Open-Source LLMs Collaboration Beats Closed-Source LLMs: A Scalable Multi-Agent System

• URL: http://arxiv.org/abs/2507.14200v1
• Date: Mon, 14 Jul 2025 16:17:11 GMT
• Title: Open-Source LLMs Collaboration Beats Closed-Source LLMs: A Scalable Multi-Agent System
• Authors: Shengji Tang, Jianjian Cao, Weihao Lin, Jiale Hong, Bo Zhang, Shuyue Hu, Lei Bai, Tao Chen, Wanli Ouyang, Peng Ye,
• Abstract summary: This paper aims to demonstrate the potential and strengths of open-source collectives.<n>We propose SMACS, a scalable multi-agent collaboration system (MACS) framework with high performance.<n> Experiments on eight mainstream benchmarks validate the effectiveness of our SMACS.
• Score: 51.04535721779685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper aims to demonstrate the potential and strengths of open-source collectives. It leads to a promising question: Can we harness multiple open-source LLMs to match or even beat the closed-source LLMs? To answer this, we propose SMACS, a scalable multi-agent collaboration system (MACS) framework with high performance. Specifically, for continuous integration of new LLMs and generalization to diverse questions, we first propose a Retrieval-based Prior Selection (RPS), which assigns a proxy performance score to each LLM to select the Top-k LLMs at the instance level for any given question. Then, we propose an Exploration-Exploitation-Driven Posterior Enhancement (EPE), encouraging the generation of diverse responses through prior dropping and selecting the high-quality response via a hybrid posterior score. Experiments on eight mainstream benchmarks validate the effectiveness of our SMACS: by integrating fifteen open-source LLMs, SMACS outperforms leading closed-source LLMs in 2025, e.g., Claude-3.7-Sonnet (+12.73%), GPT-4.1(+5.36%) and GPT-o3-mini(+5.28%) across multiple tasks. Remarkably, it even exceeds the average of best results of different datasets from both open-source LLMs (+2.86%) and closed-source LLMs (+2.04%), pushing the upper bound of intelligence. Code will be released at https://github.com/magent4aci/SMACS.

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