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（Legal Judgment Prediction, LJP）旨在基于事实描述预测法律案件的结果，是推动法律系统发展的基础任务。传统方法通常依赖于统计分析或基于角色的模拟，但在处理多重指控、多样化证据以及缺乏适应性方面面临挑战。本文提出了JurisMMA，一种用于LJP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的阶段。此外，我们构建了JurisMM，一个包含超过10万份近期中国司法记录的大型数据集，涵盖文本和多模态视频-文本数据，支持全面评估。在JurisMM和基准数据集LawBench上的实验验证了我们框架的有效性。这些结果表明，我们的框架不仅对LJP有效，也适用于更广泛的法律应用，为未来法律方法和数据集的发展提供了新的视角。