@inproceedings{liu-etal-2025-demac,
    title = "{D}e{MAC}: Enhancing Multi-Agent Coordination with Dynamic {DAG} and Manager-Player Feedback",
    author = "Liu, Yuhan  and
      Xu, Cong  and
      Liu, Lu  and
      Wang, Yihua  and
      Chen, Feiyu  and
      Jia, Qi  and
      Zhao, Yaqian  and
      Wang, Zhichun  and
      Li, Xiang",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.757/",
    doi = "10.18653/v1/2025.findings-emnlp.757",
    pages = "14072--14098",
    ISBN = "979-8-89176-335-7",
    abstract = "Multi-agent systems (MAS) powered by large language models (LLMs) have shown potential in tackling multifaceted problems through advanced understanding and reasoning. However, they struggle to adapt to evolving task dependencies and to handle uncertainties, such as shifting priorities or unpredictable disruptions. These constraints undermine their ability to dynamically adjust long-term strategies and inter-agent collaboration. To address these challenges, we propose DeMAC, a Dynamic Environment-Aware Manager-Player Agents Coordination framework that enhances multi-agent coordination through long-term strategic planning. DeMAC uses a dynamically updated directed acyclic graph (DAG) and a Manager-Player Dual-Feedback mechanism to align strategic and operational decisions. Moreover, DeMAC enables agents to maintain collaboration and dynamically adapt to changing environmental conditions, outperforming traditional reinforcement learning and human-agent collaboration in the Overcooked simulation. Experimental results highlight DeMAC{'}s ability to tackle complex coordination tasks, demonstrating its potential to advance LLM-based MAS in dynamic, complex task dependency environments."
}