Judicial Requirements for Generative AI in Legal Reasoning

Large Language Models (LLMs) are being integrated into professional domains, yet their limitations in high-stakes fields like law remain poorly understood. This paper defines the core capabilities that an AI system must possess to function as a reliable reasoning tool in judicial decision-making. Using the IRAC (Issue-Rule-Application-Conclusion) model as an analytical framework, the study focuses on the most challenging phases of legal adjudication: determining the applicable Rule (R) and performing the Application (A) of that rule to the facts of a case. From a judicial perspective, the analysis deconstructs legal reasoning into a series of core requirements, including the ability to select the correct legal framework across jurisdictions, generate sound arguments based on the doctrine of legal sources, distinguish ratio decidendi from obiter dictum in case law, resolve ambiguity arising from general clauses like "reasonableness", manage conflicting legal provisions, and correctly apply the burden of proof. The paper then maps various AI enhancement mechanisms, such as Retrieval-Augmented Generation (RAG), multi-agent systems, and neuro-symbolic AI, to these requirements, assessing their potential to bridge the gap between the probabilistic nature of LLMs and the rigorous, choice-driven demands of legal interpretation. The findings indicate that while these techniques can address specific challenges, significant challenges remain, particularly in tasks requiring discretion and transparent, justifiable reasoning. Our paper concludes that the most effective current role for AI in law is a dual one: as a high-volume assistant for simple, repetitive cases and as a sophisticated "sparring partner" for human experts in complex matters.