mlsanityData to 7 Oct 2026

Multimodal Multi-Agent Empowered Legal Judgment Prediction

arXiv 2601.12815 ↗First posted 19 Jan 2026cs.CL cs.AI cs.CYcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 19 of its 27 references that are in the map. First posted on arXiv 19 Jan 2026.

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19 of 27 references.

References · 19

  1. 2025Qwen2.5-VL Technical Reportcited by 5,418
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  3. 2024GPT-4o System Cardcited by 4,467
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  3. 2025LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networkscited by 22
  4. 2025PDTrim: Targeted Pruning for Prefill-Decode Disaggregation in Inferencecited by 14
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  7. 2025JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learningcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 14.

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