# RAGulating Compliance: A Multi-Agent Knowledge Graph for Regulatory QA

arXiv: https://arxiv.org/abs/2508.09893 · HTML: https://mlsanity.com/paper/2508.09893/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 12 of 12)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey](https://mlsanity.com/paper/2305.18703/) (2023)
- [Evaluation of OpenAI o1: Opportunities and Challenges of AGI](https://mlsanity.com/paper/2409.18486/) (2024)
- [Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases](https://mlsanity.com/paper/2403.10446/) (2024)
- [The Need for Guardrails with Large Language Models in Medical Safety-Critical Settings: An Artificial Intelligence Application in the Pharmacovigilance Ecosystem](https://mlsanity.com/paper/2407.18322/) (2024)
- [A Case Study for Compliance as Code with Graphs and Language Models: Public release of the Regulatory Knowledge Graph](https://mlsanity.com/paper/2302.01842/) (2023)
- [Advancing Risk and Quality Assurance: A RAG Chatbot for Improved Regulatory Compliance](https://mlsanity.com/paper/2507.16711/) (2025)
- [Agent-based environment for knowledge integration](https://mlsanity.com/paper/1303.6106/) (2013)

## Built on by (most cited first, 6 of 6)

- [LLM Agents in Law: Taxonomy, Applications, and Challenges](https://mlsanity.com/paper/2601.06216/) (2026)
- [Knowledge Graph Representations for LLM-Based Policy Compliance Reasoning](https://mlsanity.com/paper/2604.27713/) (2026)
- [AUDITFLOW: Executable Symbolic Environments for Structured Financial Reporting Verification](https://mlsanity.com/paper/2606.03031/) (2026)
- [Reverse Engineering Compliance: A Dual-Graph Verification Framework for Auditing Legacy IT Security Concepts](https://mlsanity.com/paper/2607.08292/) (2026)
- [IntelliAudit: Using Large Language Models to Evaluate Audit Controls](https://mlsanity.com/paper/2608.07688/) (2026)
- [Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152](https://mlsanity.com/paper/2608.16394/) (2026)

## Abstract (arXiv)

Regulatory compliance question answering (QA) requires precise, verifiable information, and domain-specific expertise, posing challenges for Large Language Models (LLMs). In this work, we present a novel multi-agent framework that integrates a Knowledge Graph (KG) of Regulatory triplets with Retrieval-Augmented Generation (RAG) to address these demands. First, agents build and maintain an ontology-free KG by extracting subject--predicate--object (SPO) triplets from regulatory documents and systematically cleaning, normalizing, deduplicating, and updating them. Second, these triplets are embedded and stored along with their corresponding textual sections and metadata in a single enriched vector database, allowing for both graph-based reasoning and efficient information retrieval. Third, an orchestrated agent pipeline leverages triplet-level retrieval for question answering, ensuring high semantic alignment between user queries and the factual "who-did-what-to-whom" core captured by the graph. Our hybrid system outperforms conventional methods in complex regulatory queries, ensuring factual correctness with embedded triplets, enabling traceability through a unified vector database, and enhancing understanding through subgraph visualization, providing a robust foundation for compliance-driven and broader audit-focused applications.