Multi Agent

Multi-agent systems research focuses on designing and analyzing systems composed of multiple interacting agents, aiming to achieve complex goals through collaboration or competition. Current research emphasizes leveraging large language models (LLMs) to enhance agent capabilities, particularly in reasoning, planning, and communication, often employing architectures like multi-agent reinforcement learning (MARL) and novel communication pipelines to improve efficiency and robustness. This field is significant for advancing AI capabilities in diverse applications, including robotics, autonomous driving, and scientific discovery, by enabling more sophisticated and adaptable intelligent systems.