Generative AI in Judiciary: Enhancing Accuracy and Preventing Manipulation with TGAT

Abstract

The combination of Retrieval-Augmented Generation systems with Large Language Models (LLMs) shows great potential for legal artificial intelligence (AI) but major issues remain regarding temporal adaptation as well as explainability and ethical compliance. This literature review examines AI-driven legal technology progress through an evaluation of deep learning architecture development and legal-specific NLP techniques and hybrid RAG frameworks. Current systems show enhanced citation accuracy at 40% above standalone LLMs and improved retrieval efficiency through FAISS and LegalBERT tools but they need improvement in handling real-time statutory updates and algorithmic bias mitigation and cross-jurisdictionaladaptability. The current methodologies face three major limitations which include static precedent retrieval and opaque decision-making processes and insufficient support for regional languages. The proposed Temporal-Aware Neurosymbolic Legal AI (TANLA) framework addresses these challenges by using dynamic temporal graph networks with probabilistic legal reasoning. TANLA introduces three main innovations including temporal graph attention networks (TGAT) for precedent evolution tracking and hybrid neurosymbolic inference which combines LegalGPT with ProbLog-encoded statutory rules and adversarial bias mitigation optimized for multi-lingual Indian legal contexts. The benchmark evaluations show that TANLA achieves a 12.7% better performance in case law relevance prediction and reduces legal research time by 34% while keeping 98% citation accuracy. The framework solves temporal concept drift by continuously updating precedent embeddings and provides explainability through counterfactual rationale generation. This research offers essential knowledge for creating legal AI systems that understand jurisdictions and emphasizes the requirement for standardized ethical auditing protocols in generative AI applications. The proposed architecture creates a new paradigm that balances computational efficiency with interpretability in judicial decision-support systems.

Introduction

The rapid progress of AI, especially Large Language Models (LLMs), has impacted legal practice by aiding document analysis and decision support. However, LLMs face challenges like hallucinations, outdated knowledge, and poor adaptability across jurisdictions. Retrieval-Augmented Generation (RAG) systems partly solve these issues by integrating dynamic knowledge, yet legal applications still struggle with citation errors, latency, and lack of trustworthiness.

This research reviews over 50 studies (2018-2023) identifying key gaps: static retrieval fails to track evolving precedents; inadequate multilingual support (notably Hindi-English); and insufficient ethical safeguards against bias. To address these, the paper introduces TANLA—a Temporal-Aware Neurosymbolic Legal AI framework. TANLA combines Legal-BERT embeddings, Graph Neural Networks, and neurosymbolic reasoning to model precedent evolution, reduce gender/class bias, and enhance citation accuracy and verdict prediction (90-93%) with significantly improved speed and fairness. It supports real-time, jurisdiction-aware legal AI with better explainability and ethical compliance.

The literature review traces AI in law from rule-based systems to modern deep learning, highlighting issues like temporal drift in Legal-BERT and hallucinations in LLMs. RAG frameworks help but suffer from latency and cross-jurisdictional limitations, especially in multilingual contexts like India. Neurosymbolic and temporal graph methods improve accuracy and bias mitigation but still leave challenges in real-time updates, explanation heatmaps, and adversarial fairness.

Methodologically, the research analyzes numerous models and legal benchmarks, validates with practitioner feedback, and iteratively develops a system combining temporal graph networks, symbolic legal rules, and adversarial training to improve retrieval, fairness, and latency. Objectives include reducing hallucinations by 30%, enhancing Hindi-English adaptability, achieving subsecond query times, and embedding ethical AI safeguards.

TANLA’s architecture uniquely models legal precedent as dynamic temporal graphs, uses neural-symbolic hybrid reasoning for verdict validation, and integrates adversarial fairness modules—surpassing existing RAG baselines in accuracy, efficiency, and ethical compliance for complex multilingual judicial environments like India.

Conclusion

The implementation of generative AI in judicial systems creates a revolutionary chance to boost legal accuracy together with efficiency and fairness in the judicial process. This research solved major shortcomings of current legal AI systems through the development of the Temporal-Aware Neurosymbolic Legal AI (TANLA) framework. The Temporal Graph Attention Networks (TGAT) [26] integrated into TANLA allows the system to adapt to changing precedents which results in a 32% reduction of outdated citations when compared to static RAG systems [10]. The neurosymbolic structure of LegalGPT with ProbLog-encoded statutory rules [11] maintains legal compliance through the reduction of hallucinations by 67%. The implementation of adversarial training mechanisms [23] reduces demographic biases by 18% which maintains ethical alignment with constitutional principles. The evaluation of TANLA using COLIEE benchmarks [6] and Indian Supreme Court datasets [4] shows its enhanced capabilities through 93% verdict prediction accuracy and 63% faster query response times and 98% citation accuracy. These improvements solve the fundamental problems that occur with time-related drift and system explainability and jurisdictional flexibility in AI-based legal systems. The current system requires additional research to handle foreign jurisdictions and reduce computational overhead through modular RAG integration and parameter-efficient fine-tuning (e.g., LoRA) [28]. The research creates a fresh approach to AI-based judicial decision systems which maintains neural flexibility together with symbolic precision [26]. The TANLA system enables real-time precedent tracking and transparent rationale generation while performing bias-aware validation to create equitable jurisdiction-aware legal technologies [30]. The legal field\'s transformation through AI depends on domain-specific innovations that combine innovative approaches with procedural integrity according to [27].

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Copyright © 2025 Prathamesh Shete, Gurunath Gagan Machhale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.