Gated Coordination for Efficient Multi-Agent Collaboration in Minecraft Game
On the map: LLM agents and tool use → Reinforcement learning for LLM agents
Abstract (arXiv)
In long-horizon open-world multi-agent systems, existing methods often treat local anomalies as automatic triggers for communication. This default design introduces coordination noise, interrupts local execution, and overuses public interaction in cases that could be resolved locally. To address this issue, we propose a partitioned information architecture for MLLM agents that explicitly separates private execution states from public coordination states. Building on this design, we introduce two key mechanisms. First, we develop an event-triggered working memory based on system-verified outcomes to maintain compact and low-noise local state representations. Second, we propose a cost-sensitive gated escalation mechanism that determines whether cross-region communication should be initiated by jointly considering node criticality, local recovery cost, and downstream task impact. In this way, communication is transformed from a default reaction into a selective decision. Experiments conducted on long-term construction tasks in open environments demonstrate that, compared to baseline models based on strong communication and planned structures, the introduction of gated communication and a partitioned information architecture results in superior performance in terms of blueprint completion quality and execution chain length. It also improves local self-recovery, reduces ineffective escalations, and increases the utility of public communication.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 20 of its 41 references that are in the map. First posted on arXiv 21 Apr 2026.
Builds on
20 of 41 references.
Closest ancestors · 8
Same direction, newest first
- 2025CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks26 Aug
- 2025Collaborating Action by Action: A Multi-agent LLM Framework for Embodied Reasoning24 Apr
- 2024ADAM: An Embodied Causal Agent in Open-World Environments29 Oct
- 2024OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents27 Jun
- 2024Hierarchical Auto-Organizing System for Open-Ended Multi-Agent Navigation13 Mar
- 2023See and Think: Embodied Agent in Virtual Environment26 Nov
- 2023JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models10 Nov
- 2022MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge17 Jun
Other references · 12
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
- 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 3,080
- 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,071
- 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,395
- 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,035
- 2023Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agentscited by 398
- 2023AutoAgents: A Framework for Automatic Agent Generationcited by 216
- 2023STEVE-1: A Generative Model for Text-to-Behavior in Minecraftcited by 97
- 2025MasRouter: Learning to Route LLMs for Multi-Agent Systemscited by 88
- 2023MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perceptioncited by 67
- 2025Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agentscited by 41
- 2024IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agentscited by 1
Built on by
Citing papers per year: 2026: 3.
3 papers.
- 2026Theory of Scene: Breaking the Symmetry Trap in Multi-Agent LLM Coordinationcited by 1
- 2026GenCoord: Executable Commitments for Embodied LLM Agents under Private Informationcited by 0
- 2026What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agentscited by 0
- 2026What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agents30 Sep 2026
- 2026Theory of Scene: Breaking the Symmetry Trap in Multi-Agent LLM Coordination26 Sep 2026
- 2026GenCoord: Executable Commitments for Embodied LLM Agents under Private Information22 Aug 2026
Similar, not cited
Close in wording, no citation either way
- 2026Multi-agent Framework for Time-Sensitive Complementary Collaboration in Minecraft
- 2025Parallelized Planning-Acting for Efficient LLM-based Multi-Agent Systems in Minecraft
- 2026MineNPC-Task: Task Suite for Memory-Aware Minecraft Agents
- 2026MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft
- 2026Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems
- 2025OpenHA: A Series of Open-Source Hierarchical Agentic Models in Minecraft
- 2026Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
- 2024TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraft