Multimodal Multi-Agent Empowered Legal Judgment Prediction
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arXiv
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| Format: | Preprint |
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2026
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| author | Kang, Zhaolu Gong, Junhao Chen, Qingxi Zhang, Hao Liu, Jiaxin Fu, Rong Feng, Zhiyuan Wang, Yuan Fong, Simon Zhou, Kaiyue |
| author_facet | Kang, Zhaolu Gong, Junhao Chen, Qingxi Zhang, Hao Liu, Jiaxin Fu, Rong Feng, Zhiyuan Wang, Yuan Fong, Simon Zhou, Kaiyue |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12815 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multimodal Multi-Agent Empowered Legal Judgment Prediction Kang, Zhaolu Gong, Junhao Chen, Qingxi Zhang, Hao Liu, Jiaxin Fu, Rong Feng, Zhiyuan Wang, Yuan Fong, Simon Zhou, Kaiyue Computation and Language Artificial Intelligence Computers and Society Multiagent Systems 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. |
| title | Multimodal Multi-Agent Empowered Legal Judgment Prediction |
| topic | Computation and Language Artificial Intelligence Computers and Society Multiagent Systems |
| url | https://arxiv.org/abs/2601.12815 |