Rule-State Inference (RSI) inverts the standard ML paradigm by treating known regulatory rules as priors and inferring the latent state of compliance and drift, rather than approximating rules from noisy data.
March 24, 2026
Original Paper
Rule-State Inference (RSI): A Bayesian Framework for Compliance Monitoring in Rule-Governed Domains
arXiv · 2603.21610
The Takeaway
In high-stakes domains like taxation or law where rules are authoritative, RSI allows models to absorb regulatory changes in O(1) time without retraining. This moves AI from 'guessing the rules' to 'probabilistic reasoning within fixed rule spaces'.
From the 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