Multi-Agent Debate (MAD) is a collaborative framework in which multiple agents iteratively refine solutions through the generation of reasoning and alternating critique cycles. Current work primarily optimizes intra-round topologies and inter-round interactions separately, limiting the adaptation of token costs to task complexity. This work introduces Heterogeneous Consensus-Progressive Reasoning for Efficient Multi-Agent Debate (HCP-MAD), leveraging consensus as a dynamic signal to facilitate progressive reasoning. The core motivation is that a majority of straightforward tasks can be effectively resolved via lightweight pair-agent debates, while complex tasks require expanded collaboration. Firstly, Heterogeneous Consensus Verification conducts rapid consensus verification using a pair of heterogeneous agents for early stopping. Next, Heterogeneous Pair-Agent Debate applies an adaptive stopping criterion to terminate mutual critique of reasoning traces. Finally, the unresolved tasks are addressed through Escalated Collective Voting by aggregating diverse perspectives from additional agents. Experiments across six benchmarks show that HCP-MAD enhances accuracy while substantially reducing token costs. Code is https://github.com/fuyu66/HCP-MAD.
CortexDebate: Debating Sparsely and Equally for Multi-Agent Debate
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；College of Artificial Intelligence, Nanjing Agricultural University（南京农业大学人工智能学院）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）
MADRA: Multi-Agent Debate for Risk-Aware Embodied Planning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；University of Chinese Academy of Sciences, Nanjing（中国科学院大学南京校区）
Beyond Symmetric Agents: Cognitive Diversity and Multi-Agent Debate in Small Language Models
Boston Children’s Hospital, Harvard Medical School（波士顿儿童医院，哈佛医学院）；Harvard School of Engineering and Applied Sciences, Harvard College（哈佛工程与应用科学学院，哈佛学院）
MAD-Fact: A Multi-Agent Debate Framework for Long-Form Factuality Evaluation in LLMs
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100085, China（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049, China（中国科学院大学网络安全学院）
Breaking the Martingale Curse: Multi-Agent Debate via Asymmetric Cognitive Potential Energy
Mohamed bin Zayed University of Artificial Intelligence（莫扎德·本·扎耶德人工智能大学）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学国际关系学院）；Harvard University（哈佛大学）
Gradual Vigilance and Interval Communication: Enhancing Value Alignment in Multi-Agent Debates
National Engineering Research Center of Educational Big Data, Central China Normal University（华中师范大学教育大数据国家工程研究中心）；Faculty of Artificial Intelligence in Education, Central China Normal University（华中师范大学人工智能教育学部）
DynaDebate: Breaking Homogeneity in Multi-Agent Debate with Dynamic Path Generation
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（认知智能国家重点实验室，中国科学技术大学）；North Automatic Control Technology Institute（北自动控制技术研究所）；Shenzhen Institute for Advanced Study, UESTC（深圳先进研究 institute, 电子科技大学）
MIND: Multi-agent inference for negotiation dialogue in travel planning
School of Mechanical Engineering, Sungkyunkwan University（成均馆大学机械工程学院）；Department of Applied Artificial Intelligence, Sungkyunkwan University（成均馆大学应用人工智能系）；Department of Software, Sungkyunkwan University（成均馆大学软件系）
Epistemic Gain, Aleatoric Cost: Uncertainty Decomposition in Multi-Agent Debate for Math Reasoning
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；ByteDance（字节跳动）；Technical University of Darmstadt（达姆施塔特工业大学）；Tongji University（同济大学）；Vector Institute（向量研究所）
Group Perspective Matters: Regulating Debate Relationships Can Mitigate Blind Conformity in Multi-Agent Debate
School of Computer Science & Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence（北京市交通数据挖掘与具身智能重点实验室）
Does Multi-Agent Debate Improve AI Feedback on Research Papers?
Charles University, Faculty of Social Sciences, Institute of Economic Studies（查理大学社会科学学院经济研究所）；Charles University, Prague Centre for Economic Policy Research, London（查理大学布拉格经济政策研究中心，伦敦）；Meta-Research Innovation Center at Stanford（斯坦福大学元研究创新中心）
