C ausal MACE : Causality Empowered Multi-Agents in M inecraft Cooperative Tasks (2025)

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. The code will be open-sourced upon the acceptance of this paper.

## introduction in recent years, large language models (llms) have shown significant abilities in various domains @xcite @xcite @xcite . beyond these fundamental domains, interest has increased in how to utilize llms' capabilities to make decisions in an open-world environment. minecraft, as a virtual interactive environment, offers a unique platform for such research. various approaches @xcite have been developed within minecraft to explore the decision-making capabilities of llms, achieving significant progress on in-game tasks. ## conclusion we present a novel global causality planning framework that significantly enhances the capabilities of multi-agent systems in open-world environments. by leveraging causality to manage and construct dependencies among subtasks from a global view, our approach ensures more efficient task arrangements. this addresses the limitations of existing multi-agent methods and enhances the overall effectiveness of task execution. additionally, our findings suggest that incorporating causality into task planning can provide a more structured and efficient approach to managing complex tasks, leading to the possibility for further exploration.