[2603.21610] Rule-State Inference (RSI): A Bayesian Framework for Compliance Monitoring in Rule-Governed Domains

Computer Science > Machine Learning arXiv:2603.21610 (cs) [Submitted on 23 Mar 2026] Title:Rule-State Inference (RSI): A Bayesian Framework for Compliance Monitoring in Rule-Governed Domains Authors:Abdou-Raouf Atarmla View a PDF of the paper titled Rule-State Inference (RSI): A Bayesian Framework for Compliance Monitoring in Rule-Governed Domains, by Abdou-Raouf Atarmla View PDF HTML (experimental) Abstract:Existing machine learning frameworks for compliance monitoring -- Markov Logic Networks, Probabilistic Soft Logic, supervised models -- share a fundamental paradigm: they treat observed data as ground truth and attempt to approximate rules from it. This assumption breaks down in rule-governed domains such as taxation or regulatory compliance, where authoritative rules are known a priori and the true challenge is to infer the latent state of rule activation, compliance, and parametric drift from partial and noisy observations. We propose Rule-State Inference (RSI), a Bayesian framework that inverts this paradigm by encoding regulatory rules as structured priors and casting compliance monitoring as posterior inference over a latent rule-state space S = {(a_i, c_i, delta_i)}, where a_i captures rule activation, c_i models the compliance rate, and delta_i quantifies parametric drift. We prove three theoretical guarantees: (T1) RSI absorbs regulatory changes in O(1) time via a prior ratio correction, independently of dataset size; (T2) the posterior is Bernstein-von Mises c...