SCMRAG: Self-Corrective Multihop Retrieval Augmented Generation System for LLM Agents

Existing Retrieval-Augmented Generation (RAG) systems primarily depend on static knowledge vectorstores which combine semantic similarity algorithms with reranking. This often leads to outdated information and retrieval errors. In this paper, we propose SCMRAG, a Self-Corrective Multihop Retrieval Augmented Generation system for LLM agents. We introduce an LLM-assisted dynamic knowledge graph creation step to enhance information retrieval and mitigate hallucinations. Unlike traditional RAG systems, SCMRAG includes a self-corrective agent driven mechanism that autonomously identifies and retrieves missing information from external web sources. Furthermore, SCMRAG's internal reasoning agent determines whether the knowledge graph provides sufficient information or if a corrective step is needed. It further improves retrieval accuracy and efficiency. We benchmark the effectiveness of SCMRAG on five datasets-MultiHop-RAG, ARC AI2, PopQA, PubHealth, and WikiBio; showing significant improvements in retrieval precision and hallucination reduction across diverse tasks. Our results highlight SCMRAG's potential to redefine how LLM agents interact with knowledge bases, offering a more adaptable and reliable solution for a wide range of applications.