Multimodal Multi-Agent Empowered Legal Judgment Prediction

• URL: http://arxiv.org/abs/2601.12815v4
• Date: Tue, 27 Jan 2026 06:04:33 GMT
• 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,
• Abstract summary: Legal Judgment Prediction aims to predict the outcomes of legal cases based on factual descriptions.<n>We introduce JurisMMA, a novel framework for LJP that effectively decomposes trial tasks, standardizes processes, and organizes them into distinct stages.<n> Experiments on JurisMM and the benchmark LawBench validate our framework's effectiveness.
• Score: 13.220449013773349
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 broader range of legal applications, offering new perspectives for the development of future legal methods and datasets.

Related papers

• LegalOne: A Family of Foundation Models for Reliable Legal Reasoning [54.57434222018289]
  We present LegalOne, a family of foundational models specifically tailored for the Chinese legal domain.<n>LegalOne is developed through a comprehensive three-phase pipeline designed to master legal reasoning.<n>We publicly release the LegalOne weights and the LegalKit evaluation framework to advance the field of Legal AI.
  arXiv  Detail & Related papers  (2026-01-31T10:18:32Z)
• PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice [67.71760070255425]
  We introduce PLawBench, a practical benchmark for evaluating large language models (LLMs) in legal practice scenarios.<n>PLawBench comprises 850 questions across 13 practical legal scenarios, with each question accompanied by expert-designed evaluation rubrics.<n>Using an LLM-based evaluator aligned with human expert judgments, we evaluate 10 state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2026-01-23T11:36:10Z)
• ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India [10.522785783474857]
  We introduce Reinforcement Learning-based Legal Reasoning (ReGal), a framework that integrates Multi-Task Instruction Tuning with Reinforcement Learning from AI Feedback.<n>Our approach is evaluated across two critical legal tasks: (i) Court Judgment Prediction and Explanation (CJPE), and (ii) Legal Document Summarization.
  arXiv  Detail & Related papers  (2025-12-19T19:13:41Z)
• GLARE: Agentic Reasoning for Legal Judgment Prediction [60.13483016810707]
  Legal judgment prediction (LJP) has become increasingly important in the legal field.<n>Existing large language models (LLMs) have significant problems of insufficient reasoning due to a lack of legal knowledge.<n>We introduce GLARE, an agentic legal reasoning framework that dynamically acquires key legal knowledge by invoking different modules.
  arXiv  Detail & Related papers  (2025-08-22T13:38:12Z)
• ASP2LJ : An Adversarial Self-Play Laywer Augmented Legal Judgment Framework [21.003203706712643]
  Legal Judgment Prediction (LJP) aims to predict judicial outcomes, including relevant legal charge, terms, and fines.<n>Current datasets, derived from authentic cases, suffer from high human annotation costs and imbalanced distributions.<n>We propose an Adversarial Self-Play Lawyer Augmented Legal Judgment Framework, called ASP2LJ.<n>Our framework enables a judge to reference evolved lawyers' arguments, improving the objectivity, fairness, and rationality of judicial decisions.
  arXiv  Detail & Related papers  (2025-06-11T06:55:40Z)
• AnnoCaseLaw: A Richly-Annotated Dataset For Benchmarking Explainable Legal Judgment Prediction [56.797874973414636]
  AnnoCaseLaw is a first-of-its-kind dataset of 471 meticulously annotated U.S. Appeals Court negligence cases.<n>Our dataset lays the groundwork for more human-aligned, explainable Legal Judgment Prediction models.<n>Results demonstrate that LJP remains a formidable task, with application of legal precedent proving particularly difficult.
  arXiv  Detail & Related papers  (2025-02-28T19:14:48Z)
• Legal Evalutions and Challenges of Large Language Models [42.51294752406578]
  We use the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions. We compare current state-of-the-art LLMs, including open-source, closed-source, and legal-specific models trained specifically for the legal domain.
  arXiv  Detail & Related papers  (2024-11-15T12:23:12Z)
• LawLLM: Law Large Language Model for the US Legal System [43.13850456765944]
  We introduce the Law Large Language Model (LawLLM), a multi-task model specifically designed for the US legal domain. LawLLM excels at Similar Case Retrieval (SCR), Precedent Case Recommendation (PCR), and Legal Judgment Prediction (LJP) We propose customized data preprocessing techniques for each task that transform raw legal data into a trainable format.
  arXiv  Detail & Related papers  (2024-07-27T21:51:30Z)
• InternLM-Law: An Open Source Chinese Legal Large Language Model [72.2589401309848]
  InternLM-Law is a specialized LLM tailored for addressing diverse legal queries related to Chinese laws. We meticulously construct a dataset in the Chinese legal domain, encompassing over 1 million queries. InternLM-Law achieves the highest average performance on LawBench, outperforming state-of-the-art models, including GPT-4, on 13 out of 20 subtasks.
  arXiv  Detail & Related papers  (2024-06-21T06:19:03Z)
• Low-Resource Court Judgment Summarization for Common Law Systems [32.13166048504629]
  We present CLSum, the first dataset for summarizing multi-jurisdictional common law court judgment documents. This is the first court judgment summarization work adopting large language models (LLMs) in data augmentation, summary generation, and evaluation.
  arXiv  Detail & Related papers  (2024-03-07T12:47:42Z)
• Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning [49.23103067844278]
  We propose the task of multi-defendant LJP, which aims to automatically predict the judgment results for each defendant of multi-defendant cases. Two challenges arise with the task of multi-defendant LJP: (1) indistinguishable judgment results among various defendants; and (2) the lack of a real-world dataset for training and evaluation.
  arXiv  Detail & Related papers  (2023-12-10T04:46:30Z)
• Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration [52.57055162778548]
  Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI. Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems. Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task.
  arXiv  Detail & Related papers  (2023-10-13T16:47:20Z)
• Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction [46.71918729837462]
  Given the fact description text of a legal case, legal judgment prediction aims to predict the case's charge, law article and penalty term. Previous studies fail to distinguish different classification errors with a standard cross-entropy classification loss. We propose a moco-based supervised contrastive learning to learn distinguishable representations. We further enhance the representation of the fact description with extracted crime amounts which are encoded by a pre-trained numeracy model.
  arXiv  Detail & Related papers  (2022-11-15T15:53:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.