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

arXiv cs.CL / 4/13/2026

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Key Points

The paper introduces NyayaMind, an open-source framework for court judgment prediction that also produces structured, legally grounded explanations aligned with judicial practice in India.
NyayaMind uses a RAG-based Retrieval Module to fetch relevant statutes and precedent cases from large legal corpora, and a Prediction Module that relies on reasoning-oriented, Indian legal domain fine-tuned LLMs to generate issues, arguments, rationale, and decisions.
The framework integrates retrieval, reasoning, and verification mechanisms to emulate the structured decision-making process courts typically follow.
Reported extensive results and expert evaluation indicate NyayaMind improves explanation quality and evidence alignment compared with existing CJPE approaches.
The work positions the system as a step toward trustworthy AI-assisted legal research and judicial decision support through transparency and scalable reasoning.

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.