How can I best benchmark LLM agent collaboration and competition?
MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents
This paper introduces MultiAgentBench, a benchmark for evaluating how well multiple AI agents based on Large Language Models (LLMs) can work together or compete in various tasks. Key points for LLM-based multi-agent systems include: MultiAgentBench tests collaboration and competition across different domains (research, coding, gaming, negotiations); it uses new ways to measure how well the agents coordinate, communicate, and plan; the study found that a good LLM is crucial for success, but coordination also matters; agents can show emergent behaviors like strategic information sharing and adapting their roles during tasks; and while better LLMs lead to better performance, effective collaboration is still key, even against weaker opponents.