LLM_MultiAgents_Survey_Papers collects recent survey papers focusing on the intersection of Large Language Models (LLMs) and multi-agent systems. This repository aims to provide a structured overview of the current research landscape, highlighting key advancements and challenges in this rapidly evolving field. The papers are categorized into several streams, offering a comprehensive view of different approaches and applications. We are particularly interested in how LLMs enable novel solutions for coordination, communication, and collective intelligence in multi-agent settings.
This repository offers a curated collection of recent survey papers, providing a concise and organized view of the burgeoning field of LLM-based multi-agents. It categorizes papers into distinct streams – frameworks, orchestration, problem-solving, and more – making it easier to navigate and locate relevant research. The repository is updated regularly, ensuring users have access to the latest developments. Furthermore, the clear categorization and concise descriptions facilitate efficient literature review.
- Multi-Agents Framework: Papers detailing architectural designs and foundational structures for implementing multi-agent systems with LLMs.
- Multi-Agents Orchestration and Efficiency: Research focusing on coordinating and optimizing the performance of multiple LLM-powered agents.
- Multi-Agents for Problem Solving: Studies exploring the application of multi-agent systems with LLMs to tackle diverse problem-solving tasks.
- Software Development: Applications of LLM-based multi-agents to automate and enhance software engineering processes.
- Maturity: Regularly updated with new publications, demonstrating active maintenance and a growing body of work. The inclusion of diverse and recent papers indicates a vibrant and expanding research community.
- Community: Includes links to papers, facilitating easy access to the research.
- Easy to navigate: Clearly categorized and well organized, making it straightforward to find relevant information.
The repository is actively maintained with new papers added regularly, demonstrating ongoing effort to keep the collection current. The inclusion of recent publications and a structured categorization suggest a healthy and active community interest in this area. The repository is continuously updated with categorized papers.
This repository serves researchers, practitioners, and students interested in understanding the state-of-the-art in LLM-based multi-agent systems. It provides a convenient entry point for exploring recent advancements, identifying research trends, and discovering relevant resources. By consolidating survey papers into logical categories, the repository streamlines the process of understanding the field and identifying potential avenues for further investigation.