Precedent-Aware Multi-Agent Retrieval-Augmented Generation in Case Law Analysis
Shatrunjay Kumar Singh ()
International Journal of Innovative Science and Research Technology (IJISRT), 2025, vol. 10, issue 12, 2546-2550
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. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:cvr:ijisrt:2025:12:ijisrt25dec1556
DOI: 10.38124/ijisrt/25dec1556
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