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Computers, Volume 14, Issue 12
December 2025 - 65 articles
Cover Story: Entity resolution in administrative and census data is challenged by noise, ambiguity, and limited interpretability in monolithic AI systems. This work introduces a multi-agent Retrieval-Augmented Generation (RAG) framework that decomposes entity resolution into specialized, cooperating agents for direct matching, relational inference, household discovery, and movement detection. Orchestrated using LangGraph, the framework integrates deterministic preprocessing with LLM-driven reasoning and evidence-grounded retrieval. Experimental results demonstrate improved accuracy, reduced API usage, and fully traceable decision paths compared to single-LLM approaches. The proposed architecture offers a scalable and interpretable foundation for next-generation entity resolution across census, healthcare, and administrative data domains. View this paper
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Computers - ISSN 2073-431X