1 citations · 1 across the 6 of their papers we have counts for
6 papers
Mr.LHDR: A Benchmark for Multimodal Real-World Long-Horizon Deep Research Agents
Minghao Guo, Meng Cao, Sui Zhao +10
Deep research agents are increasingly capable of web search, tool use, multimodal evidence analysis, and information synthesis. However, existing benchmarks mainly evaluate medium-…
MCite-RL: Towards Reliable Multimodal RAG via Citation-enhanced Agentic Reinforcement Learning
Suifeng Zhao, Zida Liu, Xinyu Lei +3
Multimodal Retrieval-Augmented Generation (RAG) with visual citation is crucial for ensuring the traceability and verifiability of MLLMs. However, current RAG and SFT-based methods…
DB3 Team's Solution For Meta KDD Cup' 25
Yikuan Xia, Jiazun Chen, Yirui Zhan +6
This paper presents the db3 team's winning solution for the Meta CRAG-MM Challenge 2025 at KDD Cup'25. Addressing the challenge's unique multi-modal, multi-turn question answering…
FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain
Suifeng Zhao, Zhuoran Jin, Sujian Li +1
Retrieval-Augmented Generation (RAG) plays a vital role in the financial domain, powering applications such as real-time market analysis, trend forecasting, and interest rate compu…
ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources
Yikuan Xia, Jiazun Chen, Yirui Zhan +6
Large language models (LLMs) excel in question-answering (QA) tasks, and retrieval-augmented generation (RAG) enhances their precision by incorporating external evidence from diver…
Citekit: A Modular Toolkit for Large Language Model Citation Generation
Jiajun Shen, Tong Zhou, Yubo Chen +1
Enabling Large Language Models (LLMs) to generate citations in Question-Answering (QA) tasks is an emerging paradigm aimed at enhancing the verifiability of their responses when LL…