Large Language Models (LLMs) are impressive. They can generate text, translate languages, and even write different kinds of creative content. But when it comes to highly specialized fields, even the most advanced LLMs can struggle. They sometimes hallucinate facts, get stuck with outdated information, and are not transparent about their sources. This makes them unreliable for specialized research. Imagine an AI that could not only answer complex scientific questions but also provide precise citations from relevant research papers. That's the goal of a new framework called SMART-SLIC. This system combines the power of LLMs with the precision of Knowledge Graphs (KGs) and Vector Stores (VSs) to create a research assistant that is both intelligent and reliable. Here’s how it works: SMART-SLIC starts by building a high-quality, domain-specific dataset. This involves gathering relevant research papers, cleaning and standardizing the text, and extracting key features using advanced techniques like tensor factorization. Next, it builds a KG that captures the relationships between different concepts and entities in the dataset, like authors, publications, and topics. Alongside this, it creates a VS that stores the full text of the documents, broken down into smaller chunks for easier searching. When a user asks a question, SMART-SLIC intelligently routes the query to the appropriate toolset. If the question is about a specific document, it uses a Retrieval Augmented Generation (RAG) agent to interact with the VS and find the most relevant text. For more general questions, it queries the KG to extract the necessary information. What sets SMART-SLIC apart is its ability to provide precise attributions. If the answer is found in a specific paragraph of a research paper, it can pinpoint the exact location, providing a DOI for easy reference. In tests focused on malware analysis research, SMART-SLIC outperformed a standalone LLM. While the LLM often struggled or fabricated answers, SMART-SLIC consistently provided accurate responses with citations. This framework has the potential to revolutionize research in a variety of fields, from robotics and materials science to legal cases and quantum computing. By combining the strengths of LLMs with structured knowledge bases, it offers a powerful new way to access and understand complex information.