Which Changes Matter? Towards Trustworthy Legal AI via Relevance-Sensitive Evaluation and Solver-Grounded Reasoning
The paper discusses the importance of distinguishing legally relevant changes in legal AI systems. It introduces a new evaluation framework called LexGuard, which aims to enhance the reliability of legal reasoning by focusing on legally material changes. The authors argue that trustworthiness in legal AI requires both accuracy and calibrated sensitivity to relevant legal distinctions.
- ▪Legal AI must remain stable under irrelevant changes while being sensitive to legally material alterations.
- ▪The evaluation framework LexGuard formalizes statutes into executable constraints and uses adversarial agents for improved reasoning.
- ▪Experiments indicate that LexGuard reduces vulnerabilities and enhances consistency in legal reasoning.
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Computer Science > Artificial Intelligence arXiv:2605.26530 (cs) [Submitted on 26 May 2026] Title:Which Changes Matter? Towards Trustworthy Legal AI via Relevance-Sensitive Evaluation and Solver-Grounded Reasoning Authors:Chen Linze, Cai Yufan, Hou Zhe, Dong Jin Song View a PDF of the paper titled Which Changes Matter? Towards Trustworthy Legal AI via Relevance-Sensitive Evaluation and Solver-Grounded Reasoning, by Chen Linze and 2 other authors View PDF HTML (experimental) Abstract:Legal reasoning requires distinguishing changes that matter from those that do not. Legal AI should remain stable under legally irrelevant perturbations, but should change when perturbations alter legally material points.
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