AI RESEARCH
De Jure: Iterative LLM Self-Refinement for Structured Extraction of Regulatory Rules
arXiv CS.LG
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ArXi:2604.02276v1 Announce Type: cross Regulatory documents encode legally binding obligations that LLM-based systems must respect. Yet converting dense, hierarchically structured legal text into machine-readable rules remains a costly, expert-intensive process. We present De Jure, a fully automated, domain-agnostic pipeline for extracting structured regulatory rules from raw documents, requiring no human annotation, domain-specific prompting, or annotated gold data.