NyayaMind- A Framework for Transparent Legal Reasoning and Judgment Prediction in the Indian Legal System

2026-04-10

AI Summary

This work proposes NyayaMind, a novel framework that addresses the longstanding challenge of achieving both high prediction accuracy and transparent, legally sound reasoning in judicial decision prediction. Designed specifically for the Indian legal context, NyayaMind uniquely integrates retrieval-augmented generation (RAG) with a domain-finetuned large language model to emulate the structured reasoning process of courts. The system retrieves relevant statutes and precedents via RAG and leverages a legally aligned fine-tuned model to generate verdict explanations that are both evidence-grounded and logically structured. Expert evaluations demonstrate that NyayaMind significantly outperforms existing approaches, achieving notable advances in explanation quality and evidentiary consistency, thereby offering a promising pathway toward trustworthy AI-assisted legal decision support.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Court Judgment Prediction and Explanation (CJPE) aims to predict a judicial decision and provide a legally grounded explanation for a given case based on the facts, legal issues, arguments, cited statutes, and relevant precedents. For such systems to be practically useful in judicial or legal research settings, they must not only achieve high predictive performance but also generate transparent and structured legal reasoning that aligns with established judicial practices. In this work, we present NyayaMind, an open-source framework designed to enable transparent and scalable legal reasoning for the Indian judiciary. The proposed framework integrates retrieval, reasoning, and verification mechanisms to emulate the structured decision-making process typically followed in courts. Specifically, NyayaMind consists of two main components: a Retrieval Module and a Prediction Module. The Retrieval Module employs a RAG pipeline to identify legally relevant statutes and precedent cases from large-scale legal corpora, while the Prediction Module utilizes reasoning-oriented LLMs fine-tuned for the Indian legal domain to generate structured outputs including issues, arguments, rationale, and the final decision. Our extensive results and expert evaluation demonstrate that NyayaMind significantly improves the quality of explanation and evidence alignment compared to existing CJPE approaches, providing a promising step toward trustworthy AI-assisted legal decision support systems.

Problem

• Court Judgment Prediction
• Legal Reasoning
• Explanation
• Indian Legal System
• Transparent AI

Innovation

• Legal Reasoning
• Judgment Prediction
• Retrieval-Augmented Generation (RAG)
• Reasoning-Oriented LLMs
• Transparent AI