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VERA-8B addresses the critical grounding gap in financial AI by unifying supervised fine-tuning and GRPO to ensure audit risk assessments are strictly evidence-based rather than merely fluent. This approach introduces necessary mechanisms for abstention and uncertainty qualification, preventing unsupported claims in high-stakes pre-enforcement scenarios while bridging raw SEC filings into reviewer-ready reports.
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VERA-8B: Evidence-Grounded Audit Risk Reasoning from SEC Filings

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2608.28402v1 Announce Type: new Abstract: Across audit applications, judgments must be supported by reasonable evidence. However, standard financial language models prioritize fluency over evidence. They are built for general financial reasoning and may produce plausible but ambiguous answers, creating a grounding gap that makes them unsuitable for audit work. We address this gap with VERA-8B, a new end-to-end audit reasoning system that identifies audit risks before enforcement actions occur. Constructing such a model raises several challenges, as no prior machine learning work targets pre-enforcement audit prediction. To our knowledge, we are the first to unify SFT and GRPO for evidence-grounded audit reasoning under one evidence standard, achieving performance that surpasses all evaluated baselines. Because auditing cannot tolerate unsupported claims, we introduce abstention and uncertainty qualification to defer uncertain or evidence-incomplete cases. Finally, we design an AuditBridge to ground model reasoning for practical audit work. It transforms raw filings into verified records and then into reviewer-ready reports, bridging finance and computation with broad generality. Together, these components produce auditable, review-ready outputs suitable for practical audit work.

Abstract:Across audit applications, judgments must be supported by reasonable evidence. However, standard financial language models prioritize fluency over evidence. They are built for general financial reasoning and may produce plausible but ambiguous answers, creating a grounding gap that makes them unsuitable for audit work. We address this gap with VERA-8B, a new end-to-end audit reasoning system that identifies audit risks before enforcement actions occur. Constructing such a model raises several challenges, as no prior machine learning work targets pre-enforcement audit prediction. To our knowledge, we are the first to unify SFT and GRPO for evidence-grounded audit reasoning under one evidence standard, achieving performance that surpasses all evaluated baselines. Because auditing cannot tolerate unsupported claims, we introduce abstention and uncertainty qualification to defer uncertain or evidence-incomplete cases. Finally, we design an AuditBridge to ground model reasoning for practical audit work. It transforms raw filings into verified records and then into reviewer-ready reports, bridging finance and computation with broad generality. Together, these components produce auditable, review-ready outputs suitable for practical audit work.

Artificial Intelligence (cs.AI)

arXiv:2608.28402 [cs.AI]

(or arXiv:2608.28402v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.28402 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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