AUDITFLOW: Executable Symbolic Environments for Structured Financial Reporting Verification
Quick Answer
AuditFlow introduces a multi-agent framework for structured financial reporting verification, achieving 82.09% accuracy with GPT-5.5, outperforming the baseline by 14.93 points.
Quick Take
It utilizes a symbolic environment for effective audit processes, demonstrating the necessity of deterministic checks for reliable verification.
Key Points
- AuditFlow separates adaptive search from deterministic verification for improved accuracy.
- The framework uses US-GAAP taxonomy and XBRL filing graphs for structured audits.
- Two junior auditors and a senior auditor collaborate to resolve discrepancies.
- Final reports include an audit verdict, expected value, evidence trail, and trustworthiness score.
- Removing deterministic checks reduces accuracy to 17.91%, highlighting their importance.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Symbolic Computation (cs.SC) |
| Cite as: | arXiv:2606.03031 [cs.AI] |
| (or arXiv:2606.03031v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.03031 arXiv-issued DOI via DataCite |
Submission history
From: Yan Wang [view email]
[v1]
Tue, 2 Jun 2026 02:14:42 UTC (9,452 KB)
— Originally published at arxiv.org
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