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.

翻译：大型语言模型（LLM）在规划长周期现实任务中展现出强大的推理能力，然而现有智能体基准测试主要关注任务完成度，忽视了并行与异步操作中的时间效率问题。为此，我们提出了ParaCook——一个面向时间高效协同规划的基准测试框架。受《Overcooked》游戏启发，ParaCook为多智能体系统提供了多种具有挑战性的交互规划环境，这些系统被实例化为烹饪任务，并通过简化的动作空间以聚焦战略并行规划的核心挑战。通过对前沿LLM模型的综合评估，我们发现现有方法生成的规划方案均未达到最优，其在并行动作协调方面存在明显不足。分析同时表明，LLM在抽象任务中具备专注于高层并行优化的潜力。ParaCook提供了可调节复杂度的可扩展评估框架，为开发与评估具有时间效率意识的多智能体规划奠定了基准基础。代码与数据已开源：https://github.com/zsq259/ParaCook。