Navigating Global AI Regulation: A Multi-Jurisdictional Retrieval-Augmented Generation System
On the map: Retrieval-augmented generation → Legal and financial RAG
Abstract (arXiv)
Navigating AI regulation across jurisdictions is increasingly difficult for policymakers, legal professionals, and researchers. To address this, we present a multi-jurisdictional Retrieval-Augmented Generation system for global AI regulation. Our corpus includes 242 documents across 68 jurisdictions, ranging from formal legislation like the EU AI Act to unstructured policy documents such as national AI strategies. The system makes three technical contributions: type-specific chunking that preserve legal structure across heterogenous documents; conditional retrieval routing with entity detection and metadata for legal citations; and priority-based re-ranking to boost enacted legislation over policy and secondary sources. Evaluation of 50 queries reveals strong performance across both single-entity and multi-jurisdictional questions, achieving 0.87 average faithfulness and 0.84 average answer relevancy. Single-entity queries achieve 0.86 average faithfulness and 0.92 average answer relevancy, while multi-jurisdictional comparison queries achieve 0.88 average faithfulness and 0.75 average answer relevancy. These findings highlight the effectiveness of domain-specific retrieval strategies for navigating complex, heterogenous regulatory corpora.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation. Builds on 9 of its 41 references that are in the map. First posted on arXiv 28 Apr 2026.
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9 of 41 references.
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- 2025Towards Reliable Retrieval in RAG Systems for Large Legal Datasets8 Oct
- 2025All for law and law for all: Adaptive RAG Pipeline for Legal Research18 Aug
- 2025LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation28 Feb
- 2024HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications29 Aug
- 2024LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain19 Aug
Other references · 4
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2021Ethical and social risks of harm from Language Modelscited by 957
- 2025Comparing Apples to Oranges: A Taxonomy for Navigating the Global Landscape of AI Regulationcited by 7
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- 2026Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
- 2026From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance
- 2026LexPath: A domain-oriented multi-path framework for legal article retrieval ↗
- 2025ASVRI-Legal: Fine-Tuning LLMs with Retrieval Augmented Generation for Enhanced Legal Regulation
- 2025Let's have a chat with the EU AI Act
- 2025Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization
- 2025A Hybrid Approach to Information Retrieval and Answer Generation for Regulatory Texts
- 2026Efficient LLM Distillation for Bangladesh Legal Context: A Smartphone-Compatible Retrieval-Augmented Generation Model