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This paper proposes a novel architectural layer, "policy-centroid routing," designed to resolve the semantic complexity of overlapping policy regimes before an AI system performs adjudication. By compressing natural language rules into representative centroids within a shared semantic space, the method aims to efficiently route only relevant constraints for authoritative review rather than attempting immediate compliance judgment. While currently theoretical with no empirical efficacy results reported, this approach addresses a critical bottleneck in deploying robust, legally compliant autonomous agents where multiple regulations intersect.
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Which Rules Matter Now? Policy-Centroid Routing Before an Intelligent System Acts

arXiv3 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2608.30757v1 Announce Type: new Abstract: Before an intelligent system can decide whether an action is allowed, it must first know which rules the action has approached. A single proposed action can implicate several policy regimes at once. Their requirements may stack, overlap, or qualify one another, yet many remain written in natural language while the action itself arrives as an incomplete description of intent. The first problem is not judgment. It is attention. Policy-centroid routing creates a layer before adjudication. It compresses expressions within each policy regime into one or more representative centroids, places the proposed action in the same semantic space, applies a declared measure, and routes every regime crossing a declared threshold to authoritative review. Several regimes may trigger at once. The output is a review agenda, not permission, prohibition, legality, breach, compliance, certification, or enforcement. The paper develops six falsifiable propositions and seven follow-on studies comparing the hypothesis with structured workflows, lexical and semantic retrieval, hierarchical and direct classification, and selective prediction under matched review burden. The studies are designed to identify where policy geometry recovers applicable regimes, where compression loses rare or overlapping obligations, and where the mechanism should abstain. The paper includes a synthetic worked example and reports no empirical efficacy result.

Title:Which Rules Matter Now? Policy-Centroid Routing Before an Intelligent System Acts

Abstract:Before an intelligent system can decide whether an action is allowed, it must first know which rules the action has approached. A single proposed action can implicate several policy regimes at once. Their requirements may stack, overlap, or qualify one another, yet many remain written in natural language while the action itself arrives as an incomplete description of intent. The first problem is not judgment. It is attention. Policy-centroid routing creates a layer before adjudication. It compresses expressions within each policy regime into one or more representative centroids, places the proposed action in the same semantic space, applies a declared measure, and routes every regime crossing a declared threshold to authoritative review. Several regimes may trigger at once. The output is a review agenda, not permission, prohibition, legality, breach, compliance, certification, or enforcement. The paper develops six falsifiable propositions and seven follow-on studies comparing the hypothesis with structured workflows, lexical and semantic retrieval, hierarchical and direct classification, and selective prediction under matched review burden. The studies are designed to identify where policy geometry recovers applicable regimes, where compression loses rare or overlapping obligations, and where the mechanism should abstain. The paper includes a synthetic worked example and reports no empirical efficacy result.

Theory and research-program paper; 9 pages, 1 figure, 2 tables. Includes a synthetic worked example; reports no empirical efficacy result

Artificial Intelligence (cs.AI); Computers and Society (cs.CY)

arXiv:2608.30757 [cs.AI]

(or arXiv:2608.30757v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.30757 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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