workshop. Best Paper Award Runner-Up.
[Agentic AI] [Self Evolving]
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Linlin Yu, Xujiang Zhao†, Dong Li, Yanchi Liu, Wei Cheng, Zhengzhang Chen, Chen Zhao, Feng Chen, Haifeng Chen.
"Uncertainty-Aware Test-Time Search for Optimization Problem Solving."
In Proceedings of the Association for Computational Linguistics. ACL 2026.
[Agentic AI] [Test Time Scaling]
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Binchi Zhang, Xujiang Zhao, Jundong Li, Haifeng Chen, Zhengzhang Chen.
"Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models."
In Proceedings of the Association for Computational Linguistics. ACL 2026.
[Post Training]
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Xuyuan Liu, Shengyu Chen, Xinshuai Dong, Yanchi Liu, Xujiang Zhao, Haoyu Wang, Yujun Yan, Haifeng Chen, Zhengzhang Chen.
"Representation Interventions Enable Lifelong Unstructured Knowledge Control."
In Proceedings of the Association for Computational Linguistics. ACL 2026.
[Post Training] [Memory]
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Minghao Guo, Qingcheng Zeng, Xujiang Zhao, Yanchi Liu, Wenchao Yu, Mengnan Du, Haifeng Chen, Wei Cheng.
"DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router."
In Findings of the European Chapter of the Association for Computational Linguistics. EACL 2026.
[Agentic AI] [Workflow]
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Wangyang Ying, Yanchi Liu, Xujiang Zhao, Wei Cheng, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen.
"Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement."
In Findings of the European Chapter of the Association for Computational Linguistics. EACL 2026.
[Agentic AI] [Workflow]
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Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao, Zongyu Wu, Junxiang Wang, Xiang Zhang, Suhang Wang, Haifeng Chen.
"Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation."
In Findings of the European Chapter of the Association for Computational Linguistics. EACL 2026.
[Agentic AI] [Time Series]
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Dong Li, Zhengzhang Chen, Xujiang Zhao, Linlin Yu, Zhong Chen, Yi He, Haifeng Chen, Chen Zhao.
"MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery."
In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI 2026.
[Reinforcement Learning]
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Zhixia He, Chen Zhao, Minglai Shao, Xintao Wu, Xujiang Zhao, Dong Li, Qin Tian, Linlin Yu.
"Out-of-Distribution Detection with Positive and Negative Prompt Supervision Using Large Language Models."
In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI 2026.
[Trustworthy AI]
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Qin Tian, Chen Zhao, Xintao Wu, Dong Li, Minglai Shao, Xujiang Zhao, Wenjun Wang.
"Class-Domain Incremental Learning on Graphs via Disentangled Knowledge Distillation."
In Proceedings of The Web Conference. WWW 2026.
[Trustworthy AI]
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Dong Li, Xujiang Zhao†, Linlin Yu, Yanchi Liu, Wei Cheng, Zhengzhang Chen, Zhong Chen, Feng Chen, Chen Zhao, Haifeng Chen.
"SolverLLM: Leveraging Test-Time Scaling for Optimization Problem via LLM-Guided Search."
In Advances in Neural Information Processing Systems. NeurIPS 2025.
[Agentic AI] [Test Time Scaling]
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Shengkun Tang, Cong Zeng, Yuanzhou Chen, Zhiqiang Shen, Wenchao Yu, Xujiang Zhao, Haifeng Chen, Wei Cheng, Zhiqiang Xu.
"Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection."
In Advances in Neural Information Processing Systems. NeurIPS 2025.
[Trustworthy AI]
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Qiwei Zhao*, Dong Li*, Yanchi Liu, Wei Cheng, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Huaxiu Yao, Chen Zhao, Haifeng Chen, Xujiang Zhao†.
"Uncertainty Propagation on LLM Agent."
In Proceedings of the Association for Computational Linguistics. ACL 2025.
[Agentic AI] [Reliable]
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Jonathan Light, Yue Wu, Yiyou Sun, Wenchao Yu, Yanchi Liu, Xujiang Zhao, Ziniu Hu, Haifeng Chen, Wei Cheng.
"Scattered Forest Search: Smarter Code Space Exploration with LLMs."
In International Conference on Learning Representations. ICLR 2025.
[Agentic AI] [Code Generation]
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Xinyuan Wang, Yanchi Liu, Wei Cheng, Xujiang Zhao, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen.
"MixLLM: Dynamic Routing in Mixed Large Language Models."
In Proceedings of the North American Chapter of the Association for Computational Linguistics. NAACL 2025.
[Efficient AI]
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Xianjun Yang, Wei Cheng, Xujiang Zhao, Wenchao Yu, Linda Petzold, Haifeng Chen.
"Position Really Matters: Towards a Holistic Approach for Prompt Tuning."
In Findings of the Association for Computational Linguistics. NAACL 2025.
[Post Training]
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Ruomeng Ding, Xujiang Zhao†, Chen Zhao, Minglai Shao, Zhengzhang Chen, Haifeng Chen.
"Evidence-Based Out-of-Distribution Detection on Multi-Label Graphs."
In Proceedings of the SIAM International Conference on Data Mining. SDM 2025.
[Trustworthy AI]
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Chengyuan Deng, Zhengzhang Chen, Xujiang Zhao, Haoyu Wang, Junxiang Wang, Jie Gao, Haifeng Chen.
"Correlation-aware Online Change Point Detection."
In Proceedings of the ACM International Conference on Information and Knowledge Management. CIKM 2025.
[Trustworthy AI]
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Chen Ling, Xujiang Zhao†, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen.
"Uncertainty Quantification for In-Context Learning of Large Language Models."
In Proceedings of the North American Chapter of the Association for Computational Linguistics. NAACL 2024.
[Reliable LLM]
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Nan Zhang, Yanchi Liu, Xujiang Zhao, Wei Cheng, Runxue Bao, Rui Zhang, Prasenjit Mitra, Haifeng Chen.
"Pruning as a Domain-Specific LLM Extractor."
In Findings of the Association for Computational Linguistics. NAACL 2024.
[Post Training] [Model Compression]
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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng.
"Large Language Models Can Be Good Privacy Protection Learners."
In Proceedings of the Conference on Empirical Methods in Natural Language Processing. EMNLP 2024.
[Post Training] [Privacy]
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Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen.
"Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments."
International Joint Conference on Artificial Intelligence. IJCAI 2024.
[Trustworthy AI]
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Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Haifeng Chen.
"Adaptation Speed Analysis for Fairness-aware Causal Models."
In Proceedings of the ACM International Conference on Information and Knowledge Management. CIKM 2023.
[Trustworthy AI]
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Weili Shi, Xueying Yang, Xujiang Zhao, Haifeng Chen, Zhiqiang Tao, Sheng Li.
"Calibrate Graph Neural Networks under Out-of-Distribution Nodes via Deep Q-learning."
In Proceedings of the ACM International Conference on Information and Knowledge Management. CIKM 2023.
[Trustworthy AI]
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Chen Ling, Xuchao Zhang, Xujiang Zhao†, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao.
"Open-Ended Commonsense Reasoning with Unrestricted Answer Candidates."
In Findings of the Conference on Empirical Methods in Natural Language Processing. EMNLP 2023.
[Application]
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Xujiang Zhao, Xuchao Zhang, Chen Zhao, Jin-hee Cho, Lance Kaplan, Dong Hyun Jeong, Audun Jøsang, Haifeng Chen, Feng Chen.
"Multi-Label Temporal Evidential Neural Networks for Early Event Detection."
In IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP 2023.
[Application]
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Xujiang Zhao, Krishnateja Killamsetty, Rishabh Iyer, Feng Chen.
"How Out-of-Distribution Data Hurts Semi-Supervised Learning."
In IEEE International Conference on Data Mining. ICDM 2022.
[Trustworthy AI]
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Xueying Yang, Jiamian Wang, Xujiang Zhao, Zhiqiang Tao.
"Calibrate Automated Graph Neural Network via Hyperparameter Uncertainty."
In Proceedings of the ACM International Conference on Information and Knowledge Management. CIKM 2022.
[Trustworthy AI]
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Xujiang Zhao, Xuchao Zhang, Wei Cheng, Wenchao Yu, Yuncong Chen, Haifeng Chen, Feng Chen.
"SEED: Sound Event Early Detection via Evidential Uncertainty."
In IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP 2022.
[Application]
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Haoliang Wang, Chen Zhao, Xujiang Zhao, Feng Chen.
"Layer Adaptive Deep Neural Networks for Out-of-distribution Detection."
In Pacific-Asia Conference on Knowledge Discovery and Data Mining. PAKDD 2022.
[Trustworthy AI]
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Krishnateja Killamsetty, Xujiang Zhao, Feng Chen, Rishabh Iyer.
"RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning."
In Advances in Neural Information Processing Systems. NeurIPS 2021.
[Efficient AI]
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Liyan Xu, Xuchao Zhang, Xujiang Zhao, Haifeng Chen, Feng Chen, Jinho D. Choi.
"Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation."
In Proceedings of the Conference on Empirical Methods in Natural Language Processing. EMNLP 2021.
[Application]
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Zhuoyi Wang, Chen Zhao, Yuqiao Chen, Hemeng Tao, Yu Lin, Xujiang Zhao, Yigong Wang, Latifur Khan.
"CLEAR: Contrastive-Prototype Learning with Drift Estimation for Resource Constrained Stream Mining."
In Proceedings of The Web Conference. WWW 2021.
[Application]
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Yibo Hu, Yuzhe Ou, Xujiang Zhao, Feng Chen.
"Multidimensional Uncertainty-Aware Evidential Neural Networks."
In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI 2021.
[Trustworthy AI]
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Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho.
"Uncertainty Aware Semi-Supervised Learning on Graph Data."
In Advances in Neural Information Processing Systems. NeurIPS 2020, Spotlight.
[Trustworthy AI]
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Weishi Shi, Xujiang Zhao, Qi Yu, Feng Chen.
"Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning."
In Advances in Neural Information Processing Systems. NeurIPS 2020.
[Efficient AI]
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Adil Alim, Xujiang Zhao, Jin-Hee Cho, Feng Chen.
"Uncertainty-Aware Opinion Inference Under Adversarial Attacks."
In IEEE International Conference on Big Data. Big Data 2019.
[Trustworthy AI]
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Xujiang Zhao, Yuzhe Ou, Lance Kaplan, Feng Chen, Jin-Hee Cho.
"Quantifying Classification Uncertainty using Regularized Evidential Neural Networks."
AAAI 2019 Fall Symposium Series. Artificial Intelligence in Government and Public Sector.
[Trustworthy AI]
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Xujiang Zhao, Shu Hu, Jin-Hee Cho, Feng Chen.
"Uncertainty-based Decision Making using Deep Reinforcement Learning."
In International Conference on Information Fusion. FUSION 2019.
[RL]
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Xujiang Zhao, Feng Chen, Jin-Hee Cho.
"Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data."
In IEEE International Conference on Big Data. Big Data 2018.
[Trustworthy AI]
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Xujiang Zhao, Feng Chen, Jin-Hee Cho.
"Deep Learning based Scalable Inference of Uncertain Opinions."
In IEEE International Conference on Data Mining. ICDM 2018.
[Trustworthy AI]
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Xujiang Zhao, Feng Chen, Jin-Hee Cho.
"Uncertainty-Based Opinion Inference on Network Data Using Graph Convolutional Neural Networks."
IEEE Military Communications Conference. MILCOM 2018.
[Trustworthy AI]
Journal Articles
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Chen Ling, Xujiang Zhao†, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Xuchao Zhang, Amit P., Wei Chen, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Jian Pei, Carl Yang, Liang Zhao.
"Domain Specialization as The Key to Make Large Language Models Disruptive: A Comprehensive Survey."
Honorably mentioned by The 2024 Economic Report of the President from the White House
ACM Computing Surveys. 2025.[Survey][Post Training]
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Ali Riahi Samani, Xujiang Zhao, Feng Chen.
"Distribution Shift, Generalization and OOD Challenge in Offline Reinforcement Learning: A Comprehensive Survey."
Neural Computing and Applications. 2026.
[Reinforcement Learning]
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Junji Jiang, Chen Ling, Hongyi Li, Guangji Bai, Xujiang Zhao, Liang Zhao.
"Quantifying Uncertainty in Graph Neural Network Explanations."
Frontiers in Big Data. 2024.
[Trustworthy AI]
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Zhen Guo*, Zelin Wan*, Qisheng Zhang*, Xujiang Zhao*, Feng Chen, Jin-hee Cho, Qi Zhang, Lance Kaplan, Dong Hyun Jeong, Audun Jøsang.
"A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning."
Information Fusion. 2023.
[Trustworthy AI]
Preprint
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Peng Xia, Peng Xia, Jianwen Chen, Hanyang Wang, Jiaqi Liu, Kaide Zeng, Yu Wang, Siwei Han, Yiyang Zhou, Xujiang Zhao, Haifeng Chen, Zeyu Zheng, Cihang Xie, Huaxiu Yao.
"SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning."
Preprint.
[Agentic AI] [Self Evolving]
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Jiaqi Liu, Shi Qiu, Mairui Li, Bingzhou Li, Peng Xia, Siwei Han, Haonian Ji, Letian Zhang, Hardy Chen, Haoqin Tu, Xinyu Yang, Xujiang Zhao, Haifeng Chen, Jiawei Zhou, Xiao Wang, Hongtu Zhu, Yun Li, Jiaheng Zhang, Yuyin Zhou, Sheng Wang, James Zou, Zeyu Zheng, Cihang Xie, Mingyu Ding, Huaxiu Yao.
"AutoResearchClaw: End-to-end Autonomous Research From Idea to Paper."
Preprint.
[Agentic AI] [Self Evolving]
Under Review
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Peng Xia, Peng Xia, Jianwen Chen, Hanyang Wang, Jiaqi Liu, Kaide Zeng, Yu Wang, Siwei Han, Yiyang Zhou, Xujiang Zhao, Haifeng Chen, Zeyu Zheng, Cihang Xie, Huaxiu Yao.
"SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning."
NeurIPS 2026 Under Review.
[Agentic AI] [Self Evolving]
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Dong Li, Yanchi Liu, Xujiang Zhao†, Wei Cheng, Zhengzhang Chen, Xintao Wu, Zhong Chen, Chen Zhao, Haifeng Chen.
"Memory-Induced Inference-Time Adaptation for Continual Learning in Small Language Models."
NeurIPS 2026 Under Review.
[Agentic AI] [Memory]
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Dong Li, Yanchi Liu, Xujiang Zhao†, Wei Cheng, Zhengzhang Chen, Xintao Wu, Zhong Chen, Chen Zhao, Haifeng Chen.
"HierFlow: Hierarchical Dual-Space Search for Automatic Agentic Workflow Generation."
NeurIPS 2026 Under Review.
[Agentic AI] [Workflow]
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Shang Ma, Haifeng Chen, Yuanzhou Chen, Yanchi Liu, Xujiang Zhao, Wenchao Yu, Yanfang Ye, Xusheng Xiao, Wei Cheng.
"The Geometry of Fakeness: OOD Learning for AI-Image Detection with Information Bottleneck."
NeurIPS 2026 Under Review.
[Trustworthy AI]
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Jiaqi Liu, Shi Qiu, Mairui Li, Bingzhou Li, Peng Xia, Siwei Han, Haonian Ji, Letian Zhang, Hardy Chen, Haoqin Tu, Xinyu Yang, Xujiang Zhao, Haifeng Chen, Jiawei Zhou, Xiao Wang, Hongtu Zhu, Yun Li, Jiaheng Zhang, Yuyin Zhou, Sheng Wang, James Zou, Zeyu Zheng, Cihang Xie, Mingyu Ding, Huaxiu Yao.
"AutoResearchClaw: End-to-end Autonomous Research From Idea to Paper."
NeurIPS 2026 Under Review.
[Agentic AI] [Self Evolving]
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Hongyu Cao, Yanchi Liu, Kunpeng Liu, Xujiang Zhao, Wei Cheng, Yanjie Fu, Haifeng Chen.
"Data-centric Small LLM Learning: A Gradient Admission Perspective."
NeurIPS 2026 Under Review.
[Agentic AI] [Memory]
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Xinshuai Dong, Haifeng Chen, Xuyuan Liu, Shengyu Chen, Haoyu Wang, Yanchi Liu, Xujiang Zhao, Kun Zhang, Zhengzhang Chen.
"TaPE: Certified Robustness Against Table Permutations with Tabular Positional Encoding."
NeurIPS 2026 Under Review.
[Post Training]
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Bangwei Guo, Xujiang Zhao†, Yanchi Liu, Wei Cheng, Shengyu Chen, Dongyue Li, Morimoto Masaharu, Takayuki Kuroda, Dimitris N. Metaxas, Haifeng Chen.
"TopoAgent: A Structure-Aware Perception-to-Reasoning Framework for Diagram-to-Graph Topology Extraction with Large Vision-Language Models."
EMNLP 2026 Under Review.
[Agentic AI] [Vision Reasoning]
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Xinyu Wu, Yanchi Liu, Dong Li, Xujiang Zhao, Wei Cheng, Zhengzhang Chen, Chen Zhao.
"FlexTag: Instruction Tagging for Multi-Perspective Understanding with Small Language Models."
EMNLP 2026 Under Review.
[Post Training]
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Kangyu Zhu, Xujiang Zhao†, Hao Chen, Peng Xia, Yiming Liang, Siwei Han, Huaxiu Yao, Haifeng Chen.
"PRISM: Progressive Reasoning with Iterative Structured Memory for Multi-Step Visual Understanding."
ECCV 2026 Under Review.
[Agentic AI] [Vision Reasoning]
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Selected Publications
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01
Escaping Whack-a-Mole: Code Documentation Optimization via Dependency-Guided Bi-level Search
Yutong Cheng, Haifeng Chen, Wenchao Yu, Xujiang Zhao, Peng Gao, Wei Cheng
ICML 2026Code Optimization
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SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning
Peng Xia, Peng Xia, Jianwen Chen, Hanyang Wang, Jiaqi Liu, Kaide Zeng, Yu Wang, Siwei Han, Yiyang Zhou, Xujiang Zhao, et al.
ICLR 2026 MemAgents WorkshopBest Paper Runner-Up
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03
Uncertainty-Aware Test-Time Search for Optimization Problem Solving
Linlin Yu, Xujiang Zhao†, Dong Li, Yanchi Liu, Wei Cheng, Zhengzhang Chen, Chen Zhao, Feng Chen, Haifeng Chen
ACL 2026Agentic AI
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04
Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models
Binchi Zhang, Xujiang Zhao, Jundong Li, Haifeng Chen, Zhengzhang Chen
ACL 2026Cultural Alignment
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05
Representation Interventions Enable Lifelong Unstructured Knowledge Control
Xuyuan Liu, Shengyu Chen, Xinshuai Dong, Yanchi Liu, Xujiang Zhao, Haoyu Wang, Yujun Yan, Haifeng Chen, Zhengzhang Chen
ACL 2026Knowledge Control
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DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router
Minghao Guo, Qingcheng Zeng, Xujiang Zhao, Yanchi Liu, Wenchao Yu, Mengnan Du, Haifeng Chen, Wei Cheng
EACL 2026 FindingsLLM Routing
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07
Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement
Wangyang Ying, Yanchi Liu, Xujiang Zhao, Wei Cheng, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen
EACL 2026 FindingsMulti-agent Extraction
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08
Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation
Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao, Zongyu Wu, Junxiang Wang, Xiang Zhang, Suhang Wang, Haifeng Chen
EACL 2026 FindingsTime Series Annotation
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09
MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery
Dong Li, Zhengzhang Chen, Xujiang Zhao, Linlin Yu, Zhong Chen, Yi He, Haifeng Chen, Chen Zhao
AAAI 2026[Reinforcement Learning]
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10
Out-of-Distribution Detection with Positive and Negative Prompt Supervision Using Large Language Models
Zhixia He, Chen Zhao, Minglai Shao, Xintao Wu, Xujiang Zhao, Dong Li, Qin Tian, Linlin Yu
AAAI 2026OOD Detection
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11
Class-Domain Incremental Learning on Graphs via Disentangled Knowledge Distillation
Qin Tian, Chen Zhao, Xintao Wu, Dong Li, Minglai Shao, Xujiang Zhao, Wenjun Wang
WWW 2026Graph Learning
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12
Distribution Shift, Generalization and OOD Challenge in Offline Reinforcement Learning: A Comprehensive Survey
Ali Riahi Samani, Xujiang Zhao, Feng Chen
Neural Computing and Applications 2026Offline RL Survey
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15
Uncertainty Propagation on LLM Agent
Qiwei Zhao*, Dong Li*, Yanchi Liu, Wei Cheng, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Huaxiu Yao, Chen Zhao, Haifeng Chen, Xujiang Zhao†
ACL 2025Agentic AI
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Correlation-aware Online Change Point Detection
Chengyuan Deng, Zhengzhang Chen, Xujiang Zhao, Haoyu Wang, Junxiang Wang, Jie Gao, Haifeng Chen
CIKM 2025Change Point Detection
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22
Uncertainty Quantification for In-Context Learning of Large Language Models
Chen Ling, Xujiang Zhao†, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen
NAACL 2024LLM Uncertainty
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24
Large Language Models Can Be Good Privacy Protection Learners
Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng
EMNLP 2024Privacy AI
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26
Quantifying Uncertainty in Graph Neural Network Explanations
Junji Jiang, Chen Ling, Hongyi Li, Guangji Bai, Xujiang Zhao, Liang Zhao
Frontiers in Big Data 2024GNN Explanation
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Selected Publications
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Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection
Cong Zeng, Shengkun Tang, Yuanzhou Chen, Zhiqiang Shen, Wenchao Yu, Xujiang Zhao, et al.
NeurIPS, 2025
paper /
code /
We theoretically analyze the failure case and reason for treating LLM detection tasks as binary classification tasks and propose to transform the task to an out-of-distribution detection task.
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SolverLLM: Leveraging Test-Time Scaling for Optimization Problem via LLM-Guided Search
Dong li, Xujiang Zhao, Linlin Yu, et al.
NeurIPS, 2025
paper /
code /
We introduce SolverLLM, a training-free framework that leverages test-time scaling to solve diverse optimization problems.
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Uncertainty Propagation on LLM Agent
Qiwei Zhao, Dong li, et al., Xujiang Zhao
ACL, 2025
paper /
code /
In this paper, we proposed a novel framework that propagates uncertainty through each step of an LLM-based agent’s reasoning process.
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Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Chen Ling,
Xujiang Zhao,
Jiaying Lu,
Chengyuan Deng,
Can Zheng,
Junxiang Wang,
Tanmoy Chowdhury,
Yun Li,
Hejie Cui,
Xuchao Zhang,
Tianyang Zhao,
Amit P.,
Wei Chen,
Haoyu Wang,
Yanchi Liu,
Zhengzhang Chen,
Haifeng Chen,
Chris White,
Quanquan Gu,
Jian Pei,
Carl Yang,
Liang Zhao,
ACM Computing Surveys CSUR (IF: 23.2), 2025
paper /
slides
We present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications. This paper was cited by 2024 Economic Report of the President of the United States
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SFS: Smarter Code Space Search Improves LLM Inference Scaling
Jonathan Light, Yue Wu, Yiyou Sun, Wenchao Yu, Yanchi Liu, Xujiang Zhao, et al.
ICLR, 2025
paper /
code /
project /
In this paper, we have shown that framing code generation as an optimization task over the code space and applying SCATTERED FOREST SEARCH is highly effective..
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MixLLM: Dynamic Routing in Mixed Large Language Models
Xinyuan Wang, Yanchi Liu, Wei Cheng, Xujiang Zhao, et al.
NAACL, 2025
paper /
code /
In this paper, we proposed MixLLM, a dynamic routing system that selects the most suitable LLM for each query by balancing response quality, cost, and latency.
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Position Really Matters: Towards a Holistic Approach for Prompt Tuning
Xianjun Yang, Wei Cheng, Xujiang Zhao, Wenchao Yu, Linda Ruth Petzold, Haifeng Chen
NAACL, 2025 (Findings)
paper /
code /
In this paper, we first derive a unified view of prompt tuning and then present a novel dynamic prompting approach that can significantly improve the performance of prompt tuning while adding only a few additional parameters..
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Evidence-Based Out-of-Distribution Detection on Multi-Label Graphs
Ruomeng Ding, Xujiang Zhao, Chen Zhao, Minglai Shao, Zhengzhang Chen, Haifeng Chen
SDM, 2025
paper /
code /
In this paper, we introduce a novel evidential method, Multi-Label Evidential Graph Neural Networks, to predict uncertainty for multiple classes on graph data.
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Large Language Models Can Be Contextual Privacy Protection Learners
Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, et al.
EMNLP, 2024
paper /
code /
In this paper, we present a comprehensive exploration of strategies for fine-tuning Large Language Models (LLMs) to incorporate domain-specific knowledge while upholding data privacy.
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Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments
Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen
IJCAI, 2024
paper /
This paper has proposed a novel framework, DCFDG, to address issues of fairness within continuously evolving dynamic environments.
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Pruning as a Domain-specific LLM Extractor
Nan Zhang, Yanchi Liu, Xujiang Zhao, Wei Cheng, Runxue Bao, Rui Zhang, Prasenjit Mitra, Haifeng Chen
NAACL, 2024 (findings)
paper /
code /
We introduce D-PRUNER, an innovative unstructured dual-pruning method for domain-specific compression on LLM. It is able to extract a compressed, domain-specific, and task-agnostic LLM by identifying weights that are pivotal for both generality and specificity.
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Uncertainty Quantification for In-Context Learning of Large Language Models
Chen Ling,
Xujiang Zhao,
Xuchao Zhang,
Wei Cheng,
et al.
NAACL, 2024
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code /
We provide an Uncertainty Quantification and Decomposition of In-Context Learning of Large Language Model.
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Open-ended Commonsense Reasoning with Unrestricted Answer Scope
Chen Ling,
Xuchao Zhang,
Xujiang Zhao,
et al.
EMNLP, 2023 (findings)
paper /
code /
We present an off-the-shelf framework KEEP to predict answers for open-ended commonsense reasoning without requiring answer candidates and a pre-defined answer scope.
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Adaptation Speed Analysis for Fairness-aware Causal Models
Yujie Lin,
Chen Zhao
Minglai Shao,
Xujiang Zhao,
Haifeng Chen,
CIKM, 2023
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code /
This paper aims to explore spurious relationships in structural causal models (SCMs) that arise due to sensitive factors.
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Calibrate Graph Neural Networks under Out-of-Distribution Nodes via Deep Q-learning
Weili Shi,
Xueying Yang
Xujiang Zhao,
Haifeng Chen,
Zhiqiang Tao,
Sheng Li,
CIKM, 2023
paper /
code /
We propose a Graph Edge Re-weighting via Deep Q-learning (GERDQ) framework to calibrate the graph neural networks.
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Multi-Label Temporal Evidential Neural Networks for Early Event Detection
Xujiang Zhao,
Xuchao Zhang,
Chen Zhao,
Jin-hee Cho,
Lance Kaplan,
Dong Hyun Jeong,
Audun Jøsang,
Haifeng Chen,
Feng Chen,
ICASSP, 2023
paper /
code /
slides
We study the problem of early event detection in multi-lable classification, and propose a novel framework, Multi-Label Temporal Evidential Neural Network (MTENN), for multi-label uncertainty estimation in temporal data.
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Knowledge-enhanced Neural Machine Reasoning: A Review
Tanmoy Chowdhury,
Chen Ling,
Xuchao Zhang,
Xujiang Zhao,
Guangji Bai,
Jian Pei,
Haifeng Chen,
Liang Zhao,
Preprint, 2023
paper /
slides
In this survey paper, we provide a comprehensive technical review of the existing knowledge-enhanced reasoning techniques across the diverse range of application domains.
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A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning
Zhen Guo*,
Zelin Wan*,
Qisheng Zhang*,
Xujiang Zhao*,
Feng Chen,
Jin-hee Cho,
Qi Zhang*,
Lance Kaplan,
Dong Hyun Jeong,
Audun Jøsang,
Information Fusion (Impact IF: 17.5), 2023
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slides
In this survey paper, we study the mature uncertainty research in belief/evidence theories in machine learning/deep learning to tackle complex problems under different types of uncertainty.
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How Out-of-Distribution Data Hurts Semi-Supervised Learning
Xujiang Zhao*,
Krishnateja Killamsetty*,
Rishabh Iyer,
Feng Chen,
ICDM 2022
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code /
slides
We study the key causes about the negative impact of OODs (boundary OODs and faraway ODDs) on SSL and proposed a simple unified robust SSL approach for many existing SSL algorithms in order to improve their robustness against OODs.
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Calibrate Automated Graph Neural Network via Hyperparameter Uncertainty
Xueying Yang
Jiamian Wang,
Xujiang Zhao,
Sheng Li,
Zhiqiang Tao,
CIKM, 2022 (Short paper)
paper /
code /
slides
In this paer, we investigate automated GNN calibration by marrying uncertainty estimation to the hyperparameter optimization (HPO) problem.
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SEED: Sound Event Early Detection via Evidential Uncertainty
Xujiang Zhao,
Xuchao Zhang,
Wei Cheng,
Wenchao Yu,
Yuncong Chen,
Haifeng Chen,
Feng Chen,
ICASSP, 2022
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code /
slides
We propose a novel Polyphonic Evidential Neural Network to model the evidential uncertainty of the class probability with Beta distribution to solve the sound event early detection problem.
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Layer Adaptive Deep Neural Networks for Out-of-distribution Detection
Haoliang Wang,
Chen Zhao,
Xujiang Zhao,
Feng Chen,
PAKDD, 2022
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code /
slides
In this paper, we proposed a novel layer-adaptive OOD detection framework (LA-OOD) for DNNs that can fully utilize the intermediate layers’ outputs.
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RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
Krishnateja Killamsetty,
Xujiang Zhao,
Rishabh Iyer,
Feng Chen,
NeurIPS, 2021
paper /
code /
slides
We propose a subset selection algorithm in semi-supervised learning to speed up the SSL training. In addition, this algorithm achieve a better performance when unlabeled data consists of Out-of-Distribution (OOD) data and imbalance.
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Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation
Liyan Xu,
Xuchao Zhang,
Xujiang Zhao,
Haifeng Chen,
Feng Chen,
Jinho D. Choi,
EMNLP, 2021 (Short paper, Oral)
paper /
code /
slides
In this work, we propose a self-learning framework that further utilizes unlabeled data of target languages, combined with uncertainty estimation in the process to select high-quality silver labels..
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CLEAR: Contrastive-Prototype Learning with Drift Estimation for Resource Constrained Stream Mining
Zhuoyi Wang,
Chen Zhao,
Yuqiao Chen,
Hemeng Tao,
Yu Lin,
Xujiang Zhao,
Yigong Wang,
Latifur Khan,
WWW, 2021
paper /
code /
slides
CLEAR: Contrastive-Prototype Learning with Drift Estimation for Resource Constrained Stream Mining.
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Multidimensional Uncertainty-Aware Evidential Neural Networks
Yibo Hu,
Yuzhe Ou,
Xujiang Zhao,
Feng Chen,
Jin-hee Cho,
AAAI, 2021
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code /
slides
By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem.
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Uncertainty Aware Semi-Supervised Learning on Graph Data
Xujiang Zhao,
Feng Chen,
Shu Hu,
Jin-hee Cho,
NeurIPS, 2020 (Spotlight)
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code /
slides /
poster /
video
A multi-source uncertainty framework of GNN that reflecting various types of uncertainties in both deep learning and belief/evidence theory domains for node classification predictions.
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Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning
Weishi Shi,
Xujiang Zhao,
Feng Chen,
Qi Yu,
NeurIPS, 2020
paper /
appendix /
code /
slides
A novel multi-source uncertainty prediction approach that enables deep learning models to be actively trained with much less labeled data.
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Uncertainty-Aware Opinion Inference Under Adversarial Attacks
Adil Alim,
Xujiang Zhao,
Jin-hee Cho,
Feng Chen,
Bigdata, 2019
paper /
slides
we propose a highly scalable opinion inference probabilistic model, namely Adversarial Collective Opinion Inference (Adv-COI), which provides a solution to infer unknown opinions with high scalability and robustness under the presence of uncertain,.
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Quantifying Classification Uncertainty using Regularized Evidential Neural Networks
Xujiang Zhao,
Yuzhe Ou,
Lance Kaplan,
Feng Chen,
Jin-hee Cho,
AAAI, 2019 Fall Symposium Series
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code /
slides
This paper presents a new approach, called a regularized EEvidential Neural Networks (ENN), that learns an ENN based on regularizations related to different characteristics of inherent data uncertainty.
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Uncertainty-based Decision Making Using Deep Reinforcement Learning
Xujiang Zhao,
Shu Hu,
Jin-hee Cho,
Feng Chen,
FUSION, 2019
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slides
This paper proposed three DRL-based schemes combining Subjective Logic and deep reinforcement learning where a reward is given based on a different type of uncertainty (i.e., vacuity, dissonance, or monosonance).
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Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data
Xujiang Zhao,
Feng Chen,
Jin-hee Cho,
Bigdata, 2018
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code /
slides
GCN-GRU:Combine Graph Convolutional Network (GCN) and the Gated Recurrent Units (GRU) to model the topological and temporal heterogeneous dependency information of a given dynamic network and conflicting opinions based on robust statistics (uncertainty)
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Deep Learning based Scalable Inference of Uncertain Opinions
Xujiang Zhao,
Feng Chen,
Jin-hee Cho,
ICDM, 2018 (Full paper; Acceptance rate: 8.86%)
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code /
slides /
poster
GCN-VAE-SL: The proposed DL-based opinion inference model handles node-level opinions explicitly in a large-scale network using graph convoluational network (GCN) and variational autoencoder (VAE) techniques.
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Uncertainty-Based Opinion Inference on Network Data Using Graph Convolutional Neural Networks
Xujiang Zhao,
Feng Chen,
Jin-hee Cho,
MILCOM, 2018
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code /
slides
We propose a general framework to model and infer heterogeneous uncertainty information in network data based on GCN and node-level opinions via knowledge distillation.
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Program Committee Member
2025: ICLR, NeurIPS, ICML, AAAI, ARR, IEEE Transactions on Medical Imaging, ACM Computing Surveys
2024: ICLR, NeurIPS, ICML, AAAI, KDD, ARR, COLM
2023: ICLR, NeurIPS, ICML, AAAI, KDD,
2022: NeurIPS, ICML, KDD, ICLR, WSDM, AAAI, SDM
NeurIPS 2021, KDD 2021
KDD 2020
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