Agentic Retrieval-Augmented Generation for Financial Document Question Answering

📰 ArXiv cs.AI

Learn how FinAgent-RAG enhances financial document question answering with agentic retrieval-augmented generation, improving compositional reasoning over heterogeneous evidence

advanced Published 9 May 2026

Action Steps

Implement FinAgent-RAG framework to augment existing RAG approaches
Configure the agentic retrieval module to handle heterogeneous evidence
Train the generation model using financial document datasets
Evaluate the performance of FinAgent-RAG on financial QA tasks
Fine-tune the model to adapt to specific financial analysis requirements

Who Needs to Know This

Data scientists and NLP engineers working on financial document analysis can benefit from FinAgent-RAG to improve question answering accuracy and efficiency

Key Insight

💡 Agentic retrieval-augmented generation can improve compositional reasoning over heterogeneous evidence in financial document analysis

Key Takeaways

Learn how FinAgent-RAG enhances financial document question answering with agentic retrieval-augmented generation, improving compositional reasoning over heterogeneous evidence

Full Article

Title: Agentic Retrieval-Augmented Generation for Financial Document Question Answering

Abstract:
arXiv:2605.05409v1 Announce Type: new Abstract: 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. We propose FinAgent-RAG, an agentic RAG framew

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