arXiv:2605.29790v2 Announce Type: replace-cross
Abstract: LLM-based multi-agent systems (MAS) have emerged as an effective paradigm for complex and long-horizon tasks. However, in real-world tasks, M...
By Zhezheng Hao, Tianfu Wang, Huanshuo Dong, Ziyan Liu, Hong Wang, Xiankun Lin, Qiang Lin, Can Wang, Hande Dong, Jiawei Chen
arXiv:2604. 08377v2 Announce Type: replace Abstract: Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment.
By Ziyu Ma, Shidong Yang, Yuxiang Ji, Xucong Wang, Yong Wang, Yiming Hu, Tongwen Huang, Xiangxiang Chu
arXiv:2610.33539v1 Announce Type: new
Abstract: Large language model (LLM)-based multi-agent systems have demonstrated remarkable capabilities across diverse tasks. Yet their memory and collaboration...
By Xufei Shi, Derong Xu, Wei Chen, Yanyu Yao, Zhi Zheng, Tong Xu
The paper introduces Goal-Aware Dynamic Multi-Agent Collaboration (GAD), a framework that assigns distinct duties to separate agents with individualized contexts to tackle long-horizon tasks. It employs a growing execution graph to structure subtasks and their dependencies, ensuring each agent retains the original instruction, task goal, and acceptance conditions. GAD dynamically adapts collaboration and information sharing, using verification and recovery checks to monitor subtask results and overall task completion, providing feedback for repair when checks fail.
By Xu Shen, Lingyong Yan, Shuaiqiang Wang, Jianmin Wu, Zhikai Chen, Jiliang Tang, Jie Ren
MOSAIC is an open‑source platform that allows agents from different decision‑making paradigms—such as reinforcement learning policies, large language models, vision‑language models, and human operators—to operate together in shared reinforcement learning environments. It achieves this through an IPC‑based worker protocol that isolates each agent’s training and inference logic, an operator abstraction that maps any agent to a minimal universal interface, and a deterministic evaluation framework offering both manual lock‑step and automated script modes for reproducible experiments.
By Abdulhamid M. Mousa, Jinhui Pang, Rakhmonberdi Khajiev, Jalaledin M. Azzabi, Abdulkarim M. Mousa, Peng Yong, Yunusa Haruna, Ming Liu
arXiv:2511. 02200v2 Announce Type: replace Abstract: The emergence of multi-agent systems powered by large language models (LLMs) has unlocked new frontiers in complex task-solving, enabling diverse agents to integrate unique expertise, collaborate flexibly, and address challenges unattainable for individual models.
By Jingbo Wang, Sendong Zhao, Haochun Wang, Yuzheng Fan, Ting Liu