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arXiv · 2508.18797

CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks

Qi Chai·Zhang Zheng·Junlong Ren·Deheng Ye·Zichuan Lin·Hao Wang
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Abstract

Minecraft, as an open-world virtual interactive environment, has become a prominent platform for research on agent decision-making and execution. Existing works primarily adopt a single Large Language Model (LLM) agent to complete various in-game tasks. However, for complex tasks requiring lengthy sequences of actions, single-agent approaches often face challenges related to inefficiency and limited fault tolerance. Despite these issues, research on multi-agent collaboration remains scarce. In this paper, we propose CausalMACE, a holistic causality planning framework designed to enhance multi-agent systems, in which we incorporate causality to manage dependencies among subtasks. Technically, our proposed framework introduces two modules: an overarching task graph for global task planning and a causality-based module for dependency management, where inherent rules are adopted to perform causal intervention. Experimental results demonstrate our approach achieves state-of-the-art performance in multi-agent cooperative tasks of Minecraft.

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Qi Chai, Zhang Zheng, Junlong Ren, Deheng Ye, Zichuan Lin, Hao Wang. 2025-08-26. CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks. https://arxiv.org/abs/2508.18797

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