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

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-jurisdictional adaptability. 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.