# Neuro-Symbolic Compliance: Integrating LLMs and SMT Solvers for Automated Financial Legal Analysis

arXiv: https://arxiv.org/abs/2601.06181 · HTML: https://mlsanity.com/paper/2601.06181/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [LawLLM: Law Large Language Model for the US Legal System](https://mlsanity.com/paper/2407.21065/) (2024)
- [Planning Anything with Rigor: General-Purpose Zero-Shot Planning with LLM-based Formalized Programming](https://mlsanity.com/paper/2410.12112/) (2024)
- [The Fusion of Large Language Models and Formal Methods for Trustworthy AI Agents: A Roadmap](https://mlsanity.com/paper/2412.06512/) (2024)
- [Building Intelligent Agents with Neuro-Symbolic Concepts](https://mlsanity.com/paper/2505.06191/) (2025)
- [Instantiation-based Formalization of Logical Reasoning Tasks using Language Models and Logical Solvers](https://mlsanity.com/paper/2501.16961/) (2025)
- [Neural-Symbolic Collaborative Distillation: Advancing Small Language Models for Complex Reasoning Tasks](https://mlsanity.com/paper/2409.13203/) (2024)
- [Can Language Models Pretend Solvers? Logic Code Simulation with LLMs](https://mlsanity.com/paper/2403.16097/) (2024)
- [Early Verification of Legal Compliance via Bounded Satisfiability Checking](https://mlsanity.com/paper/2209.04052/) (2022)

## Built on by (most cited first, 3 of 3)

- [Know Your Limits : On the Faithfulness of LLMs as Solvers and Autoformalizers in Legal Reasoning](https://mlsanity.com/paper/2606.16118/) (2026)
- [Reverse Engineering Compliance: A Dual-Graph Verification Framework for Auditing Legacy IT Security Concepts](https://mlsanity.com/paper/2607.08292/) (2026)
- [Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog](https://mlsanity.com/paper/2607.21412/) (2026)

## Abstract (arXiv)

Financial regulations are increasingly complex, hindering automated compliance-especially the maintenance of logical consistency with minimal human oversight. We introduce a Neuro-Symbolic Compliance Framework that integrates Large Language Models (LLMs) with Satisfiability Modulo Theories (SMT) solvers to enable formal verifiability and optimization-based compliance correction. The LLM interprets statutes and enforcement cases to generate SMT constraints, while the solver enforces consistency and computes the minimal factual modification required to restore legality when penalties arise. Unlike transparency-oriented methods, our approach emphasizes logic-driven optimization, delivering verifiable, legally consistent reasoning rather than post-hoc explanation. Evaluated on 87 enforcement cases from Taiwan's Financial Supervisory Commission (FSC), the system attains 86.2% correctness in SMT code generation, improves reasoning efficiency by over 100x, and consistently corrects violations-establishing a preliminary foundation for optimization-based compliance applications.