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This research introduces Conditional Progressive Pruning (CPP), a lightweight framework designed to optimize Multi-Agent Debate by dynamically pruning unnecessary communication rounds while maintaining performance. By successfully outperforming both single-agent baselines and consistency methods under strict cost constraints, the study addresses critical efficiency challenges in test-time scaling for LLMs. This advancement validates multi-agent collaboration as a viable strategy for complex reasoning tasks without incurring prohibitive computational overhead.
Beyond Solo and Consistency: Vindicating Multi-Agent Debate via Conditional Progressive Pruning
arXiv:2609.33974v1 Announce Type: new 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.
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
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.
Computation and Language (cs.CL)
arXiv:2609.33974 [cs.CL]
(or arXiv:2609.33974v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.33974 arXiv-issued DOI via DataCite (pending registration)
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