Abstract: When large language models(LLMs) handle knowledge-intensive tasks,issues of hallucinations and knowledge timeliness restrict their application in high-reliability fields.Traditional RAG(Retrieval-Augmented Generation) systems help LLMs expand their knowledge boundaries by invoking external knowledge;however,they suffer from limitations such as insufficient semantic relevance and rigid retrieval strategies in multi-hop reasoning tasks.To break through the aforementioned bottlenecks,this study proposes an intelligent iterative retrieval-augmented generation framework(PR-RAG) based on retrieval task planning and a verification-reflection mechanism.Specifically,this framework employs a task planning module to conduct hierarchical classification of questions and decomposition of sub-problems,acquires contextual information via an adaptive hybrid retrieval strategy that integrates vector retrieval and graph retrieval,and ultimately realizes closed-loop optimization through a dual verification mechanism(including logical verification and factual verification).Experimental results show that the exact matching of PR-RAG on datasets such as HotpotQA and 2WikiMQA are increased by an average of 12.8% compared with baseline methods,which effectively improves the accuracy and robustness in handling complex knowledge-intensive tasks.This study confirms that the synergistic feedback between hierarchical retrieval task planning and the verification-reflection mechanism can significantly enhance the knowledge reasoning capabilities of LLMs and the reliability of the system.

Key words: Large language models, Retrieval-augmented generation, Intelligent agent, Question-answering system, Knowledge graph