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

Yaoru Li, Shunyu Liu, Tongya Zheng, Li Sun, Mingli Song

Keywords: Multi-Agent Systems, Large Language Models

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.

Area: Generative and Agentic AI (GAAI)

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Submission Number: 90