Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems
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. Combining principles from formal methods with SoTA machine learning, we propose techniques that enable AI-enabled product and service developers, as well as third party AI developers and evaluators, to perform offline auditing and online (runtime) monitoring of product-specific (temporally
Record details
Published: 15 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Transparency
Retrieved: 14 July 2026
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ethics.ai (15 May 2026), “Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems,” evidence record 4259, https://ethics.ai/record/4259 (originally published by arXiv).
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