Title: Self-Resource Allocation in Multi-Agent LLM Systems
Primary area: foundation or frontier models, including LLMs
Keywords: Multi-Agent Systems, Large Language Models, Task Allocation

Abstract: With the development of LLMs as agents, there is a growing interest in connecting agents into multi-agent systems to solve tasks concurrently, focusing on their role in task assignment and coordination. This paper explores how LLMs can allocate computational tasks among agent networks, considering factors such as cost, efficiency, and performance. We address key questions, including the effectiveness of LLMs as orchestrators and planners, comparing their effectiveness in task assignment and coordination. Our experiments show that LLMs can achieve high validity and accuracy in resource allocation tasks. We find that the planner method outperforms the orchestrator method in handling concurrent actions, resulting in improved efficiency and better utilization of agents. Furthermore, we show that providing explicit information about worker capabilities improves the allocation strategies of planners, particularly when dealing with suboptimal workers.

TL;DR: This paper explores how LLMs optimize task allocation in multi-agent systems, evaluating their role as orchestrators and planners, and assessing task assignment based on cost and agent capabilities.