Journal of Advances in Developmental Research

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Evaluation and Benchmarking of Multi-Agent LLM Systems: A Comprehensive Review

Author(s) Yash Agrawal
Country United States
Abstract We are seeing exciting new possibilities as systems built on large language models (LLMs) begin to work together in teams, collaborating, negotiating, and coordinating to tackle complex tasks. However, figuring out how to properly evaluate these multi-agent systems is still a work in progress. Many current approaches rely on overly simple tasks, scattered observations, or domain-specific benchmarks. This review takes a closer look at how these systems are currently being evaluated, introduces a framework for understanding different evaluation needs, and highlights major gaps. It also suggests a path forward toward more consistent and reliable benchmarking. Clear, structured evaluation methods are essential for measuring how well these systems collaborate, adapt, scale, and interact with humans. Establishing shared standards will help advance the field, make results easier to compare, and support the safe and effective use of multi-agent systems.
Keywords Multi-agent systems, large language models, emergent behavior, robustness and safety, human–AI teaming, scalability and efficiency, collective intelligence, trust and interpretability, standardized metrics, synthetic societies, cross-domain evaluation, reproducibility.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 15, Issue 2, July-December 2024
Published On 2024-11-12
Cite This Evaluation and Benchmarking of Multi-Agent LLM Systems: A Comprehensive Review - Yash Agrawal - IJAIDR Volume 15, Issue 2, July-December 2024.
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