[2509.20502] MARS: toward more efficient multi-agent collaboration for LLM reasoning

Computer Science > Computation and Language arXiv:2509.20502 (cs) [Submitted on 24 Sep 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:MARS: toward more efficient multi-agent collaboration for LLM reasoning Authors:Xiao Wang, Jia Wang, Yijie Wang, Pengtao Dang, Sha Cao, Chi Zhang View a PDF of the paper titled MARS: toward more efficient multi-agent collaboration for LLM reasoning, by Xiao Wang and 5 other authors View PDF HTML (experimental) 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 sta...