From Norms to Indicators (N2I-RAG): An Agentic Retrieval-Augmented Generation Framework for Legal Indicator Computation
The paper introduces N2I-RAG, a framework aimed at automating the computation of legal indicators from normative texts. It addresses challenges in legal language interpretation and document quality by integrating adaptive retrieval and validation mechanisms. The evaluation shows that N2I-RAG outperforms existing systems and provides a foundation for transparent legal observatories.
- ▪N2I-RAG is designed to automate the computation of legal indicators in a transparent and traceable manner.
- ▪The framework integrates adaptive retrieval, LLM-based agents, and validation mechanisms in a modular pipeline.
- ▪Comparative experiments demonstrate that N2I-RAG consistently outperforms baseline systems.
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Computer Science > Artificial Intelligence arXiv:2605.26926 (cs) [Submitted on 26 May 2026] Title:From Norms to Indicators (N2I-RAG): An Agentic Retrieval-Augmented Generation Framework for Legal Indicator Computation Authors:Youssef Al Mouatamid, Marie Bonnin, Jihad Zahir View a PDF of the paper titled From Norms to Indicators (N2I-RAG): An Agentic Retrieval-Augmented Generation Framework for Legal Indicator Computation, by Youssef Al Mouatamid and 2 other authors View PDF HTML (experimental) Abstract:Computing legal indicators from normative texts is a key task in legal monitoring and policy evaluation, but presents significant challenges due to the complexity, scale, and interpretive nature of legal language, as well as the variability in available document quality.
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