Authors : Shatrunjay Kumar Singh

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/m6trdj3w

Scribd : https://tinyurl.com/y8b4cnz6

DOI : https://doi.org/10.38124/ijisrt/25dec1556

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Retrieval-Augmented Generation (RAG) systems promise practical legal assistance by grounding Large Language Models (LLMs) in external authority. However, standard RAG optimizes semantic similarity and often fails to respect common-law constraints such as jurisdictional bindingness, court hierarchy, temporal validity, and negative treatment. We propose Precedent- Aware Multi-Agent RAG (PA-MA-RAG), an agentic architecture that decomposes legal research and writing into specialized agents for issue framing, authority planning, retrieval, precedent ranking, conflict resolution, drafting, and citation verification. Our method introduces an authority- constrained re-ranking objective that prioritizes controlling precedents while penalizing overruled or otherwise negatively treated cases. The verifier agent enforces evidence-grounded generation by requiring each legal proposition to be supported by retrieved holdings and quotations. We describe an evaluation protocol for both precedent retrieval and citation-grounded legal analysis generation, including authority correctness, supported-claim rate, and robustness to conflicting precedent.

Keywords : Precedent-Aware RAG, Multi-Agent Systems, Legal Information Retrieval, Stare Decisis, Authority Ranking, Citation Networks, CLERC, COLIEE.

References :

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