AI RESEARCH
Agentic Retrieval-Augmented Generation for Financial Document Question Answering
arXiv CS.CL
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ArXi:2605.05409v1 Announce Type: cross Financial document question answering (QA) demands complex multi-step numerical reasoning over heterogeneous evidence--structured tables, textual narratives, and footnotes--scattered across corporate filings. Existing retrieval-augmented generation (RAG) approaches adopt a single-pass retrieve-then-generate paradigm that struggles with the compositional reasoning chains prevalent in financial analysis.