MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration
arXiv:2605.25030v1 Announce Type: new Abstract: 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 introduces MimirRAG (Metadata-Integrated Multi-Agent Information Retrieval)
The increasing sophistication of LLMs and the critical need for verifiable, hallucination-free AI in finance are driving the development of advanced RAG systems like MimirRAG.
Sophisticated financial AI, grounded in verifiable data, enables more accurate analysis and automated decision-making workflows, impacting market efficiency and investment strategies.
The ability to integrate multi-agent frameworks with metadata for RAG systems changes how LLMs can reliably process and interpret complex, mixed financial documents.
- · Financial institutions
- · AI developers
- · Data analytics companies
- · Investors
- · Traditional manual financial analysis
- · Generic RAG systems
- · Unaudited AI tools
Financial professionals gain access to highly reliable AI tools for data retrieval and analysis.
Increased efficiency and accuracy in financial decision-making could lead to new financial products and services.
Automation of complex financial analysis shifts labor demands and accelerates market reactions to information.
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Read at arXiv cs.LG