🤖 AI Summary
Multi-agent large language models (MA-LLMs) lack systematic performance benchmarking, hindering principled design and deployment.
Method: We propose the first problem-solving–oriented analytical framework for MA-LLM influencing factors, integrating multi-agent systems theory, prompt engineering, distributed negotiation modeling, and empirical attribution analysis to systematically decouple three core variables: role design, communication topology, and decision-making mechanisms.
Contribution/Results: We (i) uncover coupling patterns among scalability, communication structure, and decision paths; (ii) rigorously characterize the performance advantage boundary of MA-LLMs over single-agent LLMs; (iii) identify three pervasive bottlenecks—excessive communication overhead, role homogenization, and consensus drift; and (iv) derive actionable architectural optimizations that jointly enhance computational efficiency and collaborative robustness. This work establishes foundational insights for principled MA-LLM development and deployment.
📝 Abstract
Multi-agent large language models (MA-LLMs) are a rapidly growing research area that leverages multiple interacting language agents to tackle complex tasks, outperforming single-agent large language models. This literature review synthesizes the latest research on agent profiles, communication structures, and decision-making processes, drawing insights from both traditional multi-agent systems and state-of-the-art MA-LLM studies. In doing so, it aims to address the lack of direct comparisons in the field, illustrating how factors like scalability, communication structure, and decision-making processes influence MA-LLM performance. By examining frequent practices and outlining current challenges, the review reveals that multi-agent approaches can yield superior results but also face elevated computational costs and under-explored challenges unique to MA-LLM. Overall, these findings provide researchers and practitioners with a roadmap for developing robust and efficient multi-agent AI solutions.