๐ฌ My research focuses on Large Language Model Evaluation and Dialogue Systems. Recently, I have been particularly interested in Context Learning and AI for Academia.
๐ข I previously interned at ByteDance (2024.09 โ 2025.09) and am currently interning at Tencent through Tencent Project UP (่ พ่ฎฏ้ไบ่ฎกๅ, 2025.12 โ Present).
๐ I was selected for the CAST Young Talent Cultivation Program for Ph.D. Students (ไธญๅฝ็งๅ้ๅนด็งๆไบบๆๅน่ฒๅทฅ็จๅๅฃซ็ไธ้กน่ฎกๅ, 2026).
๐ I serve as a Reviewer for AAAI, ACL ARR, ICLR, NeurIPS, and ICML, and as an Area Chair for ACL ARR. I was recognized as an Outstanding Reviewer for the ACL Rolling Review (ARR) May 2026 cycle (EMNLP 2026) and a Silver Reviewer for ICML 2026.
Beyond research, my personal interests include:
๐ญ I have been a lifelong astronomy enthusiast and served as President of the Fudan Astronomy Society during my undergraduate years.
โฝ I am a devoted fan of football โ my all-time idol is Lionel Andrรฉs Messi. Congratulations to Messi and Argentina on reaching the 2026 FIFA World Cup Final โ his third World Cup final! ๐ฆ๐ท
๐ฎ I served as Captain of the Fudan University League of Legends Varsity Team (Jungle), peaked at Challenger (ๆๅผบ็่ ) in S3 and S12, and my favorite champion lately is Gangplank.
๐ง mingzhang23 [at] m [dot] fudan [dot] edu [dot] cn / konglongge [at] outlook [dot] com
๐ฌ Please feel free to add me on WeChat: zanyingluan
๐ฅ News
2026.10 ๐ Selected for the CAST Young Talent Cultivation Program for Ph.D. Students (ไธญๅฝ็งๅๅๅฃซ็้ๅน)!
2026.09 ๐ ExplorationBench is now available on arXiv! Can AI systems explore and discover genuinely new knowledge in verifiable Alien Worlds? Check out our project website and GitHub repository.
2026.08 ๐ Tencent Hy4 preview (770B-A49B, 1M context) is released and open-sourced โ proud to have contributed to it!
2026.08 ๐ SpeechRole and TIDE accepted by EMNLP 2026 Main; JFTA-Bench accepted by EMNLP 2026 Findings!
2026.07 ๐ Tencent Hy3 (295B-A21B) is officially released and open-sourced โ proud to have contributed to it!
2026.07 โ๏ธ Attended ACL 2026 in San Diego, USA.
2026.05 ๐ LLMEval-Logic is now available on arXiv!
2026.04 ๐ Tencent Hy3 preview (295B-A21B), the first model after the Hunyuan rebuild, is released and open-sourced โ proud to have contributed to it!
A benchmark for evaluating whether AI systems can acquire genuinely new knowledge through exploration. Built on verifiable Alien Worlds whose executable rules conflict with familiar knowledge, it contains two sandboxes, AlienCode and AlienLogic, where systems must frame hypotheses, design experiments, and iterate before solving held-out tasks.
A comprehensive benchmark for evaluating context learning capabilities of large language models, providing systematic assessment across diverse context-dependent tasks.
An LLM-powered agentic system that performs verifiable scholarly novelty assessment, automatically evaluating the originality of research ideas through systematic literature analysis.
A large-scale longitudinal study on the robustness and fairness of LLM evaluation, addressing critical issues in benchmarking consistency and providing reliable evaluation methodologies.
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 Guiโ , Chao Xin, Chengzhi Wei, Lin Yan, Yonghui Wu, Qi Zhangโ , Xuanjing Huangโ
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.
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.
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities arXiv 2024.01