Agentic Retrieval-Augmented Generation for Financial Document Question Answering

This paper introduces FinAgent-RAG, an agentic RAG framework for financial document question answering, focusing on complex numerical reasoning. It orchestrates iterative retrieval-reasoning loops with self-verification, incorporating a Contrastive Financial Retriever and a Program-of-Thought module for precise arithmetic.