International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 3
May-June 2026

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Ai-powered Multi-modal Research Paper Assistant a Smart Retrieval-Augmented System for Researchers and Students

Author(s) Mr. Mohit Kumar Goswami, Ms. Vibha Kamble
Country India
Abstract The exponential growth of scientific and academic publications has created significant challenges for researchers and students in efficiently discovering, understanding, and analysing relevant research papers. Traditional keyword-based search systems often lack semantic understanding and fail to provide contextual, accurate, and intelligent responses from large collections of academic documents. To address these limitations, this thesis presents an AI-Powered Multi-Modal Research Paper Assistant (A Smart Retrieval Augmented System for Researchers and Students), a smart Retrieval-Augmented Generation (RAG)-based framework designed to assist researchers and students in academic
knowledge exploration and literature analysis. The proposed system integrates advanced Artificial Intelligence technologies including Natural Language Processing (NLP), Large Language Models (LLMs), semantic search, vector embeddings, and multi-modal document understanding to enable intelligent
interaction with research papers. The framework processes academic PDF documents through text extraction, document chunking, embedding generation, and vector indexing techniques. Using semantic similarity search and Retrieval-Augmented Generation, the system retrieves contextually relevant information and generates accurate, meaningful, and citation-aware responses to user queries. Unlike traditional research search systems, the proposed assistant combines retrieval mechanisms with generative AI models to significantly reduce hallucination and improve factual reliability. The multi-modal capability of the system further enables understanding of textual content, metadata, tables, and structural information present in research documents. Technologies such as transformer-based architectures, vector databases, and prompt engineering techniques are incorporated to enhance retrieval precision and response quality.
The developed framework supports multiple academic tasks including research paper summarization, semantic question answering, literature review assistance, keyword extraction, and contextual research exploration. Experimental evaluation demonstrates improved retrieval accuracy, faster information access, enhanced semantic understanding, and better user interaction compared to conventional search approaches. The proposed system provides an intelligent and scalable solution for modern academic research environments and contributes toward the advancement of AI-driven educational and research support systems.
Keywords Artificial Intelligence (AI), Retrieval-Augmented Generation (RAG), Multi-Modal AI, Natural Language Processing (NLP), Large Language Models (LLMs), Semantic Search, Research Paper Assistant, Vector Database, Transformer Models, Academic Research Automation, Machine Learning, Information Retrieval, Generative AI, Document Understanding, Embedding Models.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-06-10
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E-ISSN 2582-2160
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