Bridging Legal Interpretation and Formal Logic: Faithfulness, Assumption, and the Future of AI Legal Reasoning

2026-05-13

AI Summary

Current large language models often rely on extratextual assumptions in legal reasoning, leading to logically unfaithful and unverifiable conclusions that fail to meet the legal profession’s stringent demands for rigor and accountability. This work proposes a neuro-symbolic framework that integrates the expressive power of large language models with formal logical verification to ensure that all inferences remain strictly grounded in the original legal text, thereby eliminating unwarranted assumptions. The resulting approach yields a traceable and verifiable legal reasoning mechanism that substantially reduces hypothetical errors, alleviates the burden of manual review, and enhances system trustworthiness while preserving logical soundness and accountability.

Abstract

The growing adoption of large language models in legal practice brings both significant promise and serious risk. Legal professionals stand to benefit from AI that can reason over contracts, draft documents, and analyze sources at scale, yet the high-stakes nature of legal work demands a level of rigor that current AI systems do not provide. The central problem is not simply that LLMs hallucinate facts and references; it is that they systematically draw inferences that go beyond what the source text actually supports, presenting assumption-laden conclusions as if they were logically grounded. This proposal presents a neuro-symbolic approach to legal AI that combines the expressive power of large language models with the rigor of formal verification, aiming to make AI-assisted legal reasoning both capable and trustworthy, thus reducing the burden of manual verification without sacrificing the accountability that legal practice demands.

Problem

• legal reasoning
• large language models
• formal logic
• faithfulness
• assumption

Innovation

• neuro-symbolic
• legal reasoning
• formal verification
• large language models
• faithfulness