[2511.21033] Towards Trustworthy Legal AI through LLM Agents and Formal Reasoning

Computer Science > Artificial Intelligence arXiv:2511.21033 (cs) [Submitted on 26 Nov 2025 (v1), last revised 5 Mar 2026 (this version, v2)] Title:Towards Trustworthy Legal AI through LLM Agents and Formal Reasoning Authors:Linze Chen, Yufan Cai, Zhe Hou, Jin Song Dong View a PDF of the paper titled Towards Trustworthy Legal AI through LLM Agents and Formal Reasoning, by Linze Chen and 3 other authors View PDF HTML (experimental) Abstract:Legal decisions should be logical and based on statutory laws. While large language models(LLMs) are good at understanding legal text, they cannot provide verifiable justifications. We present L4L, a solver-centric framework that enforces formal alignment between LLM-based legal reasoning and statutory laws. The framework integrates role-differentiated LLM agents with SMT-backed verification, combining the flexibility of natural language with the rigor of symbolic reasoning. Our approach operates in four stages: (1) Statute Knowledge Building, where LLMs autoformalize legal provisions into logical constraints and validate them through case-level testing; (2) Dual Fact-and-Statute Extraction, in which the prosecutor-and defense-aligned agents independently map case narratives to argument tuples; (3) Solver-Centric Adjudication, where SMT solvers check the legal admissibility and consistency of the arguments against the formalized statute knowledge; (4) Judicial Rendering, in which a judge agent integrates solver-validated reasoning with st...