ParaCook: On Time-Efficient Planning for Multi-Agent Systems

• URL: http://arxiv.org/abs/2510.11608v1
• Date: Mon, 13 Oct 2025 16:47:07 GMT
• Title: ParaCook: On Time-Efficient Planning for Multi-Agent Systems
• Authors: Shiqi Zhang, Xinbei Ma, Yunqing Xu, Zouying Cao, Pengrui Lu, Haobo Yuan, Tiancheng Shen, Zhuosheng Zhang, Hai Zhao, Ming-Hsuan Yang,
• Abstract summary: Large Language Models (LLMs) exhibit strong reasoning abilities for planning long-horizon, real-world tasks.<n>We present ParaCook, a benchmark for time-efficient collaborative planning.
• Score: 62.471032881396496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. The code and data are available at https://github.com/zsq259/ParaCook.

Related papers

• Learning to Share: Selective Memory for Efficient Parallel Agentic Systems [49.78267008828593]
  Agentic systems solve complex tasks by coordinating multiple agents that iteratively reason, invoke tools, and exchange intermediate results.<n>Recent approaches deploy multiple agent teams running in parallel to explore diverse reasoning trajectories.<n>We propose Learning to Share (LTS), a learned shared-memory mechanism for parallel agentic frameworks.
  arXiv  Detail & Related papers  (2026-02-05T18:20:21Z)
• Benchmarking LLMs' Swarm intelligence [50.544186914115045]
  Large Language Models (LLMs) show potential for complex reasoning, yet their capacity for emergent coordination in Multi-Agent Systems (MAS) remains largely unexplored.<n>We introduce SwarmBench, a novel benchmark designed to systematically evaluate tasks of LLMs acting as decentralized agents.<n>We propose metrics for coordination effectiveness and analyze emergent group dynamics.
  arXiv  Detail & Related papers  (2025-05-07T12:32:01Z)
• Haste Makes Waste: Evaluating Planning Abilities of LLMs for Efficient and Feasible Multitasking with Time Constraints Between Actions [56.88110850242265]
  We present Recipe2Plan, a novel benchmark framework based on real-world cooking scenarios.<n>Unlike conventional benchmarks, Recipe2Plan challenges agents to optimize cooking time through parallel task execution.
  arXiv  Detail & Related papers  (2025-03-04T03:27:02Z)
• Plan-over-Graph: Towards Parallelable LLM Agent Schedule [53.834646147919436]
  Large Language Models (LLMs) have demonstrated exceptional abilities in reasoning for task planning.<n>This paper introduces a novel paradigm, plan-over-graph, in which the model first decomposes a real-life textual task into executable subtasks and constructs an abstract task graph.<n>The model then understands this task graph as input and generates a plan for parallel execution.
  arXiv  Detail & Related papers  (2025-02-20T13:47:51Z)
• Robotouille: An Asynchronous Planning Benchmark for LLM Agents [7.574421886354134]
  Asynchronous planning is essential for agents that must account for time delays, reason over diverse long-horizon tasks, and collaborate with other agents.<n>We introduce Robotouille, a benchmark environment designed to test agents' ability to handle long-horizon asynchronous scenarios.<n>Our results show that ReAct (gpt4-o) achieves 47% on synchronous tasks but only 11% on asynchronous tasks, highlighting significant room for improvement.
  arXiv  Detail & Related papers  (2025-02-06T05:50:37Z)
• ET-Plan-Bench: Embodied Task-level Planning Benchmark Towards Spatial-Temporal Cognition with Foundation Models [38.89166693142495]
  ET-Plan-Bench is a benchmark for embodied task planning using Large Language Models (LLMs)<n>It features a controllable and diverse set of embodied tasks varying in different levels of difficulties and complexities.<n>Our benchmark distinguishes itself as a large-scale, quantifiable, highly automated, and fine-grained diagnostic framework.
  arXiv  Detail & Related papers  (2024-10-02T19:56:38Z)
• Spatial Reasoning and Planning for Deep Embodied Agents [2.7195102129095003]
  This thesis explores the development of data-driven techniques for spatial reasoning and planning tasks. It focuses on enhancing learning efficiency, interpretability, and transferability across novel scenarios.
  arXiv  Detail & Related papers  (2024-09-28T23:05:56Z)
• Improving Planning with Large Language Models: A Modular Agentic Architecture [7.63815864256878]
  Large language models (LLMs) often struggle with tasks that require multi-step reasoning or goal-directed planning. We propose an agentic architecture, the Modular Agentic Planner (MAP), in which planning is accomplished via the recurrent interaction of specialized modules. We find that MAP yields significant improvements over both standard LLM methods.
  arXiv  Detail & Related papers  (2023-09-30T00:10:14Z)
• AI planning in the imagination: High-level planning on learned abstract search spaces [68.75684174531962]
  We propose a new method, called PiZero, that gives an agent the ability to plan in an abstract search space that the agent learns during training. We evaluate our method on multiple domains, including the traveling salesman problem, Sokoban, 2048, the facility location problem, and Pacman.
  arXiv  Detail & Related papers  (2023-08-16T22:47:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.