MARS: toward more efficient multi-agent collaboration for LLM reasoning

• URL: http://arxiv.org/abs/2509.20502v1
• Date: Wed, 24 Sep 2025 19:24:33 GMT
• Title: MARS: toward more efficient multi-agent collaboration for LLM reasoning
• Authors: Xiao Wang, Jia Wang, Yijie Wang, Pengtao Dang, Sha Cao, Chi Zhang,
• Abstract summary: Multi-Agent Review System (MARS) is a role-based collaboration framework inspired by the review process.<n>We show that MARS matches the accuracy of Multi-Agent Debate (MAD) while reducing both token usage and inference time by approximately 50%.
• Score: 12.889395413072696
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have achieved impressive results in natural language understanding, yet their reasoning capabilities remain limited when operating as single agents. Multi-Agent Debate (MAD) has been proposed to address this limitation by enabling collaborative reasoning among multiple models in a round-table debate manner. While effective, MAD introduces substantial computational overhead due to the number of agents involved and the frequent communication required. In this paper, we propose MARS (Multi-Agent Review System), a role-based collaboration framework inspired by the review process. In MARS, an author agent generates an initial solution, reviewer agents provide decisions and comments independently, and a meta-reviewer integrates the feedback to make the final decision and guide further revision. This design enhances reasoning quality while avoiding costly reviewer-to-reviewer interactions, thereby controlling token consumption and inference time. We compared MARS with both MAD and other state-of-the-art reasoning strategies across multiple benchmarks. Extensive experiments with different LLMs show that MARS matches the accuracy of MAD while reducing both token usage and inference time by approximately 50\%. Code is available at https://github.com/xwang97/MARS.

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