@inproceedings{zhang-etal-2026-paracook,
    title = "{P}ara{C}ook: On Time-Efficient Planning for Multi-Agent Systems",
    author = "Zhang, Shiqi  and
      Ma, Xinbei  and
      Xu, Yunqing  and
      Cao, Zouying  and
      Lu, Pengrui  and
      Yuan, Haobo  and
      Shen, Tiancheng  and
      Zhang, Zhuosheng  and
      Zhao, Hai  and
      Yang, Ming-Hsuan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1378/",
    pages = "27683--27701",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) exhibit strong reasoning abilities for planning long-horizon, real-world tasks, yet existing agent benchmarks focus on task completion while neglecting time efficiency in parallel and asynchronous operations. To address this, we present ParaCook, a benchmark for time-efficient collaborative planning. Inspired by the Overcooked game, ParaCook provides an environment for various challenging interaction planning of multi-agent systems that are instantiated as cooking tasks, with a simplified action space to isolate the core challenge of strategic parallel planning. Through a comprehensive evaluation of state-of-the-art LLMs, we find that current approaches achieve suboptimal plans, which struggle with parallel actions or coordination. Our analysis also reveals LLMs' potential on abstract tasks where they can focus on high-level parallel optimization. ParaCook provides a scalable evaluation framework with adjustable complexity, establishing a foundation for developing and assessing time efficiency-aware multi-agent planning."
}