A Knowledge-Augmented Multi-Agent AI System for Context-Aware Legal Reasoning and Case Outcome Prediction
Year: 2026
Type: preprint
Abstract: Recent advancements in Legal Artificial Intelligence (LegalAI) have focused on automated legal case analysis and judgment prediction using Transformer-based language models, legal knowledge retrieval, structured case information and multimodal representations. Recent studies have also explored retrieval-augmented reasoning and multi-agent approaches to integrate case facts, applicable laws, similar cases, and contextual legal evidence. However, existing approaches often treat legal text, structured case information, external legal knowledge, and collaborative reasoning as separate components, limiting comprehensive context-aware judgment prediction. This work develops a knowledge-augmented multi-agent AI framework that integrates contextu-al legal text, structured case information, legal knowledge retrieval, and coordinated multi-agent reasoning to predict legal judgment outcomes as Approved or Rejected. The study uses a Legal Judgment Prediction Dataset containing 1,522 cases, including 1,000 Approved and 522 Rejected cases. The framework integrates Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa), Multimodal Feature Fusion (MFF), embedding-based retrieval with FAISS, and a Heterogeneous-Agent Proximal Policy Optimization (HAPPO)-based multi-agent system comprising Legal Retrieval, Similar Case, Case Analysis and Legal Reasoning Agents. Knowledge augmentation and context-aware reasoning are followed by Feature Tokenizer Transformer (FT-Transformer)-based outcome prediction. Experimental evaluation achieved 0.9876 accuracy, 0.9842 precision, 0.9918 recall, 0.9880 F1-score, and 0.9999 ROC-AUC, while re-trieval achieved 0.5843 Precision@5, 0.6371 Recall@5, and 0.7401 Mean Reciprocal Rank (MRR). Ablation analysis showed reduced performance after removing text embedding, multimodal fu-sion, knowledge retrieval, or HAPPO-based reasoning. Overall, the framework demonstrates an effective approach for context-aware legal judgment prediction and provides a foundation for validation on larger and more diverse legal datasets.
Source: Preprints.org
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7223046037