MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning

Understand

Large Language Model (LLM)-driven Multi-agent systems (Mas) have recently emerged as a powerful paradigm for tackling complex real-world tasks.

• However, existing Mas construction methods typically rely on manually crafted interaction mechanisms or heuristic rules, introducing human biases and constraining the autonomous ability.
• Even with recent advances in adaptive Mas construction, existing systems largely remain within the paradigm of semi-autonomous patterns.
• In this work, we propose MasHost, a Reinforcement Learning (RL)-based framework for autonomous and query-adaptive Mas design.