![[2601.12815] Multimodal Multi-Agent Empowered Legal Judgment Prediction](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2601.12815] Multimodal Multi-Agent Empowered Legal Judgment Prediction
Summary
This paper presents JurisMMA, a novel framework for Legal Judgment Prediction (LJP) that utilizes multimodal data to enhance the accuracy of predicting legal case outcomes.
Why It Matters
Legal Judgment Prediction is crucial for improving legal systems and processes. By introducing a framework that integrates multimodal data and a comprehensive dataset, this research addresses existing challenges in LJP, potentially transforming how legal outcomes are assessed and predicted.
Key Takeaways
- JurisMMA framework effectively decomposes legal trial tasks.
- Introduces a large dataset with over 100,000 Chinese judicial records.
- Demonstrates improved prediction accuracy for legal outcomes.
- Framework applicable to a broader range of legal applications.
- Offers new perspectives for future legal methods and datasets.
Computer Science > Computation and Language arXiv:2601.12815 (cs) [Submitted on 19 Jan 2026 (v1), last revised 19 Feb 2026 (this version, v5)] Title:Multimodal Multi-Agent Empowered Legal Judgment Prediction Authors:Zhaolu Kang, Junhao Gong, Qingxi Chen, Hao Zhang, Jiaxin Liu, Rong Fu, Zhiyuan Feng, Yuan Wang, Simon Fong, Kaiyue Zhou View a PDF of the paper titled Multimodal Multi-Agent Empowered Legal Judgment Prediction, by Zhaolu Kang and Junhao Gong and Qingxi Chen and Hao Zhang and Jiaxin Liu and Rong Fu and Zhiyuan Feng and Yuan Wang and Simon Fong and Kaiyue Zhou View PDF HTML (experimental) Abstract: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...
