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arXiv · 2503.03505

Parallelized Planning-Acting for Efficient LLM-based Multi-Agent Systems in Minecraft

Yaoru Li·Shunyu Liu·Tongya Zheng·Li Sun·Mingli Song
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Abstract

Recent advancements in Large Language Model~(LLM)-based Multi-Agent Systems (MAS) have demonstrated remarkable potential for tackling complex decision-making tasks. However, existing frameworks inevitably rely on serialized execution paradigms, where agents must complete sequential LLM planning before taking action. This fundamental constraint severely limits real-time responsiveness and adaptation, which is crucial in dynamic environments with ever-changing scenarios like Minecraft. In this paper, we propose a novel parallelized planning-acting framework for LLM-based MAS, featuring a dual-thread architecture with interruptible execution to enable concurrent planning and acting. Specifically, our framework comprises two core threads: (1) a planning thread driven by a centralized memory system, maintaining synchronization of environmental states and agent communication to support dynamic decision-making; and (2) an acting thread equipped with a comprehensive skill library, enabling automated task execution through recursive decomposition. Extensive experiments on Minecraft demonstrate the effectiveness of the proposed framework.

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Yaoru Li, Shunyu Liu, Tongya Zheng, Li Sun, Mingli Song. 2025-03-05. Parallelized Planning-Acting for Efficient LLM-based Multi-Agent Systems in Minecraft. https://arxiv.org/abs/2503.03505

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