Towards Expert Financial QA via Self-Improving RAG

Abstract:Expert-level financial question answering requires both grounded verification to catch numeric hallucinations and audit trails for regulatory compliance, attributes that standard single-pass RAG systems lack. We take a step toward this goal with Self-Improving RAG, a framework that decomposes document QA into three specialized agents (Retrieval, Reasoning, and Judge) coordinated by an orchestrator with feedback-driven self-correction. When the Judge Agent scores an answer below a dynamic threshold, the system triggers retry with escalated strategies: broader retrieval, more careful prompting, and relaxed acceptance criteria. We evaluate on FinanceBench (SEC filing QA), where Self-Improving RAG achieves 86% oracle-guided accuracy (measuring agreement with gold answers) with a 36.4% Lazarus Rate, recovering nearly 4 in 10 initially incorrect answers through targeted retry. A key finding is that a fixed retrieval pipeline with judge-driven retry achieves strong results without dynamic routing, providing full interpretability. Every decision is logged with confidence scores, enabling the audit trails required for regulated financial applications.

17 pages, 2 figures. Accepted at the ICLR 2026 Workshop on Advances in Financial AI

Computation and Language (cs.CL)

arXiv:2608.26706 [cs.CL]

(or arXiv:2608.26706v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.26706 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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