超越单打独斗与一致性：通过条件渐进剪枝验证多智能体辩论

Computer Science > Computation and Language arXiv:2609.33974 (cs) [Submitted on 27 Sep 2026] Title:Beyond Solo and Consistency: Vindicating Multi-Agent Debate via Conditional Progressive Pruning Authors:Ruosong Ye, Caiqi Zhang, Jiahao Li, Haijun Wu, Xiaolong Luo, Huiyuan Chen, Yu Wang, Ying Chen, Zhenting Wang, Kai Mei, Yang Zhou, Dimitris N. Metaxas View a PDF of the paper titled Beyond Solo and Consistency: Vindicating Multi-Agent Debate via Conditional Progressive Pruning, by Ruosong Ye and 11 other authors View PDF HTML (experimental) Abstract:Large Language Model (LLM) based Multi-Agent Debate (MAD) is one of the most effective test time scaling techniques. Through multi-round communication, agents complement each other in knowledge and reasoning and solve tasks that no single member can solve. However, existing MAD frameworks fail to beat strong Single Agent and Consistency-based baselines under the same strict cost limit, which shakes the foundation of the MAD field. We propose Conditional Progressive Pruning (CPP), a lightweight pruning framework that fully exploits multi-round MAD. CPP outperforms all existing MAD frameworks on multiple dominated benchmarks. It is also the first to fully outperform consistency methods. Our code, detailed agent interaction records will be released soon. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2609.33974 [cs.CL] (or arXiv:2609.33974v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.33974 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Kai Mei [view email] [v1] Sun, 27 Sep 2026 22:16:04 UTC (1,249 KB) Full-text links: Access Paper: View a PDF of the paper titled Beyond Solo and Consistency: Vindicating Multi-Agent Debate via Conditional Progressive Pruning, by Ruosong Ye and 11 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)