AI RESEARCH
Parallelized Planning-Acting for Efficient LLM-based Multi-Agent Systems in Minecraft
arXiv CS.AI
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ArXi:2503.03505v2 Announce Type: replace Recent advancements in Large Language Model~(LLM)-based Multi-Agent Systems (MAS) have nstrated 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.