Chenghao Zhang

"Against stupidity the very gods
Themselves contend in vain."

I'm Chenghao David Zhang, a 📚 Ph.D. student at the Gaoling School of Artificial Intelligence, Renmin University of China, under the supervision of Prof. Zhicheng Dou. I earned my 🎓 B.Eng. degree in Software Engineering from the School of Computer Science, Beijing University of Posts and Telecommunications in 2024. My research focuses on 🌈 multimodal information retrieval and 👀 vision-language models. Outside my main research, I have a strong interest in 🧩 computer graphics.

Always keen to explore cool, interesting, and meaningful research problems that push the boundaries of AI and create real-world impact.

Email / Google Scholar / Twitter / Github / bilibili

Research

I'm currently working on a unified multimodal retriever for RAG systems. Below are my prior works, with key papers highlighted.

	Progressive Multimodal Reasoning via Active Retrieval Guanting Dong, Chenghao Zhang, Mengjie Deng, Yutao Zhu, Zhicheng Dou, Ji-Rong Wen ACL, 2026 arXiv A framework designed to progressively improve the reasoning capabilities of MLLMs through Active Retrieval (AR) and Monte Carlo Tree Search (MCTS).
	FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research Jiajie Jin, Yutao Zhu, Guanting Dong, Yuyao Zhang, Xinyu Yang, Chenghao Zhang, Tong Zhao, Zhao Yang, Zhicheng Dou, Ji-Rong Wen WWW, Short-Paper, 2025 arXiv \| github project A Python toolkit for the reproduction and development of RAG research. Including 36 pre-processed benchmark RAG datasets and 23 state-of-the-art RAG algorithms, and 7 reasoning-based methods that combine reasoning ability with retrieval.
	Understand what LLM needs: Dual preference alignment for retrieval-augmented generation Guanting Dong, Yutao Zhu, Chenghao Zhang, Zechen Wang, Ji-Rong Wen, Zhicheng Dou WWW, 2025 arXiv A framework designed to align diverse knowledge preferences within RAG systems
	INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zheng Liu, Jirong Wen, Zhicheng Dou ACL, 2024 arXiv \| bibtex \| code \| dataset A novel instruction tuning dataset, INTERS, encompassing 20 tasks across three fundamental IR categories: query understanding, document understanding, and query-document relationship understanding. The data are derived from 43 distinct datasets with manually written templates.

Services

Teaching Assistant

Data Structures and Algorithms, 2024 Fall
Few Shot Learning, 2025 Fall

This website is adapted from Jon Barron's template. Many thanks!