Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems
The article discusses the integration of formal methods with large language models (LLMs) for auditing and monitoring AI systems. It presents techniques for ensuring compliance with behavioral constraints throughout the AI development lifecycle. The proposed methods demonstrate improved detection of violations compared to traditional approaches.
- ▪The study focuses on monitoring and auditing AI-enabled products from pre-deployment to post-deployment.
- ▪Techniques are proposed for offline auditing and online monitoring of behavioral constraints in LLMs.
- ▪Experimental results indicate that the new methods outperform baseline LLM approaches in detecting violations.
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Computer Science > Artificial Intelligence arXiv:2605.16198 (cs) [Submitted on 15 May 2026] Title:Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems Authors:Parand A. Alamdari, Toryn Q. Klassen, Sheila A. McIlraith View a PDF of the paper titled Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems, by Parand A. Alamdari and 2 other authors View PDF HTML (experimental) Abstract:We examine one particular dimension of AI governance: how to monitor and audit AI-enabled products and services throughout the AI development lifecycle, from pre-deployment testing to post-deployment auditing.
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