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

TL;DR

NyayaMind is an open-source framework that enhances transparent legal reasoning and judgment prediction in the Indian legal system by integrating retrieval, reasoning, and verification modules.

Contribution

It introduces a scalable, transparent framework combining retrieval and reasoning modules for improved legal decision prediction in India.

Findings

Significantly improves explanation quality over existing methods

Effectively retrieves relevant statutes and precedents

Generates structured legal reasoning outputs

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.