发表机构
New York University（纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究推出端到端审计推理系统VERA-8B，统一SFT与GRPO实现证据支撑审计推理，引入弃权机制，设计AuditBridge实现原始文件到可用报告的转换，性能优于基线模型。
AI 中文摘要
在各类审计应用中，判断必须有合理证据支撑，但标准金融语言模型优先考虑流畅性而非证据，它们针对通用金融推理构建，可能生成看似合理却模糊的答案，形成的证据支撑差距使其不适合审计工作。我们针对该差距推出VERA-8B，这是一款全新的端到端审计推理系统，可在执行处罚前识别审计风险。构建此类模型面临诸多挑战，因为此前无机器学习工作聚焦于执行前审计预测。据我们所知，我们是首个在同一证据标准下将SFT（监督微调）与GRPO（组相对策略优化）统一用于证据支撑审计推理的团队，取得的性能超越所有评估的基线模型。由于审计无法容忍无支撑的主张，我们引入弃权（不执行）与不确定性限定机制，以推迟处理不确定或证据不完整的案例。最后，我们设计了AuditBridge（审计桥），为实际审计工作提供模型推理的证据支撑，它将原始SEC文件转换为经验证的记录，再转换为可供审核人员使用的报告，以广泛的通用性搭建起金融与计算之间的桥梁。这些组件共同产出可审计、可供审核使用的输出，适用于实际审计工作。
英文摘要
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.
