Introduction to the LLM_MultiAgents_Survey_Papers Project
The LLM_MultiAgents_Survey_Papers project is dedicated to the exploration of Large Language Model (LLM)-based multi-agent systems as presented in various scholarly papers. This project serves as a comprehensive resource for researchers and enthusiasts interested in the intersection of artificial intelligence and multi-agent systems, summarizing significant academic contributions and insights.
Survey Paper
At the heart of the project is a survey paper that provides an in-depth analysis of LLM-based multi-agent architectures. Available on arXiv, the survey offers a summarized view of the architecture and employs visuals such as diagrams to help readers understand the variety and complexity of these systems. The aim is to provide a roadmap for researchers to navigate the landscape of multi-agent studies supported by LLMs.
Project Goals and Structure
The primary goal of this project is to maintain an updated list of papers related to LLM-based multi-agent systems, facilitating ongoing research and development. The project classifies papers into five key categories:
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Multi-Agents Framework: This category explores foundational frameworks that support multi-agent systems, delving into scalability and the theoretical underpinnings of these architectures.
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Multi-Agents Orchestration and Efficiency: This section focuses on efficiency and coordination among agents, addressing optimization and communication strategies within agent networks.
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Multi-Agents for Problem Solving: Papers here highlight practical applications of multi-agent systems in areas like software development, scientific experiments, and data analysis.
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Multi-Agents for World Simulation: This category simulates real-world scenarios, exploring societal interactions, gaming environments, psychological models, and even economic simulations by leveraging the capabilities of LLMs.
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Multi-Agents Datasets and Benchmarks: This part of the project is dedicated to datasets and benchmarking standards, essential for evaluating the performance and effectiveness of multi-agent systems.
Recent Updates and News
The project team is committed to keeping the database fresh by updating the list of papers bi-weekly. Researchers are encouraged to contribute to this repository by sharing any new papers that fit within the project's scope, ensuring a collaborative and comprehensive resource.
Contribution and Community Engagement
Contributions from the community are highly valued. Researchers and practitioners can contribute by identifying missing papers, suggesting improvements, or even partaking in discussions on advancements in the field. Contact details and guidance for contributions are readily available for interested parties.
Conclusion
The LLM_MultiAgents_Survey_Papers project stands as a valuable resource for anyone interested in the dynamic and rapidly-evolving field of LLM-based multi-agent systems. By categorizing and summarizing significant research papers, this project looks to foster greater understanding and innovation in this exciting domain of artificial intelligence. Whether you're a seasoned researcher or a curious learner, this project offers insights and resources to fuel your exploration of multi-agent systems.