# GDPR Auto-Formalization with AI Agents and Human Verification

arXiv: https://arxiv.org/abs/2604.14607 · HTML: https://mlsanity.com/paper/2604.14607/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 8 of 8)

- [Language to Logical Form with Neural Attention](https://mlsanity.com/paper/1601.01280/) (2016)
- [Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision](https://mlsanity.com/paper/1611.00020/) (2016)
- [Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation](https://mlsanity.com/paper/2305.15541/) (2023)
- [Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law](https://mlsanity.com/paper/2502.17638/) (2025)
- [Catala: A Programming Language for the Law](https://mlsanity.com/paper/2103.03198/) (2021)
- [Enabling Reasoning with LegalRuleML](https://mlsanity.com/paper/1711.06128/) (2017)
- [Can Legislation Be Made Machine-Readable in PROLEG?](https://mlsanity.com/paper/2601.01477/) (2026)
- [PYTHEN: A Flexible Framework for Legal Reasoning in Python](https://mlsanity.com/paper/2603.15317/) (2026)

## Abstract (arXiv)

We study the overall process of automatic formalization of GDPR provisions using large language models, within a human-in-the-loop verification framework. Rather than aiming for full autonomy, we adopt a role-specialized workflow in which LLM-based AI components, operating in a multi-agent setting with iterative feedback, generate legal scenarios, formal rules, and atomic facts. This is coupled with independent verification modules which include human reviewers' assessment of representational, logical, and legal correctness. Using this approach, we construct a high-quality dataset to be used for GDPR auto-formalization, and analyze both successful and problematic cases. Our results show that structured verification and targeted human oversight are essential for reliable legal formalization, especially in the presence of legal nuance and context-sensitive reasoning.