N2I-RAG is an agentic RAG pipeline that automates binary legal indicator computation from complex normative texts with explicit traceability to provisions.
https://arxiv.org/abs/2502.04413
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ReCellTy constructs a knowledge graph with 18850 nodes and 48944 edges, retrieves relevant entities for differential genes, and applies multi-task LLM reasoning to improve single-cell type annotation over standard LLMs by up to 0.21 in human scores and 6.1% in semantic similarity.
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From Norms to Indicators (N2I-RAG): An Agentic Retrieval-Augmented Generation Framework for Legal Indicator Computation
N2I-RAG is an agentic RAG pipeline that automates binary legal indicator computation from complex normative texts with explicit traceability to provisions.
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ReCellTy: Domain-Specific Knowledge Graph Retrieval-Augmented LLMs Reasoning Workflow for Single-Cell Annotation
ReCellTy constructs a knowledge graph with 18850 nodes and 48944 edges, retrieves relevant entities for differential genes, and applies multi-task LLM reasoning to improve single-cell type annotation over standard LLMs by up to 0.21 in human scores and 6.1% in semantic similarity.