Mingyue Cheng

Mingyue Cheng Ph.D.  ·  Associate Researcher

State Key Laboratory of Cognitive Intelligence
School of Computer Science and Technology,  University of Science and Technology of China
USTC-AGI Group Google Scholar GitHub CV (PDF)
Address: B709, Xinzhi Building, Gaoxin Campus of USTC, Hefei, Anhui, China, 230031

Biography

I am an Associate Researcher at the School of Computer Science and Technology, University of Science and Technology of China (USTC). I am also affiliated with the State Key Laboratory of Cognitive Intelligence at USTC, working under the guidance of Prof. Enhong Chen and Prof. Qi Liu. Previously, I obtained my Ph.D. degree under the supervision of Prof. Qi Liu.

Research Interests

My research centers on LLM-driven reasoning and AI agents, with a focus on context-aware reasoning, autonomous interactive, and continual learning and adaptation. This work is motivated by complex tasks in time-series intelligence and science intelligence (scientific knowledge and tool mining). These application-driven directions are framed by 科言 SciToken — understanding the scientific world, and 科语 SciTime — modeling the dynamic world.

  • 🤖LLMs and Agentic AI: Developing autonomous interactive learning for large language models, including environment-interactive Agentic RL, tool-augmented reasoning, multi-agent orchestration, and continual capability evolution through context, knowledge, and memory.
  • 📊Time Series Mining: Developing context-aware predictive intelligence, with a focus on multimodal context representation, slow-thinking reasoning, uncertainty-aware forecasting, and autonomous agentic interaction.
  • 🔬Scientific Literature Mining: Focusing on scientific knowledge acquisition, scientific knowledge parsing, scientific knowledge extraction, scientific knowledge-enhanced reasoning, and scientific knowledge innovation and discovery.

Application Domains

Real-world settings for developing and evaluating intelligent systems.

AI for Science

Focusing on scientific tools, scientific knowledge mining, and autonomous scientific discovery.

Scientific Literature MiningScientific Tool Mining

Personalized Intelligence

Focusing on user behavior and preference modeling for adaptive, personalized recommendations.

Behavior modelingContextual Memory

Forecasting Intelligence

Focusing on context-aware forecasting, uncertainty estimation, and adaptive decision support for complex, evolving systems.

Context-aware forecastingUncertainty & decisions
Research collections: 🤖 LLMs and Agentic AI（认知大模型） · 📊 Time Series Intelligence（科语） · 📚 Science Intelligence（科言）
Prospective students and research collaborators are welcome to explore the USTC-AGI Group and contact me by email.

Latest News

  • [Oct. 2026] 🎉 I will serve as an Area Chair (AC) for ICML 2027.
  • [Oct. 2026] 🎉 I will serve as an Area Chair (AC) for The Web Conference 2027 (WWW 2027).
  • [Sep. 2026] 🎉 Congratulations on our demo papers CastClaw and AgentWorld being accepted to the IEEE ICDM 2026 Demo Track!
  • [Sep. 2026] 🎉 Congratulations on our survey A Comprehensive Survey of Time Series Forecasting: Concepts, Challenges, and Future Directions being accepted by IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE)!
  • [Aug. 2026] 🎉 Congratulations on our paper PaperScout being accepted to Findings of EMNLP 2026!
  • [Aug. 2026] 🎉 We are excited to present our tutorial, “Context-Aware Time Series Forecasting: From Pattern Extrapolation to Cognitive Reasoning,” at IEEE ICDM 2026. See you in Shenyang!
  • [Aug. 2026] 🎉 I will serve as the Publication Chair for ICEBE 2026.
  • [Aug. 2026] 🎉 Congratulations on our papers Mind2Report, Time-R1, and AlphaCast being accepted to ACM CIKM 2026!
  • [Aug. 2026] 🎉 Congratulations on our survey A Survey on Table Mining with Large Language Models: Challenges, Advancements and Prospects being accepted by ACM Computing Surveys (ACM CSUR)!
  • [Aug. 2026] 🎉 Congratulations on our demo papers Agent-R1 and TabClaw being accepted to the ACM CIKM 2026 Demo Track!
  • [Jul. 2026] Our tutorial on Context-Aware Time Series Forecasting has been accepted by the IEEE ICDM 2026 Industry Tutorial Session. I will serve as a tutorial organizer and speaker.
  • [Jul. 2026] Our workshop, FedKDD/FedMAS 2026, will be held in conjunction with KDD 2026. I am honored to serve as the Program Chair.
  • [Jul. 2026] 🏆 Our Agentic Time Series Forecasting solution CastStar ranked #1 on the GIFT-Eval Overall leaderboard.
  • [Jun. 2026] 🎉 Our project CastClaw（星辰） received a Poster Presentation Certificate at the 2026 BAAI Conference Agent for Science Competition.
  • [Jun. 2026] 🎉 Congratulations on our Knowledge-Oriented RAG Survey being accepted to ACM Transactions on Information Systems (ACM TOIS)!
  • [May. 2026] 🎉 Congratulations to Zhiding Liu on his work DisenTS being accepted to IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)!
  • [May. 2026] 🎉 Congratulations to Xiaoyu Tao on her paper being accepted to ACM Transactions on Intelligent Systems and Technology (ACM TIST)!
  • [May. 2026] 🎉 Congratulations to two of my co-supervised students (Yucong Luo and Jie Ouyang) on receiving ByteDance Jindouyun offers!
  • [May. 2026] 🎉 Congratulations to Yitong Zhou on our paper ChemTable being accepted to KDD 2026!
  • [May. 2026] 🚀 We released CastFactory（炼星坊）, a framework for LLM-driven time series forecasting model training. GitHub
  • [May. 2026] 🎉 Congratulations to Xiaoyu Tao and Bohou Zhang on our papers (TokenCast and ScholarSum) being accepted to IJCAI 2026!
  • [May. 2026] 🎉 Congratulations to Yaguo Liu and Xiaoyu Tao on our papers (CoGenCast and MemCast) being accepted to ICML 2026!
  • [Apr. 2026] 📄 We released the preprint version of our survey on LLM-based agents from the contextual cognition perspective on Preprints.org: "A Comprehensive Survey of the LLM-Based Agent: The Contextual Cognition Perspective".
View All News →

Selected Publications

(* Corresponding Author, + Equal Contribution)

View all publications →

  1. Mingyue Cheng, Xiaoyu Tao, Qi Liu, Ze Guo, Enhong Chen, Position: Beyond Model-Centric Prediction — Agentic Time Series Forecasting. (Preprint) [PDF]
  2. Xiaoyu Tao, Mingyue Cheng, Bokai Pan, Chuang Jiang, Huanjian Zhang, Tian Gao, Yaguo Liu, Qi Liu, Enhong Chen, CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting. (Preprint) [ArXiv]
  3. Bokai Pan, Mingyue Cheng*, Zhiding Liu, Shuo Yu, Xiaoyu Tao, Yuchong Wu, Qi Liu, Defu Lian, Enhong Chen, CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting. (Preprint) [PDF] [Code]
  4. Xiaoyu Tao, Mingyue Cheng, Chuang Jiang, Tian Gao, Huanjian Zhang, Yaguo Liu, Qi Liu, Cast-R1: Learning Tool-Augmented Sequential Decision Policies for Time Series Forecasting. (Preprint) [PDF] [Code]
  5. Daoyu Wang, Mingyue Cheng*, Shuo Yu, Zirui Liu, Ze Guo, Xin Li, Qi Liu, PaperArena: An Evaluation Benchmark for Tool-Augmented Agentic Reasoning on Scientific Literature. (Preprint) [PDF] [Code]
  6. Daoyu Wang, Qingchuan Li, Mingyue Cheng, Jie Ouyang, Shuo Yu, Qi Liu, Enhong Chen, StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning. (Preprint) [PDF]
  7. Mingyue Cheng, Daoyu Wang, Shuo Yu, Qingchuan Li, Jie Ouyang, Yucong Luo, Yiju Zhang, Qi Liu*, Enhong Chen, A Comprehensive Survey of the LLM-Based Agent: The Contextual Cognition Perspective. (Preprint) [Preprint]

  1. Mingyue Cheng, Yucong Luo, Jie Ouyang, Qi Liu*, Huijie Liu, Li Li, Shuo Yu, Bohou Zhang, Jiawei Cao, Jie Ma, Daoyu Wang, Enhong Chen, A Survey on Knowledge-Oriented Retrieval-Augmented Generation. ACM Transactions on Information Systems (ACM TOIS) Accepted. [PDF] [Code]
  2. Mingyue Cheng, Zhiding Liu, Xiaoyu Tao, Qi Liu*, Jintao Zhang, Tingyue Pan, Shilong Zhang, Panjing He, Xiaohan Zhang, Daoyu Wang, Jiahao Wang, Enhong Chen, A Comprehensive Survey of Time Series Forecasting: Concepts, Challenges, and Future Directions. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE) Accepted. [PDF] [Code]
  3. Mingyue Cheng, Qingyang Mao, Qi Liu*, Yitong Zhou, Yupeng Li, Jiahao Wang, Jiaying Lin, Jiawei Cao, Enhong Chen, A Survey on Table Mining with Large Language Models: Challenges, Advancements and Prospects. ACM Computing Surveys (ACM CSUR) Accepted. [PDF] [Code]
  4. Tingyue Pan, Mingyue Cheng*, Shilong Zhang, Zhiding Liu, Xiaoyu Tao, Yucong Luo, Jintao Zhang, Qi Liu, OneCast: Structured Decomposition and Modular Generation for Cross-Domain Time Series Forecasting. ACM Transactions on Knowledge Discovery from Data (ACM TKDD) Accepted. [PDF]
  5. Mingyue Cheng, Daoyu Wang, Qi Liu*, Shuo Yu, Xiaoyu Tao, Yuqian Wang, Chengzhong Chu, Yu Duan, Mingkang Long, Enhong Chen, Mind2Report: A Cognitive Deep Research Agent for Expert-Level Commercial Report Synthesis. ACM CIKM 2026 Accepted. [PDF] [Code]
  6. Yitong Zhou, Yucong Luo, Mingyue Cheng*, Jiahao Wang, Daoyu Wang, Tingyue Pan, Jintao Zhang, Qi Liu, Enhong Chen, Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs. ACM CIKM 2026 Accepted. [PDF] [Code]
  7. Xiaohan Zhang, Tian Gao, Mingyue Cheng*, Bokai Pan, Ze Guo, Yaguo Liu, Xiaoyu Tao, Qi Liu, AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecasting. ACM CIKM 2026 Accepted. [PDF] [Code]
  8. Mingyue Cheng, Xiaoyu Tao, Huajian Zhang, Qi Liu*, Enhong Chen, InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancement. ACM Transactions on Intelligent Systems and Technology (ACM TIST) Accepted. [PDF] [Code]
  9. Yitong Zhou, Mingyue Cheng*, Qingyang Mao, Yucong Luo, Qi Liu, Yupeng Li, Xiaohan Zhang, Deguang Liu, Xin Li, Enhong Chen, Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables. KDD2026 Accepted. [PDF] [Code]
  10. Xiaoyu Tao, Mingyue Cheng*, Ze Guo, Shuo Yu, Yaguo Liu, Qi Liu, Shijin Wang, MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning. ICML2026 Accepted. [PDF]
  11. Mingyue Cheng, Yaguo Liu, Daoyu Wang, Xiaoyu Tao, Qi Liu*, CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting. ICML2026 Accepted. [PDF]
  12. Xiaoyu Tao, Shilong Zhang, Mingyue Cheng*, Daoyu Wang, Tingyue Pan, Bokai Pan, Changqing Zhang, Shijin Wang, From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization. IJCAI2026 Accepted. [PDF]
  13. Bohou Zhang, Xiaoyu Tao, Mingyue Cheng*, Huijie Liu, Qi Liu, ScholarSum: Student-Teacher Abstractive Summarization via Knowledge Graph Reasoning and Reflective Refinement. IJCAI2026 Accepted.
  14. Jiawei Cao, Jie Ouyang, Mingyue Cheng*, Zhaomeng Zhou, Chunli Liu, Yupeng Li, Zirui Liu, Shijin Wang, Re3: Relevance & Recency Retrieval for Mitigating Temporal Hallucination. ACL2026 Accepted.
  15. Huajian Zhang, Mingyue Cheng*, Yucong Luo, Xiaoyu Tao, STaR: Towards Cognitive Table Reasoning via Slow-Thinking Large Language Models. ACM WWW2026. [PDF] [Code]
  16. Zhiding Liu, Ben Chen, Mingyue Cheng*, Enhong Chen, Li Li, Chenyi Lei*, Wenwu Ou, Han Li and Kun Gai, Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce. ACM WWW2026. [PDF]
  17. Shuo Yu, Mingyue Cheng*, Daoyu Wang, Qi Liu, Zirui Liu, Ze Guo, Xiaoyu Tao, MemWeaver: A Hierarchical Memory from Textual Interactive Behaviors for Personalized Generation. ACM WWW2026. [PDF]
  18. Mingyue Cheng, Jiahao Wang, Daoyu Wang, Xiaoyu Tao, Qi Liu*, Enhong Chen, Can Slow-Thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting. ACM WSDM2026: 99–110, Feb 2026. [PDF] [Code]
  19. Mingyue Cheng, Xiaoyu Tao, Zhiding Liu, Qi Liu*, Hao Zhang, Rujiao Zhang and Enhong Chen, TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders, ACM WSDM2026: 498–508, Feb 2026. [arXiv version] [Code]
  20. Chuang Jiang, Mingyue Cheng*, Xiaoyu Tao, Qingyang Mao, Jie Ouyang and Qi Liu, TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoning. ACM WSDM2026: 260–270, Feb 2026. [PDF] [Code]
  21. Qingchuan Li, Mingyue Cheng*, Zirui Liu, Daoyu Wang, Yuting Zeng, Tongxuan Liu, From Hypothesis to Premises: LLM-based Backward Logical Reasoning with Selective Symbolic Translation. AAAI2026: 31671–31679, Mar 2026. [PDF] [DOI]
  22. Yupeng Li, Mingyue Cheng*, Yucong Luo, Yitong Zhou, Qingyang Mao, Shijin Wang, BLADE: A Behavior-Level Data Augmentation Framework with Dual Fusion Modeling for Multi-Behavior Sequential Recommendation. AAAI2026: 15189–15197, Mar 2026. [PDF] [DOI]
  23. Tingyue Pan, Jie Ouyang, Mingyue Cheng, Qingchuan Li, Zirui Liu, Daoyu Wang, Mingfan Pan, Shuo Yu, Qi Liu, PaperScout: An Autonomous Agent for Academic Paper Search with Process-Aware Sequence-Level Policy Optimization. Findings of EMNLP 2026 Accepted. [PDF]
  24. Xiaoyu Tao, Mingyue Cheng, Ze Guo, Bokai Pan, Qi Liu, Shijin Wang, Enhong Chen, CastClaw: A Human-in-the-Loop Autonomous Agent for Interactive Time Series Forecasting. IEEE ICDM 2026 Demo Track Accepted. [Code]
  25. Daoyu Wang, Mingyue Cheng, Qingchuan Li, Shuo Yu, Jie Ouyang, Qi Liu, Enhong Chen, AgentWorld: A Data Factory for Training LLM Agents from Multi-Turn Interactions. IEEE ICDM 2026 Demo Track Accepted. [Code]
  26. Mingyue Cheng, Shuo Yu, Daoyu Wang, Qingchuan Li, Xiaoyu Tao, Jie Ouyang, Yucong Luo, Yitong Zhou, Qi Liu*, Enhong Chen, Agent-R1: A Unified and Modular Framework for Agentic Reinforcement Learning. ACM CIKM2026 Demo Track Accepted.
  27. Mingyue Cheng, Shuo Yu, Daoyu Wang, Qingchuan Li, Xiaoyu Tao, Qingyang Mao, Yitong Zhou, Qi Liu*, TabClaw: An Interactive and Self-Evolving Agent for Spreadsheet Manipulation and Table Reasoning. ACM CIKM2026 Demo Track Accepted.

  1. Mingyue Cheng, Jiqian Yang, Tingyue Pan, Qi Liu*, Zhi Li, ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis, ACM WWW2025, Sydney, 2025. [PDF] [Code] — Included in sktime
  2. Xiaoyu Tao, Tingyue Pan, Mingyue Cheng*, Yucong Luo, Qi Liu, Enhong Chen, Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification. (ACM Transactions on Intelligent Systems and Technology (ACM TIST)) [PDF] [Code]
  3. Mingyue Cheng, Yiheng Chen, Qi Liu*, Zhiding Liu, Yucong Luo, Enhong Chen, InstrucTime: Advancing Time Series Classification with Multimodal Language Modeling, ACM WSDM2025 (Best of WSDM): 792–800, Hannover, 2025. [PDF] [Code] [Poster]
  4. Mingyue Cheng, Xiaoyu Tao, Qi Liu*, Hao Zhang, Yiheng Chen, Defu Lian, Cross-Domain Pre-training with Language Models for Transferable Time Series Representations, ACM WSDM2025: 175–183, Hannover, 2025. [PDF] [Code] [Poster]
  5. Daoyu Wang, Mingyue Cheng*, Zhiding Liu, Qi Liu, TimeDART: A Diffusion Autoregressive Transformer for Self-supervised Time Series Representation, ICML 2025, Vancouver, PMLR 267, 2025. [PDF] [Code]
  6. Jie Ouyang, Tingyue Pan, Mingyue Cheng*, Ruiran Yan, Yucong Luo, Jiaying Lin, Qi Liu, HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on RAG, ACL 2025: 6036–6063, Vienna, 2025. [PDF] [Code]
  7. Mingyue Cheng, Jintao Zhang, Zhiding Liu, Chunli Liu*, A Hybrid Multi-Factor Framework for Dynamic Intraoperative Hypotension Prediction, IJCAI 2025: 4923–4931, Montreal, 2025. [PDF]
  8. Jintao Zhang, Mingyue Cheng*, Xiaoyu Tao, Zhiding Liu, Daoyu Wang, Conditional Denoising Meets Polynomial Modeling: A Flexible Decoupled Framework for Time Series Forecasting, IJCAI 2025: 6993–7001, Montreal, 2025. [PDF] [Code]
  9. Yitong Zhou, Mingyue Cheng*, Qingyang Mao, Feiyang Xu, Xin Li, Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner, IJCAI 2025: 2503–2511, Montreal, 2025. [PDF] [Code]
  10. Hao Zhang, Mingyue Cheng*, Qi Liu, Zhiding Liu, Linbo Zhu, Yu Su, Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems, IJCAI 2025: 3624–3632, Montreal, 2025. [PDF] [Code]
  11. Jiahao Wang, Mingyue Cheng*, Qingyang Mao, Qi Liu, Feiyang Xu, Xin Li, Enhong Chen, TableTime: Reformulating Time Series Classification as Zero-Shot Table Understanding via LLMs, ACM CIKM 2025. [PDF] [Code]
  12. Shuo Yu, Mingyue Cheng*, Jiqian Yang, Jie Ouyang, et al., Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study, ACM CIKM 2025.

  1. Jie Ouyang, Yucong Luo, Mingyue Cheng*, Shuo Yu, Daoyu Wang, Qi Liu, Enhong Chen, Revisiting the Solution of Meta KDD Cup 2024: CRAG. (KDD Cup Workshop, 2nd Place in Task 2 & Task 3) [Slides] [Poster]
  2. Mingyue Cheng, Qi Liu*, Wenyu Zhang, Zhiding Liu, Hongke Zhao, Enhong Chen, A General Tail Item Representation Enhancement Framework for Sequential Recommender Systems, Frontiers of Computer Science (FCS), 2024, 18(6): 1–12. [PDF] [Code]
  3. Rujiao Zhang, Hao Zhang, Yucong Luo, Zhiding Liu, Mingyue Cheng, Qi Liu, Enhong Chen*, Learning the Dynamics in Sequential Recommendation by Exploiting Real-time Information, ACM CIKM2024: 4288–4292, Oct 2024. [PDF]
  4. Jie Wang, Fajie Yuan, Mingyue Cheng, et al., TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback, APWeb-WAIM2024: 193–208, Aug 2024. [PDF]
  5. Zhiding Liu, Jiqian Yang, Mingyue Cheng*, Yucong Luo, Zhi Li, Generative Pretrained Hierarchical Transformer for Time Series Forecasting, ACM SIGKDD2024: 2003–2013, Barcelona, 2024. [PDF]
  6. Yucong Luo, Mingyue Cheng, Hao Zhang, Junyu Lu, Enhong Chen*, Unlocking the Potential of Large Language Models for Explainable Recommendations. (DASFAA2024) [PDF] [Code]
  7. Junzhe Jiang, Shang Qu, Mingyue Cheng*, Qi Liu, et al., Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling. (DASFAA2024) [PDF] [Code]
  8. Mingyue Cheng, Hao Zhang, Qi Liu*, Fajie Yuan, Zhi Li, Zhenya Huang, Enhong Chen, Longfei Li, Jun Zhou, Empowering Sequential Recommender Systems from Mixture of Collaborative Signals and Semantic Relatedness. (DASFAA2024) [PDF] [Code]
  9. Hao Zhang, Mingyue Cheng*, Qi Liu, Yucong Luo, Rui Li, Enhong Chen, Learning Recommender Systems with Soft Target: A Decoupled Perspective. DASFAA 2024, LNCS 14852, Gifu, Japan, 2024. [PDF] [Code]
  10. Mingyue Cheng, Hao Zhang, Jiqian Yang, Qi Liu*, Li Li, Xin Huang, Liwei Song, Zhi Li, Zhenya Huang, Enhong Chen, Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform. (WWW2024)
  11. Junchen Fu, Fajie Yuan, Yu Song, Zheng Yuan, Mingyue Cheng, et al., Exploring Adapter-based Transfer Learning for Recommender Systems, ACM WSDM'2024: 208–217, Mar 2024. [PDF] [Code]

  1. Zhiding Liu, Mingyue Cheng, Zhi Li, Zhenya Huang, Qi Liu, Yanhu Xie, Enhong Chen, Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective, NeurIPS'2023: 36, New Orleans, 2023. [PDF] [Code]
  2. 耿杰, 刘春丽*, 魏雪梅, 程明月, 袁昆, 李洋, 刘业政, 基于用户重购行为的产品推荐方法, 计算机研究与发展 2023, 60(8): 1795–1807.
  3. Mingyue Cheng, Qi Liu*, Zhiding Liu, Zhi Li, Yucong Luo, Enhong Chen, FormerTime: Hierarchical Multi-scale Representation for Multivariate Time Series Classification, ACM WWW'2023: 1437–1445, Austin, 2023. [PDF] [Code]
  4. Wenqiang He, Mingyue Cheng, Qi Liu*, Zhi Li, ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification, DASFAA'2023: 353–369, Tianjin, 2023.
  5. Mingyue Cheng, Zhiding Liu+, Qi Liu*, Shenyang Ge, Enhong Chen, Towards Automatic Designing of Deep Hybrid Network Architecture for Sequential Recommendation, ACM WWW'2022: 1923–1932. [Code]
  6. Zhiding Liu, Mingyue Cheng, Zhi Li, Qi Liu, Enhong Chen*, One Person, One Model — Learning Compound Router for Sequential Recommendation, IEEE ICDM'2022. [Code]
  7. Junzhe Jiang, Mingyue Cheng, Qi Liu*, Zhi Li, Enhong Chen, Nested Named Entity Recognition from Medical Texts: A Multi-task Learning Approach, CAAI CICAI'2022: 248–259.
  8. Runlong Yu, Qi Liu*, Yuyang Ye, Mingyue Cheng, Enhong Chen, Jianhui Ma, Collaborative List-and-Pairwise Filtering from Implicit Feedback, IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2022, 34(6): 2667–2680.
  9. Kai Zhang, Qi Liu*, Zhenya Huang, Mingyue Cheng, Kun Zhang, Mengdi Zhang, Wei Wu, Enhong Chen, Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification, ACM SIGIR'2022: 1566–1576.
  10. Mingyue Cheng, Fajie Yuan+, Qi Liu*, Xin Xin, Enhong Chen, Learning Transferrable User Representations with Sequential Behaviors via Contrastive Pre-training, IEEE ICDM'2021: 51–60.
  11. Mingyue Cheng, Fajie Yuan+, Qi Liu*, Shenyang Ge, Zhi Li, Runlong Yu, Defu Lian, Senchao Yuan, Enhong Chen, Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement, ACM SIGIR'2021: 575–584.
  12. Linan Yue, Qi Liu*, Han Wu, Kai Zhang, Yanqing An, Mingyue Cheng, Biao Yin, Dayong Wu, NeurJudge: A Circumstance-aware Neural Framework for Legal Judgment Prediction, ACM SIGIR'2021: 973–982.
  13. Yanqing An, Qi Liu*, Han Wu, Kai Zhang, Linan Yue, Mingyue Cheng, Hongke Zhao, Senchao Yuan, Enhong Chen, LawyerPAN: A Proficiency Assessment Network for Trial Lawyers, ACM SIGKDD'2021: 5–13.
  14. Mingyue Cheng, Runlong Yu, Qi Liu*, Hongke Zhao, Hefu Zhang, Enhong Chen, Alpha-Beta Sampling for Pairwise Ranking in One-Class Collaborative Filtering, IEEE ICDM'2019: 1000–1005.
  15. 程明月, 刘淇*, 李徵, 于润龙, 高维博, 陈恩红, 多重对级贝叶斯个性化排序算法. (南京信息工程大学学报自然科学版, 2019(03): 302–308)

Systems Product

科言乐问

科言 · Science Intelligence

Discover scientific literature through quick search and in-depth retrieval.

lewen.bdaa.pro Visit website

科言文修

科言 · Science Intelligence

Support academic writing with polishing, proofreading, and translation tools.

writelearn.bdaa.pro Visit website

Open Project

LLMs and Agentic AI

Agent-R1
2025.04 CIKM 2026 GitHub Website Docs 1661
Agent-R1 is a unified, modular training framework for Agentic RL. It models each round of agent interaction as a step-level RL transition and decouples trajectory representation, context construction, environment interfaces, and optimization algorithms. This allows GRPO, PPO, and other algorithms to be compared and extended on a shared foundation for multi-turn agent training.
Claw-R1
2026.03 GitHub Website 194
Claw-R1 provides the Data Foundation for Agentic RL, enabling the systematic collection, evaluation, and curation of high-quality training data from diverse agent interactions. It introduces a middleware layer (Gateway + DataPool) between the Agent Side and the Training Side, focusing on data infrastructure rather than training algorithms themselves.

Time Series Intelligence（科语）

CastClaw（星辰）
2026.04 GitHub Website Docs 47
CastClaw（星辰） is an agent framework for human–AI collaboration in time series forecasting research. It uses three specialized agents, Planner, Forecaster, and Critic, to orchestrate the complete forecasting workflow and incorporates human confirmation at key stages. It packages data analysis, feature engineering, and classical time series model capabilities into an extensible runtime toolbox.
CastMind（星思）
GitHub 0
CastMind（星思） is a large model for time series projection in complex systems, driven by contextual reasoning. It integrates historical time series, external context, and domain knowledge and follows an extrapolate → understand → project → revise process for extended cognitive reasoning. Under specific conditions, it assesses whether future trends will persist, strengthen, weaken, shift, or reverse, enabling forecast revision and explanations of the underlying mechanisms.

Science Intelligence（Scientific Tool and Knowledge）

PaperScout（科言乐问）
2026.01 EMNLP 2026 GitHub Website Paper 9
PaperScout is an autonomous scientific literature retrieval agent for complex research queries. It treats paper discovery as multi-turn decision-making, using accumulated retrieval context to choose between Search for new candidates and Expand for following paper references. With process-aware, sequence-level reinforcement learning (PSPO), it learns to refine its retrieval strategy from intermediate feedback and identify papers that match the research topic and query constraints.
Academic Search
2026.04 GitHub 616
Academic Search is a scientific literature retrieval and metadata skill for Codex, Claude Code, and other AI agents. It brings together sources including arXiv, Semantic Scholar, OpenAlex, Google Scholar, and CNKI, supporting query expansion, citation tracing, record validation, cross-source deduplication, and BibTeX export, as well as open-access full-text retrieval. It adapts search strategies to the discipline and records source provenance and access status to support literature reviews and research surveys.

Benchmarks & Datasets

Time Series Intelligence（科语）

Time Series Intelligence · Context-aware Forecasting
FutureCast（天星台）
2026.05 GitHub Website 2
FutureCast（天星台） is a multi-domain benchmark for context-aware time series forecasting. It pairs historical observations with numerical covariates, textual descriptions, and event context, and centers its evaluation design on context–sequence alignment, contextual reasoning, and adaptation to new evidence. Designed for time series foundation models and forecasting agents, it examines how external evidence informs trend judgments and forecast revision, with emphasis on forecast accuracy, reasoning quality, and evidence-based explanations.

Science Intelligence（Scientific Tool and Knowledge）

Scientific Literature · Agentic Evaluation
PaperArena Scientific Literature Mining Paper
2025.10 Website GitHub 18
PaperArena: An Evaluation Benchmark for Tool-Augmented Agentic Reasoning on Scientific Literature benchmarks agentic systems on scientific reading and reasoning with tool use. It targets literature understanding, tool-augmented reasoning, and evidence-grounded evaluation over scientific papers and related scholarly workflows.
AI for Science · Chemical Tables
ChemTable Scientific Literature Mining Paper
2025.06 KDD 2026 GitHub 8
Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables introduces ChemTable, a real-world benchmark curated from chemical literature. It supports two core tasks: table recognition and table understanding, with expert annotations over cell polygons, logical layouts, and chemistry-specific semantic labels such as reagents, catalysts, yields, and graphical components.

Retrieval & Recommendation

E-Commerce Search · Recall to Relevance
KuaiSearch Search-based Recommendation Paper
2026.02 Website GitHub 31
KuaiSearch: A Large-Scale E-Commerce Search Dataset for Recall, Ranking, and Relevance is built from real user search interactions on Kuaishou. It preserves authentic user queries and natural-language product texts, covers cold-start users and long-tail products, and spans the three key stages of modern search systems: recall, ranking, and relevance judgment.
RAG Evaluation · Dynamic Benchmark
HoH Retrieval-Augmented Generation Paper
2025.06 ACL 2025 GitHub 7
HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on RAG studies how retrieval-augmented generation systems fail when knowledge becomes stale. It provides a dynamic evaluation setting for measuring temporal robustness, outdated-information sensitivity, and the impact of knowledge freshness in modern RAG pipelines.

Education

Professional Experience

  • Jun. 2023 ~ Present, Associate Researcher, School of Computer Science and Technology, USTC, Hefei, China
  • Jan. 2021 ~ Jun. 2023, Research Assistant, Research Center for Big Data in Intelligent Healthcare, USTC, Hefei, China
  • Jun. 2019 ~ Oct. 2019, Research Intern, Data Service Center (DSC), Platform and Content Group (PCG), Tencent, Shenzhen, China
  • Jul. 2018 ~ Sep. 2018, Engineer Intern, Lab of Big Data, Core Technology R&D Platform, iFLYTEK, Hefei, China

Teaching

  • Introduction to Data Science（数据分析及实践）, Undergraduate Course, Spring 2024.
  • Introduction to Data Science（数据分析及实践）, Undergraduate Course, Spring 2025.
  • Introduction to Data Science（数据分析及实践）, Undergraduate Course, Spring 2026.
  • Introduction to Systems Programming（系统程序设计基础）, Undergraduate Course, Spring 2026.

Honors and Awards

  • Aug. 2025, 中国科学技术大学红专青年人才
  • Mar. 2025, Best of WSDM2025（优秀论文奖）
  • Dec. 2024, 昇腾人工智能创新大赛全国总决赛金奖（指导教师）
  • Aug. 2024, Second Place of Task2 & Task3 in KDD Cup'2024
  • Jan. 2024, 中国科学技术大学雄鹰创新创业基金（指导教师）
  • Dec. 2023, 2023年中国国际大学生创新大赛国家级银奖（教育部）
  • Jul. 2023, 全国"互联网+"大学生创新创业大赛安徽省金奖（安徽省教育厅）
  • Mar. 2023, 中国科学技术大学优秀毕业生
  • May. 2022, 2022年中国科学技术大学"创新创业"基金
  • Dec. 2021, "创青春"中国青年创新创业项目支持——大学生创业"金种子"计划（团中央）
  • Sep. 2021, 博士研究生国家奖学金（教育部）

Professional Service

Program Committee Member
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD): 2023, 2024, 2025, 2026, 2027
  • ACM International World Wide Web Conference (TheWebConf): 2024, 2025, 2026, 2027 (Area Chair 2027)
  • International Conference on Web Search and Data Mining (WSDM): 2025, 2027
  • ACM International Conference on Information and Knowledge Management (CIKM): 2024, 2025
  • IEEE International Conference on Data Mining (ICDM)
  • International Conference on Machine Learning (ICML): 2025, 2026
  • International Conference on Learning Representations (ICLR): 2025, 2027 (Area Chair 2027)
  • Conference on Neural Information Processing Systems (NeurIPS): 2024, 2025, 2026
  • Annual Meeting of the Association for Computational Linguistics (ACL): 2025, 2026
  • Conference on Empirical Methods in Natural Language Processing (EMNLP)
  • AAAI Conference on Artificial Intelligence (AAAI): 2026, 2027
  • ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL): 2026
  • International Joint Conference on Artificial Intelligence (IJCAI): 2024, 2025, 2026 (SPC)
  • SIAM International Conference on Data Mining (SDM): 2024
  • Database Systems for Advanced Applications (DASFAA): 2024, 2025
Journal Reviewer
  • IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • IEEE Transactions on Neural Network and Learning Systems (IEEE TNNLS)
  • IEEE Transactions on Industrial Informatics (IEEE TII)
  • IEEE Transactions on Audio, Speech and Language Processing (IEEE T-ASL)
  • ACM Transactions on Knowledge Discovery from Data (TKDD)
  • ACM Transactions on Intelligent Systems and Technology (TIST)
  • Transactions on Machine Learning Research (TMLR)
  • Neurocomputing
  • Frontiers of Computer Science (FCS)
  • Science China Information Sciences (SCIS)
  • 软件学报
  • 计算机学报
Academic Organization Service
  • IEEE Task Force on Data-Efficient Agentic Learning (DEAL)
  • IEEE Task Force on AI for Time Series and Spatio-Temporal Data
  • Technical Committee on Artificial Intelligence and Pattern Recognition, China Computer Federation (CCF) — Executive Committee Member
  • Information Retrieval Technical Committee, Chinese Information Processing Society of China (CIPS) — Corresponding Member
Conference Organization
  • IEEE ICEBE 2026 — Publication Chair

Research Grants

  • 2026.07–2029.06, Chinese Academy of Sciences Strategic Priority Research Program for Basic and Interdisciplinary Frontier Research (Category B); Mechanisms and Methods for Autonomous Interactive Learning in Large Models; Project Lead
  • 2026.08–2028.07, New-Generation Artificial Intelligence National Science and Technology Major Project; Scientific Data Governance Toolchain and Datasets — Chemistry; Core Project Member
  • 2026.01–2028.12, National Natural Science Foundation of China — Young Scientists Fund (Category C); Cross-Domain Context-Aware Time Series Representation Learning and Forecasting; Project Lead
  • 2027.01–2028.12, USTC Youth Innovation Fund Project; Multi-Turn Interactive Learning and Continual Evolution for Large-Model Agents: Methods and Applications; Project Lead
  • 2025.01–2026.12, USTC New Medicine Joint Fund Cultivation Project (Double First-Class Discipline Development Special Program); Time Series Modeling Methods and Applications Using Perioperative Physiological Data; Project Lead
  • 2024.09–2026.08, Anhui Provincial Natural Science Foundation; Table Semantic Understanding and Reasoning for Scientific Literature; Project Lead

My research is also partially supported by industry grants from:

Related Links

Prof. Enhong Chen
University of Science and Technology of China
USTC
Prof. Qi Liu
University of Science and Technology of China
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Prof. Defu Lian
University of Science and Technology of China
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Prof. Le Wu
Hefei University of Technology
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Prof. Tong Xu
University of Science and Technology of China
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Prof. Hongke Zhao
Tianjin University
TJU