AI RESEARCH
MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration
arXiv CS.LG
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ArXi:2605.25030v1 Announce Type: new Retrieval-augmented generation (RAG) systems offer a promising approach to reduce hallucinations and improve answer accuracy in large language models (LLMs), a requirement for reliable, financial analysis where answers must be grounded in verifiable evidence from filings rather than generated from model priors. However, designing RAG systems that extract meaningful insights from mixed financial documents and integrate into analyst workflows remains challenging. This paper