# GraphCompliance: Structure-Aware Regulatory Compliance through Policy-Context Graph Alignment

arXiv: https://arxiv.org/abs/2510.26309 · HTML: https://mlsanity.com/paper/2510.26309/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
- [A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts](https://mlsanity.com/paper/2402.09727/) (2024)
- [SaulLM-7B: A pioneering Large Language Model for Law](https://mlsanity.com/paper/2403.03883/) (2024)
- [Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering](https://mlsanity.com/paper/2404.17723/) (2024)
- [Knowledge Graph-Guided Retrieval Augmented Generation](https://mlsanity.com/paper/2502.06864/) (2025)
- [Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning](https://mlsanity.com/paper/2410.14211/) (2024)
- [Enhancing Legal Compliance and Regulation Analysis with Large Language Models](https://mlsanity.com/paper/2404.17522/) (2024)
- [Interactive GDPR-Compliant Privacy Policy Generation for Software Applications](https://mlsanity.com/paper/2410.03069/) (2024)

## Built on by (most cited first, 9 of 9)

- [Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions](https://mlsanity.com/paper/2602.22680/) (2026)
- [Knowledge Graph Representations for LLM-Based Policy Compliance Reasoning](https://mlsanity.com/paper/2604.27713/) (2026)
- [RegDivergence-101: An LLM Benchmark for Cross-Jurisdiction Regulatory Contradiction Detection in Life Sciences](https://mlsanity.com/paper/2608.28607/) (2026)
- [LLM+Graph@VLDB'2025 Workshop Summary](https://mlsanity.com/paper/2604.02861/) (2026)
- [REGREACT: Self-Correcting Multi-Agent Pipelines for Structured Regulatory Information Extraction](https://mlsanity.com/paper/2604.12054/) (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)
- [Verication-driven closed-loop multi-agent large language modelframework for code-compliant structural design](https://mlsanity.com/paper/2608.07978/) (2026)
- [Verdict Without the Rule: Diagnosing and Auditing Regulatory Rule Sensitivity in LLM Compliance Systems](https://mlsanity.com/paper/2610.12313/) (2026)

## Abstract (arXiv)

Regulatory compliance is an important requirement in many industrial and administrative settings. Yet compliance assessment remains difficult because regulatory requirements are distributed across structured documents and may depend on hierarchy, definitions, conditions, and cross-references, while the evidence relevant to a particular requirement may be embedded among multiple interacting actors, events, objects, and conditions in the assessed context. We present GraphCompliance, a structure-aware framework that represents regulatory documents as a schema-guided Policy Graph and contextual information as a Context Graph of entity-relation triples. GraphCompliance aligns the two graphs through a Compliance Gate, which identifies relevant regulatory requirements and evaluates them against the corresponding contextual evidence. This design makes regulatory structure explicit while organizing contextual facts into a comparable form, helping preserve structural dependencies and reduce terminology mismatch across different regulatory settings. Evaluation on EU2UK, GDPR-13, and CONTRACT yields Matthews correlation coefficients (MCC) of 0.802, 0.682, and 0.793, respectively. These values are numerically higher than those of the strongest compared methods on all three datasets. Ablation results further show that the key components of GraphCompliance contribute to its overall performance. These results support the utility of structured policy-context alignment for compliance-related prediction across heterogeneous regulatory settings. The source code of GraphCompliance is available at https://github.com/CHUNG-JiSeong/GraphCompliance.git.