arXiv:2605.27068v2 Announce Type: replace-cross
Abstract: Social deduction games have become a popular testbed for probing reasoning, deception, coordination, and belief modeling in Large Language Mo...
By Ye Yuan, Rui Song, Weien Li, Zeyu Li, Haochen Liu, Xiangyu Kong, Changjiang Han, Yonghan Yang, Zichen Zhao, Zixuan Dong, Fuyuan Lyu, Bowei He, Haolun Wu, Jikun Kang, Xue Liu
DREAM (Dynamic Resolution Assignment For Multimodal Multi-Agent Debate) enhances multimodal multi-agent debate by dynamically assigning each agent the optimal visual resolution through a zero-shot probe round that measures uncertainty with Average Normalized Log-Likelihood. It also introduces Uncertainty-Guided Rollback Aggregation to mitigate groupthink by tracking and restoring low-uncertainty answers that are overridden by group pressure. Across six multimodal datasets, DREAM improves the accuracy-token trade-off by 1.5–3.2% over existing baselines without requiring dataset-specific tuning.
By Khanh-Binh Nguyen, Van Dai Do, Tien Anh Nguyen, Svetha Venkatesh, Hung Le
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:2601. 02854v2 Announce Type: replace Abstract: As an agent-level reasoning and coordination paradigm, Multi-Agent Debate (MAD) orchestrates multiple agents through structured debate to improve answer quality and support complex reasoning.
By Ao Li, Jinghui Zhang, Luyu Li, Yuxiang Duan, Lang Gao, Mingcai Chen, Weijun Qin, Shaopeng Li, Fengxian Ji, Ning Liu, Lizhen Cui, Xiuying Chen, Yuntao Du
arXiv:2608. 12877v1 Announce Type: new Abstract: Multi-hop fact verification, which verifies claims by reasoning over multiple pieces of evidence, is critical for combating misinformation on social media yet remains highly challenging.
By Runze Zhao, Zixin Tang, Xiaoshuai Hao, Leyuan Chang, Xiaopeng Fu, Boyu Qiao, Dongyang Zhang
arXiv:2604. 11741v2 Announce Type: replace Abstract: Vision-language models (VLMs) have shown impressive capabilities in perceptual tasks, yet they degrade in complex multi-hop reasoning under multiplayer game settings with imperfect and deceptive information.
By Keyang Zhong, Junlin Xie, Hefeng Wu, Haofeng Li, Guanbin Li