X-MAS: Towards Building Multi-Agent Systems with Heterogeneous LLMs

• URL: http://arxiv.org/abs/2505.16997v1
• Date: Thu, 22 May 2025 17:56:39 GMT
• Title: X-MAS: Towards Building Multi-Agent Systems with Heterogeneous LLMs
• Authors: Rui Ye, Xiangrui Liu, Qimin Wu, Xianghe Pang, Zhenfei Yin, Lei Bai, Siheng Chen,
• Abstract summary: This paper explores the paradigm of heterogeneous LLM-driven multi-agent systems (MAS)<n>We introduce X-MAS-Bench, a comprehensive testbed designed to evaluate the performance of various LLMs across different domains and MAS-related functions.<n>We demonstrate that transitioning from homogeneous to heterogeneous MAS can significantly enhance system performance without requiring structural redesign.
• Score: 38.8226073406788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LLM-based multi-agent systems (MAS) extend the capabilities of single LLMs by enabling cooperation among multiple specialized agents. However, most existing MAS frameworks rely on a single LLM to drive all agents, constraining the system's intelligence to the limit of that model. This paper explores the paradigm of heterogeneous LLM-driven MAS (X-MAS), where agents are powered by diverse LLMs, elevating the system's potential to the collective intelligence of diverse LLMs. We introduce X-MAS-Bench, a comprehensive testbed designed to evaluate the performance of various LLMs across different domains and MAS-related functions. As an extensive empirical study, we assess 27 LLMs across 5 domains (encompassing 21 test sets) and 5 functions, conducting over 1.7 million evaluations to identify optimal model selections for each domain-function combination. Building on these findings, we demonstrate that transitioning from homogeneous to heterogeneous LLM-driven MAS can significantly enhance system performance without requiring structural redesign. Specifically, in a chatbot-only MAS scenario, the heterogeneous configuration yields up to 8.4\% performance improvement on the MATH dataset. In a mixed chatbot-reasoner scenario, the heterogeneous MAS could achieve a remarkable 47\% performance boost on the AIME dataset. Our results underscore the transformative potential of heterogeneous LLMs in MAS, highlighting a promising avenue for advancing scalable, collaborative AI systems.

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