Structured financial audit verification is difficult for language-model agents because correctness depends on structured evidence rather than text alone. A model must link reported facts to taxonomy concepts, traverse calculation or dimensional relations, and recompute expected values before applying an audit rule. We propose AuditFlow, a graph-grounded multi-agent framework that separates adaptive search from deterministic verification. AuditFlow builds a symbolic environment from a static US-GAAP taxonomy graph and a dynamic XBRL filing graph, and exposes it through typed tools for fact retrieval, taxonomy traversal, numerical checking, and rule evaluation. Two junior auditors inspect each case from regulatory and evidentiary views, while a senior auditor resolves disagreements and can request further investigation. The final reports are fused through evidential aggregation to produce an audit verdict, expected value, evidence trail, and trustworthiness score. On a FinAuditing-derived FinMR sample, AuditFlow reaches 82.09% joint audit accuracy under GPT-5.5, outperforming the strongest baseline by 14.93 points. Removing deterministic checks drops accuracy to 17.91%, showing that the symbolic environment performs the verification step that the model cannot reliably replace.

翻译：摘要：结构化财务审计验证对语言模型代理而言具有挑战性，因为正确性依赖于结构化证据而非文本本身。模型必须将报告的事实映射到分类概念，遍历计算或维度关系，并在应用审计规则前重新计算期望值。我们提出AuditFlow，一种基于图的多智能体框架，将自适应搜索与确定性验证分离。AuditFlow从静态的US-GAAP分类图和动态的XBRL申报图中构建符号环境，并通过类型化工具提供事实检索、分类遍历、数值校验和规则评估功能。两名初级审计员分别从监管和证据角度审查每个案例，而一名高级审计员负责解决分歧并可要求进一步调查。最终报告通过证据聚合形成审计结论、期望值、证据链和可信度评分。在基于FinAuditing的FinMR样本上，AuditFlow在GPT-5.5下达到82.09%的联合审计准确率，超过最强基线14.93个百分点。移除确定性校验后准确率下降至17.91%，表明符号环境执行了模型无法可靠替代的验证步骤。