Towards Expert Financial QA via Self-Improving RAG

• URL: http://arxiv.org/abs/2608.26706v1
• Date: Thu, 27 Aug 2026 07:01:41 GMT
• Title: Towards Expert Financial QA via Self-Improving RAG
• Abstract summary: Self-Improving RAG is a framework that decomposes document QA into three specialized agents.<n>Retrieval, Reasoning, and Judge are coordinated by an orchestrator with feedback-driven self-correction.<n>We evaluate on FinanceBench (SEC filing QA) where Self-Improving RAG achieves 86% oracle-guided accuracy with a 36.4% Lazarus Rate.
• Score: 2.1042118213583394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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