Multi-Agent Systems and Large Language Models in Research

The field of artificial intelligence is witnessing a significant shift towards the development of multi-agent systems and large language models. These advancements are enabling autonomous, scalable, and iterative analysis in various domains, including social science and engineering. Noteworthy papers in this area include LUCID-MA, which introduces a multi-agent framework for crime data analysis and prediction, and Xolver, a multi-agent reasoning framework that equips a large language model with a persistent, evolving memory of holistic experience. AgentGroupChat-V2 is another notable framework that addresses challenges in system architecture design, cross-domain generalizability, and performance guarantees through a divide-and-conquer fully parallel architecture and adaptive collaboration engine. These innovative approaches are advancing the field by providing efficient, general-purpose solutions for complex reasoning scenarios and demonstrating significant potential in social simulation and task resolution domains. The integration of large language models with external tools and multi-agent architectures is leading to improved performance and more accurate results in various applications, including foundation design automation and competitive programming.

Sources

AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction

LLMs for Sentence Simplification: A Hybrid Multi-Agent prompting Approach

LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?

Investigating the Potential of Large Language Model-Based Router Multi-Agent Architectures for Foundation Design Automation: A Task Classification and Expert Selection Study

Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need

Managing Complex Failure Analysis Workflows with LLM-based Reasoning and Acting Agents

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