SWARM-SLR AIssistant: A Unified Framework for Scalable Systematic Literature Review Automation

2026-03-05

AI Summary

This study addresses critical bottlenecks in current systematic literature review (SLR) tools, particularly their limited scalability and poor integration into user-friendly workflows. To overcome these challenges, the authors propose a unified framework that combines structured SLR methodologies with large language model (LLM)-based intelligent agents. The framework features modular interfaces for integrating diverse research tools and introduces a centralized, metadata-driven tool registry that enables developers to annotate and share tool specifications autonomously. This design substantially enhances the modularity, extensibility, and automation of SLR processes. Preliminary evaluation indicates marked improvements in system usability; however, balancing efficiency, accessibility, and transparency remains a significant challenge.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Metareasoning and Metaheuristics
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Despite a growing ecosystem of tools supporting Systematic Literature Reviews (SLRs), integrating them into user-friendly workflows remains challenging. The Streamlined Workflow for Automating Machine-Actionable Systematic Literature Reviews (SWARM-SLR) unified the tool annotation and provided a cohesive yet modular workflow, but faced scalability and usability issues. We introduce the SWARM-SLR AIssistant, a unified framework that combines the SWARM-SLR's structured methodology with an agent-based assistant that integrates research tools in a modular interface. The first SWARM-SLR stage is integrated, enabling conversational, LLM-guided support and persistent data storage. To address the tool assessment bottleneck, we propose a centralized tool registry that allows developers to annotate and register tools autonomously using a shared metadata schema. Preliminary evaluation shows improved usability, but challenges remain in balancing efficiency, accessibility, and transparency. Further development is needed to realize scalable SLR automation.

Problem

• Systematic Literature Review
• Scalability
• Usability
• Tool Integration
• Automation

Innovation

• Systematic Literature Review
• LLM-guided assistant
• tool registry
• modular workflow
• scalable automation