Deep Research Agent: An AI-Powered System for Automated Research Paper Analysis and Citation-Based Answer Generation
TL;DR
Deep Research Agent is presented, an AI-powered system for the automated analysis of research papers and the generation of citation-based answers, which focuses specifically on local scholarly-paper analysis, hybrid retrieval, explicit evidence mapping, and citation-grounded responses.
Abstract
The rapid growth of scholarly publications makes it difficult for researchers and students to locate relevant evidence, compare findings, and produce source-grounded answers efficiently. This paper presents Deep Research Agent, an AI-powered system for the automated analysis of research papers and the generation of citation-based answers. The system ingests uploaded PDF research papers, extracts and cleans the text, segments the content into retrievable passages, generates semantic embeddings using Sentence-BERT, stores the vectors in ChromaDB, and combines dense retrieval with BM25 keyword matching. Retrieved evidence is passed to a large language model via citation-aware prompts, ensuring that generated answers remain linked to the source passages. A Neo4j knowledge graph layer supports exploration of entity and topic relationships, while a Streamlit interface provides an accessible workflow for uploading papers and asking research questions. Compared with generic RAG assistants, the proposed system focuses specifically on local scholarly-paper analysis, hybrid retrieval, explicit evidence mapping, and citation-grounded responses. The revised manuscript also discusses practical deployment issues, including document quality, retrieval latency, privacy, evaluation, and scalability.