2025· International Journal of Emerging Trends in Multidisciplinary Research· 0 citations
TL;DR
This study presents a conceptual framework that combines semantic retrieval, intelligent reasoning, automated literature analysis, and workflow orchestration, demonstrating how LLM-powered systems can transform scientific research into scalable, accurate, ethical, and collaborative knowledge discovery processes.
Abstract
Scientific publishing, digital repositories, patents, and multidisciplinary research datasets have expanded rapidly, making traditional literature review methods increasingly inefficient. Large Language Models (LLMs) address this challenge by enabling intelligent knowledge discovery, semantic search, literature summarization, research gap identification, hypothesis generation, citation assistance, and academic writing support. By integrating Retrieval-Augmented Generation (RAG), vector databases, knowledge graphs, citation networks, and domain-specific ontologies, LLMs improve contextual relevance, reduce hallucinations, and enhance research accuracy. These capabilities accelerate interdisciplinary collaboration, automate research workflows, and support evidence-based decision-making. However, challenges such as hallucination, bias, outdated knowledge, explainability, privacy, intellectual property, reproducibility, and computational requirements remain significant. Modern AI-assisted research systems increasingly incorporate human-in-the-loop validation, explainable AI, and responsible governance to ensure trustworthy outcomes. This study presents a conceptual framework that combines semantic retrieval, intelligent reasoning, automated literature analysis, and workflow orchestration, demonstrating how LLM-powered systems can transform scientific research into scalable, accurate, ethical, and collaborative knowledge discovery processes.
Artificial Intelligence (AI) and Large Language Models (LLMs) have significantly transformed knowledge management by enabling intelligent, context-aware, and automated information access. However, standalone LLMs often suffer from limitations such as outdated knowledge, hallucinated responses, lack of domain-specific expertise, and limited transparency, reducing their reliability in enterprise and research applications. Retrieval-Augmented Generation (RAG) has emerged as an effective solution by combining language models with external knowledge retrieval, allowing responses to be generated using up-to-date and relevant information. This study proposes a comprehensive Retrieval-Augmented Generation framework for intelligent knowledge management systems. The framework integrates document acquisition, preprocessing, semantic embedding generation, vector database indexing, document retrieval, prompt augmentation, LLM-based response generation, response validation, and continuous knowledge base updates. It supports diverse knowledge sources, including enterprise databases, technical documents, digital libraries, and research repositories, while incorporating sparse, dense, hybrid retrieval, and neural reranking techniques to improve retrieval accuracy. The proposed framework is evaluated using retrieval precision, recall, F1-score, response relevance, latency, grounding accuracy, and user satisfaction. Results demonstrate improved semantic understanding, reduced hallucinations, enhanced factual correctness, and real-time knowledge updates compared with conventional keyword-based knowledge management systems. The study also discusses future directions, including multimodal RAG, graph-enhanced retrieval, federated knowledge management, continual learning, and autonomous enterprise knowledge assistants, establishing RAG as a robust foundation for trustworthy and intelligent knowledge-driven AI systems.
Louis Pouzin, J. Arsac· International Journal of Mod...· 0 citations
This study proposes an LLM-powered knowledge management framework that combines Retrieval-Augmented Generation (RAG), semantic embeddings, enterprise-specific language models, and vector databases to transform enterprise data into actionable knowledge.
Farhan Malik, Zara Ahmed· International Journal of App...· 0 citations
The Structural Gap Hypothesis Agent (SGHA), a fully automated, corpus-first research-problem discovery system that runs entirely on a local LLM, is introduced and compared with the AI Scientist-v2 idea formulation module in five machine-learning domains.
A layered reliability framework is defined in which graph-based inference addresses knowledge incompleteness, retrieval-augmented prompt control mitigates instability, and ontology grounding reduces semantic ambiguity, providing a foundation for more reliable biomedical AI systems.
Experimental results indicate that coordinated autonomous agents significantly reduce research time, improve workflow consistency, enhance knowledge discovery, and increase scientific productivity compared with conventional AI-based research assistants.
Anatoly Kitov, M. Kartsev· International Journal of Eme...· 0 citations
A literature-based architectural framework for reliable knowledge retrieval systems that separates external knowledge management from LLM-based reasoning and generation is developed and indicates that reliable LLM deployment should be treated as an end-to-end architectural problem rather than solely a model-performance problem.
Bharat Kumar Reddy Karumuri· International Journal of Eng...· 0 citations
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