ParaCook: On Time-Efficient Planning for Multi-Agent Systems
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917275172339712 |
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| author | Zhang, Shiqi Ma, Xinbei Xu, Yunqing Cao, Zouying Lu, Pengrui Yuan, Haobo Shen, Tiancheng Zhang, Zhuosheng Zhao, Hai Yang, Ming-Hsuan |
| author_facet | Zhang, Shiqi Ma, Xinbei Xu, Yunqing Cao, Zouying Lu, Pengrui Yuan, Haobo Shen, Tiancheng Zhang, Zhuosheng Zhao, Hai Yang, Ming-Hsuan |
| contents | 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. The code and data are available at https://github.com/zsq259/ParaCook. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11608 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ParaCook: On Time-Efficient Planning for Multi-Agent Systems Zhang, Shiqi Ma, Xinbei Xu, Yunqing Cao, Zouying Lu, Pengrui Yuan, Haobo Shen, Tiancheng Zhang, Zhuosheng Zhao, Hai Yang, Ming-Hsuan Artificial Intelligence 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. The code and data are available at https://github.com/zsq259/ParaCook. |
| title | ParaCook: On Time-Efficient Planning for Multi-Agent Systems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.11608 |