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. 23532v1 Announce Type: cross Abstract: Swarms of LLM-assisted autonomous robots are increasingly proposed for cooperative intelligence, surveillance, and reconnaissance (ISR) in contested environments.
By Nikolaos Kekatos, Stylianos Basagiannis, Panagiotis Katsaros, Alexios Lekidis, Tom Nianios
The paper introduces TalkMesh, a decentralized network of small language model agents that learn to communicate effectively during inference. Each agent proposes an answer, scores it with a confidence head, and the most confident agent broadcasts a hint; lower‑confidence agents revise their proposals if a new suggestion scores higher. This gossip‑based consensus, trained via group relative policy optimization, enables a mesh of three agents to match the accuracy of majority voting over 32 samples, and scales to larger meshes to significantly boost performance on benchmarks like GSM8K and MATH-500.
By Mehmet Kerem Turkcan
The paper introduces Degrees of Freedom (DoF), a continuous framework that measures how much decision-making is delegated to agents in large language model–based multi‑agent systems. By varying DoF, the authors explore a spectrum between fully centralized and fully decentralized coordination, finding that intermediate settings often outperform both extremes across several task domains. The study shows that task characteristics and retained experience influence how coordination granularity affects performance, offering empirical guidance for designing hybrid coordination regimes.
By Yiqing Peng, Hehai Lin, Zixuan Wang, Dongyi Zhang, Zhengjie Zhu, Yi Han, Yangyang Zheng, Hao Wang, Qian Li, Chengwei Qin
arXiv:2606. 31174v1 Announce Type: new Abstract: Production large language-model (LLM) agents are increasingly deployed not as lone problem-solvers but as managers: a main model creates specialized subagents, delegates work, and orchestrates their parallel, asynchronous returns through dynamic workflows.
By Kaiwen Xiong, Haonian Ji, Shi Qiu, Zeyu Zheng, Cihang Xie, Xinyu Ye, Huaxiu Yao
The study evaluates two hierarchical red team agent architectures—RL planner with RL executor (RL+RL) and LLM planner with LLM executor (LLM+LLM)—across multiple cyber environments. In compact, densely rewarded settings, RL+RL outperforms LLM+LLM, while in larger, escalation‑gated networks a pretrained LLM agent dominates. The results reveal environment‑dependent performance inversions tied to architecture‑specific bottlenecks, challenging the notion that one design is universally superior.
By Ayan Javeed Shaikh, Arunesh Sinha, Nathaniel D. Bastian, Ankit Shah