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

Paper: arXiv 2603.17723

Authors: Wei Wei, Jin Zheng, Zining Wang

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.

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 5.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents an applied AI framework leveraging RAG and LLMs for systematically reviewing finance literature, using option pricing as a case study. While mathematically light, it offers a pragmatic empirical evaluation of LLM capabilities in synthesizing complex domain-specific research.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Efficient, Context-Aware SLRs"] --> B["Human-in-the-Loop Task Definition<br>& Expert Supervision"]
    C[("Input:<br>Structured Knowledge Sources<br>& Paper Corpus")] --> D
    B --> D["Computational Engine:<br>RAG + LLM Processing"]
    D --> E["Data Synthesis:<br>Multidimensional Categorization<br>& Topic Mapping"]
    E --> F["Option Pricing Case Study"]
    F --> G["Key Outcomes:<br>Automated SLR, Relationship Maps<br>& Trend Identification"]