About
I am Chunyi Peng(彭淳毅), a first-year M.S. student at Northeastern University, advised by Zhenghao Liu and Yukun Yan. I'm currently interning at ByteDance Seed.
My current research interests focus on Multimodal Retrieval-Augmented Generation (MRAG) and Multimodal Large Language Models (MLLM) and Agentic RL.
News
Our work 'Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains' is accepted by ACL2026!
Our work 'Mixture-of-Retrieval Experts for Reasoning-Guided Multimodal Knowledge Exploitation' is accepted by SIGIR2026!
UltraRAG has reached 5,500 stars on GitHub!
Selected Publications
View All →Mixture-of-Retrieval Experts for Reasoning-Guided Multimodal Knowledge Exploitation
Chunyi Peng†, Zhipeng Xu†, Zhenghao Liu, Yukun Yan, Others
SIGIR
This paper introduces MoRE, a multimodal RAG framework that lets MLLMs dynamically coordinate text, image, and table retrieval experts during reasoning. It further proposes Step-GRPO to train expert routing with fine-grained stepwise feedback, improving both answer accuracy and retrieval efficiency.
VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation
Yubo Sun†, Chunyi Peng†, Yukun Yan, Zhenghao Liu, Others
Preprint
This paper introduces EVisRAG, a visual retrieval-augmented framework that improves multi-image reasoning by explicitly collecting question-relevant evidence from retrieved images before generating an answer. It also proposes RS-GRPO, a reward-scoped training strategy that strengthens evidence grounding and reduces visual hallucinations in visual question answering.
UltraRAG v2: A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines
Sen Mei, Haidong Xin, Chunyi Peng, Yukun Yan, Others
OpenBMB
A Low-Code MCP Framework for Building Complex and Innovative Retrieval-Augmented Generation systems.