{"ID":2860741,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.02668","arxiv_id":"2510.02668","title":"AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems","abstract":"Foundation models have revolutionized artificial intelligence, yet their application in recommender systems remains limited by reasoning opacity and knowledge constraints. This paper introduces AgenticRAG, a novel framework that combines tool-augmented foundation models with retrieval-augmented generation for zero-shot explainable recommendations. Our approach integrates external tool invocation, knowledge retrieval, and chain-of-thought reasoning to create autonomous recommendation agents capable of transparent decision-making without task-specific training. Experimental results on three real-world datasets demonstrate that AgenticRAG achieves consistent improvements over state-of-the-art baselines, with NDCG@10 improvements of 0.4\\% on Amazon Electronics, 0.8\\% on MovieLens-1M, and 1.6\\% on Yelp datasets. The framework exhibits superior explainability while maintaining computational efficiency comparable to traditional methods.","short_abstract":"Foundation models have revolutionized artificial intelligence, yet their application in recommender systems remains limited by reasoning opacity and knowledge constraints. This paper introduces AgenticRAG, a novel framework that combines tool-augmented foundation models with retrieval-augmented generation for zero-shot...","url_abs":"https://arxiv.org/abs/2510.02668","url_pdf":"https://arxiv.org/pdf/2510.02668v1","authors":"[\"Bo Ma\",\"Hang Li\",\"ZeHua Hu\",\"XiaoFan Gui\",\"LuYao Liu\",\"Simon Liu\"]","published":"2025-10-03T01:52:37Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.AI\"]","methods":"[\"RAG\"]","has_code":false}