Adaptive Multi-agent Systems

Adaptive In-conversation Team Building for Language Model Agents: Enhancing Flexibility and Performance in Complex Task Solving

Leveraging multiple large language model (LLM) agents can effectively tackle complex tasks, but the design of such multi-agent systems remains challenging. This work introduces a new adaptive team-building paradigm, realized through the Captain Agent, which dynamically forms and manages teams for each step of a task-solving process to ensure diverse expertise and prevent stereotypical outputs.

自適應對話式團隊建構以提升語言模型代理的能力

Adaptive In-conversation Team Building for Language Model Agents: Enhancing Flexibility and Performance in Complex Task Solving