activity
20242026
collaborators

5 papers

cs.CV2026

AgenticOCR: Parsing Only What You Need for Efficient Retrieval-Augmented Generation

Zhengren Wang, Dongsheng Ma, Huaping Zhong +4

The expansion of retrieval-augmented generation (RAG) into multimodal domains has intensified the challenge for processing complex visual documents, such as financial reports. Whil…

cs.CL2026

DocDancer: Towards Agentic Document-Grounded Information Seeking

Qintong Zhang, Xinjie Lv, Jialong Wu +8

Document Question Answering (DocQA) focuses on answering questions grounded in given documents, yet existing DocQA agents lack effective tool utilization and largely rely on closed…

cs.CV2025

DOCR-Inspector: Fine-Grained and Automated Evaluation of Document Parsing with VLM

Qintong Zhang, Junyuan Zhang, Zhifei Ren +8

Document parsing aims to transform unstructured PDF images into semi-structured data, facilitating the digitization and utilization of information in diverse domains. While vision…

cs.CV2024

OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation

Junyuan Zhang, Qintong Zhang, Bin Wang +6

Retrieval-augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge to reduce hallucinations and incorporate up-to-date information without…

cs.CL2024

Can LLMs be Good Graph Judge for Knowledge Graph Construction?

Haoyu Huang, Chong Chen, Zeang Sheng +2

In real-world scenarios, most of the data obtained from the information retrieval (IR) system is unstructured. Converting natural language sentences into structured Knowledge Graph…