Legal Judgment Prediction (LJP) aims to predict the outcomes of legal cases based on factual descriptions, serving as a fundamental task to advance the development of legal systems. Traditional methods often rely on statistical analyses or role-based simulations but face challenges with multiple allegations, diverse evidence, and lack adaptability. In this paper, we introduce JurisMMA, a novel framework for LJP that effectively decomposes trial tasks, standardizes processes, and organizes them into distinct stages. Furthermore, we build JurisMM, a large dataset with over 100,000 recent Chinese judicial records, including both text and multimodal video-text data, enabling comprehensive evaluation. Experiments on JurisMM and the benchmark LawBench validate our framework's effectiveness. These results indicate that our framework is effective not only for LJP but also for a broader range of legal applications, offering new perspectives for the development of future legal methods and datasets.

翻译：司法判决预测旨在基于事实描述预测法律案件结果，是推动法律体系发展的基础任务。传统方法通常依赖统计分析或基于角色的模拟，但在处理多重指控、多样化证据及缺乏适应性方面面临挑战。本文提出JurisMMA，一种用于司法判决预测的新型框架，能有效分解审判任务、标准化流程并将其组织为不同阶段。此外，我们构建了JurisMM数据集，包含超过10万份近期中国司法记录，涵盖文本与多模态视频-文本数据，支持全面评估。在JurisMM和基准数据集LawBench上的实验验证了本框架的有效性。结果表明，该框架不仅适用于司法判决预测，还可广泛应用于更广泛的法律应用场景，为未来法律方法与数据集的发展提供了新视角。