@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T ParaCook: On Time-Efficient Planning for Multi-Agent Systems
%A Zhang, Shiqi
%A Ma, Xinbei
%A Xu, Yunqing
%A Cao, Zouying
%A Lu, Pengrui
%A Yuan, Haobo
%A Shen, Tiancheng
%A Zhang, Zhuosheng
%A Zhao, Hai
%A Yang, Ming-Hsuan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-paracook
%X 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.
%U https://aclanthology.org/2026.findings-acl.1378/
%P 27683-27701
Markdown (Informal)
[ParaCook: On Time-Efficient Planning for Multi-Agent Systems](https://aclanthology.org/2026.findings-acl.1378/) (Zhang et al., Findings 2026)
ACL
- Shiqi Zhang, Xinbei Ma, Yunqing Xu, Zouying Cao, Pengrui Lu, Haobo Yuan, Tiancheng Shen, Zhuosheng Zhang, Hai Zhao, and Ming-Hsuan Yang. 2026. ParaCook: On Time-Efficient Planning for Multi-Agent Systems. In Findings of the Association for Computational Linguistics: ACL 2026, pages 27683–27701, San Diego, California, United States. Association for Computational Linguistics.