LR-Robot: A Unified Supervised Intelligent Framework for Real-Time Systematic Literature Reviews with Large Language Models

2026-03-18

AI Summary

This work proposes a human-AI collaborative intelligence framework that integrates expert supervision with large language models to overcome the limitations of existing systematic literature review tools, which often exhibit constrained contextual understanding and rely heavily on expert intervention. By synergistically combining retrieval-augmented generation (RAG) with structured knowledge sources, the framework enables multidimensional classification of academic literature, construction of relational knowledge graphs, and fine-grained analysis of thematic evolution. Evaluated in the domain of option pricing, the approach significantly enhances the efficiency and accuracy of systematic reviews, successfully uncovering research trends, thematic interconnections, and high-impact directions. This study thus establishes a scalable and intelligent paradigm for conducting systematic literature reviews in complex scholarly domains.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Recent advances in artificial intelligence (AI) and natural language processing (NLP) have enabled tools to support systematic literature reviews (SLRs), yet existing frameworks often produce outputs that are efficient but contextually limited, requiring substantial expert oversight.The framework employs a human-in-the-loop process to define sub-SLR tasks, evaluate models, and ensure methodological rigor, while leveraging structured knowledge sources and retrieval-augmented generation (RAG) to enhance factual grounding and transparency. LR-Robot enables multidimensional categorization of research, maps relationships among papers, identifies high-impact works, and supports historical, fine-grained analyses of topic evolution. We demonstrate the framework using an option pricing case study, enabling comprehensive literature analysis. Empirical results reveal the current capabilities of AI in understanding and synthesizing literature, uncover emerging trends, reveal topic connections, and highlight core research directions. By accelerating labor-intensive review stages while preserving interpretive accuracy, LR-Robot provides a practical, customizable, and high-quality approach for AI-assisted SLRs. Key contributions: (1) a novel framework combining AI and expert supervision for contextually informed SLRs, (2) support for multidimensional categorization, relationship mapping, and fine-grained topic evolution analysis, and (3) empirical demonstration of AI-driven literature synthesis in the field of option pricing.

Problem

• Systematic Literature Reviews
• Contextual Limitation
• Expert Oversight
• Literature Synthesis
• Topic Evolution

Innovation

• Retrieval-Augmented Generation
• Human-in-the-Loop
• Systematic Literature Review
• Multidimensional Categorization
• Topic Evolution Analysis