AI 中文总结
针对韩国刑法中量刑预测与文件起草问题，提出神经符号框架，用大语言模型提取语义，可满足性模理论求解器计算法定罚款，减少幻觉风险，并纳入人工参与验证，对交通犯罪量刑指南形式化，支持简易起诉。
AI 中文摘要
韩国刑事司法系统利用简易程序来加快处理大量轻微违法行为，如简单的酒后驾车、无证驾驶和轻微交通伤亡事故。虽然该机制提高了司法效率，但处理这些案件给检察官带来了巨大行政负担，因此需要能将非结构化法律文本准确转化为确定性法定结果的自动化系统。近期大语言模型在语义提取方面表现出色，但概率性质限制了其在法律判决预测任务中的可靠性。我们提出一个神经符号框架，将非结构化法律事实与形式验证联系起来。架构将大语言模型限制在语义提取，把法定罚款计算交给可满足性模理论求解器，减少计算中的幻觉风险。还纳入人工参与验证方案以保留专业法律监督。我们在此流程中对2026年交通犯罪量刑指南进行形式化，展示了支持简易起诉的确定性方法。
英文摘要
The Korean criminal justice system utilizes summary proceedings (guyaksik) to expedite high-volume minor infractions, such as simple driving under the influence (DUI), unlicensed driving, and minor traffic casualties. Although this mechanism improves judicial throughput, processing these cases creates a substantial administrative burden for prosecutors, driving the need for automated systems that can precisely translate unstructured legal text into deterministic statutory outcomes. While recent Large Language Models (LLMs) excel at semantic extraction, their probabilistic nature inherently limits their reliability in Legal Judgment Prediction tasks. Specifically, when confronted with the arithmetic constraints of legal statutes, LLMs can produce hallucinations. Given that legal accountability permits virtually no tolerance for stochastic errors, purely neural architectures remain limited in their direct judicial applications. To address these limitations, we propose a Neuro-Symbolic framework that bridges unstructured legal facts with formal verification. Our architecture restricts the LLM exclusively to semantic extraction, while offloading statutory fine calculations to a Satisfiability Modulo Theories solver. This division of labor reduces hallucination risks during computation. Furthermore, we incorporate a Human-in-the-Loop verification scheme to preserve professional legal oversight. We formalize the 2026 Sentencing Guidelines for Traffic Offenses within this pipeline, demonstrating a deterministic approach to supporting summary indictments.
Comments17 pages, 4 figures
