Towards Expert Financial QA via Self-Improving RAG
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-Improvin...
| Authors | Junjie Xiong, Shawheen Ghezavat, Aum Hirpara |
|---|---|
| Primary category | cs.CL |
| Published | 27 August 2026 |
| Last updated at source | 23 September 2026 |
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