arXiv:2608.03239v2 Announce Type: replace
Abstract: Large language model-based multi-agent systems (LLM-MAS) are designed through roles, debate protocols, and aggregation rules. These choices create...
By Ming Shen, Chao Shang, Sadat Shahriar, Devang Kulshreshtha, Yi Zhang, Sandesh Swamy, Yanjun Qi
arXiv:2607. 26212v1 Announce Type: cross Abstract: Multi-Agent Debate (MAD) is a promising paradigm for improving the accuracy and robustness of Large Language Model (LLM)-based agentic systems.
By Quim Motger, Marc Oriol, Jordi Marco, Xavier Franch
arXiv:2606. 04223v1 Announce Type: new Abstract: Multi-agent systems are commonly designed to reduce disagreement through voting, consensus protocols, debate, or fault-tolerant aggregation.
By Micha{\l} Wawer, Jaros{\l}aw A. Chudziak
arXiv:2510. 20963v2 Announce Type: replace Abstract: Multi-agent debate (MAD) was proposed as a promising approach for ensembling the wisdom of multiple large language models (LLMs) to improve reasoning and provide effective supervision to superhuman LLMs.
By Yongqiang Chen, Gang Niu, James Cheng, Bo Han, Masashi Sugiyama
arXiv:2607. 06157v1 Announce Type: cross Abstract: Deliberation plays a crucial role in collaboration; when humans work together, they naturally engage in communication to align information and reach an agreement.
By Chenxu Wang, Yongkun Yang, Boyuan Du, Shiwei Lin, Huaping Liu
arXiv:2606. 00655v1 Announce Type: cross Abstract: The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental questions regarding their scaling behavior and intrinsic collective dynamics remain underexplored.
By Jialing Li, Zhouhong Gu, Yin Cai, Hongwei Feng