👋 Hi, I'm Ming Zhang (张明), also known as DinoBro (恐龙哥) or DinoDoctor (恐龙博士). I am a third-year direct Ph.D. student at the FudanNLP Lab, School of Computer Science, Fudan University, co-advised by Prof. Qi Zhang, A.P. Tao Gui, and Prof. Xuanjing Huang.

🔬 My research focuses on Large Language Model Evaluation and Dialogue Systems. Recently, I have been particularly interested in Context Learning and AI for Academia.

🏢 I previously interned at ByteDance (2024.09 – 2025.09) and am currently interning at the Tencent Qingyun Program (2025.12 – Present).

📝 I serve as a Reviewer for AAAI, ACL ARR, ICLR, NeurIPS, and ICML, and as an Area Chair for ACL ARR.

Beyond research, my personal interests include:

🔭 I have been a lifelong astronomy enthusiast and served as President of the Fudan Astronomy Society during my undergraduate years.

⚽ I am a devoted fan of football — my all-time idol is Lionel Andrés Messi.

🎮 I served as Captain of the Fudan University League of Legends Varsity Team (Jungle), peaked at Challenger (最强王者) in S3 and S12, and my favorite champion lately is Gangplank.

📧 mingzhang23 [at] m [dot] fudan [dot] edu [dot] cn  /  konglongge [at] outlook [dot] com

💬 Please feel free to add me on WeChat: zanyingluan

🔥 News

• 2026.02 🎉 Thinking with Video accepted by CVPR 2026!
• 2026.02 📄 CL-bench is now available on arXiv!
• 2026.01 📄 OpenNovelty and TaxoBench are now available on arXiv!
• 2026.01 🎉 Game-RL accepted by ICLR 2026!
• 2025.11 🎉 Reasoning or Memorization and Speech Tokenizer accepted by AAAI 2026!
• 2025.09 🎉 EvaLearn accepted by NeurIPS 2025!
• 2025.08 🎉 LLMEval-Med accepted by EMNLP 2025!
• 2025.05 🎉 PFDial accepted by ACL 2025!
• 2025.01 🎉 Our LLM Agent Survey published in Science China Information Sciences!
• 2024.09 🎉 TransferTOD and MathTrap accepted by EMNLP 2024!
• 2024.07 🎉 Mousi accepted by COLM 2024!
• 2023.12 🎉 LLMEval accepted by AAAI 2024!

⭐ Selected Works

^* denotes co-first author, ^† denotes corresponding author.

Context Learning

CL-bench: A Benchmark for Context Learning

Shihan Dou^*, Ming Zhang^*, Zhangyue Yin^*, Chenhao Huang, Yujiong Shen, Junzhe Wang, Jiayi Chen, Yuchen Ni, Junjie Ye, Cheng Zhang, Huaibing Xie, Jianglu Hu, Shaolei Wang, Weichao Wang, Yanling Xiao, Yiting Liu, Zenan Xu, Zhen Guo, Pluto Zhou^†, Tao Gui^†, Zuxuan Wu, Xipeng Qiu, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Di Wang, Shunyu Yao

A comprehensive benchmark for evaluating context learning capabilities of large language models, providing systematic assessment across diverse context-dependent tasks.

Paper Code Leaderboard

AI for Academia

OpenNovelty: An LLM-Powered Agentic System for Verifiable Scholarly Novelty Assessment

Ming Zhang^*†, Kexin Tan^*, Yueyuan Huang^*, Yujiong Shen, Chunchun Ma, Li Ju, Xinran Zhang, Yuhui Wang, Wenqing Jing, Jingyi Deng, Huayu Sha, Binze Hu, Jingqi Tong, Changhao Jiang, Yage Geng, Yuankai Ying, Yue Zhang, Zhangyue Yin, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang^†, Xuanjing Huang

An LLM-powered agentic system that performs verifiable scholarly novelty assessment, automatically evaluating the originality of research ideas through systematic literature analysis.

Paper Code Website

LLM Evaluation

LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models

Ming Zhang^*†, Yujiong Shen^*, Jingyi Deng^*, Yuhui Wang^*, Huayu Sha, Kexin Tan, Qiyuan Peng, Yue Zhang, Junzhe Wang, Shichun Liu, Yueyuan Huang, Jingqi Tong, Changhao Jiang, Yilong Wu, Zhihao Zhang, Mingqi Wu, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang^†, Xuanjing Huang

A large-scale longitudinal study on the robustness and fairness of LLM evaluation, addressing critical issues in benchmarking consistency and providing reliable evaluation methodologies.

Paper Code

LLM Evaluation

EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving NeurIPS 2025

Shihan Dou^*, Ming Zhang^*, Chenhao Huang, Jiayi Chen, Feng Chen, Shichun Liu, Yan Liu, Chenxiao Liu, Cheng Zhong, Zongzhang Zhang, Tao Gui^†, Chao Xin, Chengzhi Wei, Lin Yan, Yonghui Wu, Qi Zhang^†, Xuanjing Huang^†

A novel framework for quantifying the learning capability and efficiency of large language models through sequential problem solving, providing new insights into how LLMs acquire and apply knowledge.

Paper Code

Agent Survey

The Rise and Potential of Large Language Model Based Agents: A Survey SCIS

Zhiheng Xi^*, Wenxiang Chen^*, Xin Guo^*, Wei He^*, Yiwen Ding^*, Boyang Hong^*, Ming Zhang^*, Junzhe Wang^*, Senjie Jin^*, Enyu Zhou^*, Rui Zheng, Xiaoran Fan, Xiao Wang, Limao Xiong, Yuhao Zhou, Weiran Wang, Changhao Jiang, Yicheng Zou, Xiangyang Liu, Zhangyue Yin, Shihan Dou, Rongxiang Weng, Wenjuan Qin, Yongyan Zheng, Xipeng Qiu, Xuanjing Huang, Qi Zhang^†, Tao Gui^†

A comprehensive survey on LLM-based agents covering their construction, applications, and evaluation. This highly influential work provides a systematic overview of the emerging field of autonomous agents powered by large language models. Published in Science China Information Sciences.

Paper Code

📝 Publications

LLM Evaluation

AI for Academia

Dialogue Systems

Others

📊 Visitors