[2605.05409] Agentic Retrieval-Augmented Generation for Financial Document Question Answering
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arXiv:2605.05409 (cs)
[Submitted on 6 May 2026]
Title:Agentic Retrieval-Augmented Generation for Financial Document Question Answering
Authors:Yang Shu, Yingmin Liu, Zequn Xie
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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 framework that orchestrates iterative retrieval-reasoning loops with self-verification, specifically engineered for the precision requirements of financial numerical reasoning. The framework integrates three domain-specific innovations: (1) a Contrastive Financial Retriever trained with hard negative mining to distinguish semantically similar but numerically distinct financial passages, (2) a Program-of-Thought reasoning module that generates executable Python code for precise arithmetic rather than relying on error-prone LLM-based mental computation, and (3) an Adaptive Strategy Router that dynamically allocates computational resources based on question complexity, reducing API costs by 41.3% on FinQA while preserving accuracy. Extensive experiments on three benchmark datasets--FinQA, ConvFinQA, and TAT-QA--demonstrate that FinAgent-RAG achieves 76.81%, 78.46%, and 74.96% execution accuracy respectively, outperforming the strongest baseline by 5.62--9.32 percentage points. Ablation studies, cross-backbone evaluation with four LLMs, and deployment cost analysis confirm the framework's robustness and practical viability for financial institutions.
Comments: 22 pages, 11 figures, 13 tables, submitted to Expert Systems with Applications
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
ACM classes: I.2.7; H.3.3
Cite as: arXiv:2605.05409 [cs.AI]
(or arXiv:2605.05409v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.05409
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yang Shu [view email] [v1] Wed, 6 May 2026 19:59:51 UTC (3,543 KB)
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