Papers with Minecraft
World Models with Hints of Large Language Models for Goal Achieving (2025.naacl-long)
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| Challenge: | Existing methods address this by adding intrinsic rewards, but they fail to provide meaningful guidance in long-horizon decision-making tasks with large state and action spaces lacking purposeful exploration. |
| Approach: | They propose a multi-modal model-based RL approach that integrates the proposed hinting subgoals into the model rollouts to encourage goal discovery and reaching in challenging tasks. |
| Outcome: | The proposed model outperforms existing methods in challenging, sparse-reward environments such as HomeGrid, Crafter, and Minecraft by 41.8%, 21.1%, and 9.9%. |
MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks (2021.emnlp-main)
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| Challenge: | Creating embodied, situated agents able to move in, communicate naturally about, and collaborate on human terms in the physical world has been a persisting goal in artificial intelligence (Winograd, 1972). |
| Approach: | They propose to use a 3D Minecraft dataset to model the beliefs of human partners in situ to enable theory of mind modeling in situated interactions. |
| Outcome: | The proposed model can be used to model human collaborative behaviors in the 3D virtual blocks world of Minecraft. |
GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks (2026.acl-long)
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Jianwen Luo, Yiming Huang, Jinxiang Meng, Fangyu Lei, Shizhu He, Xiao Liu, Shanshan Jiang, Bin Dong, Jun Zhao, Kang Liu
| Challenge: | Existing toolsets that use large language models are limited to single-task settings. |
| Approach: | They propose a framework that dynamically constructs and evolves a hierarchical graph of reusable tools across multiple scenarios. |
| Outcome: | The proposed framework achieves up to 4.3 faster milestone completion in Minecraft compared to the previous state-of-the-art method and provides an average improvement of 9.23% over existing tool-making methods in code generation tasks and 10.03% in agent tasks. |
Learning to Execute Actions or Ask Clarification Questions (2022.findings-naacl)
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| Challenge: | Existing work on Minecraft Corpus Dataset only learns to execute instructions neglecting the importance of asking for clarifications. |
| Approach: | They propose to annotate all builder utterances into eight types, including clarification questions, and propose a builder agent model capable of determining when to ask or execute instructions. |
| Outcome: | The proposed model outperforms existing models on the collaborative building task with a substantial improvement. |
Generating Instructions at Different Levels of Abstraction (2020.coling-main)
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| Challenge: | Using hierarchical planning, technical instructions can be described at different levels of abstraction. |
| Approach: | They propose a method from AI planning which can capture the structure of complex objects neatly. |
| Outcome: | The proposed method can capture the structure of complex objects neatly. |
LLaMA-Rider: Spurring Large Language Models to Explore the Open World (2024.findings-naacl)
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| Challenge: | Recent studies have used Large Language Models to help decision-making and planning in environments, but their capacity to acquire environmental knowledge and adapt in an open world remains uncertain. |
| Approach: | They propose an approach to spur LLMs to explore the open world, gather experiences, and learn to improve their task-solving capabilities by using a feedback-revision mechanism. |
| Outcome: | The proposed model enhances the efficiency of the LLM in exploring the open world and improves its ability to accomplish more tasks through fine-tuning with merely 1.3k instances of collected data, showing minimal training costs compared to baseline using reinforcement learning. |
BAR: A Backward Reasoning based Agent for Complex Minecraft Tasks (2025.findings-acl)
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| Challenge: | Existing studies focus on forward reasoning based planning, but this paradigm doesn't work well for complex tasks. |
| Approach: | They propose to decompose a task into easily executed steps by planning and use a backward reasoning based agent to make the planning starting from the terminal state. |
| Outcome: | The proposed model outperforms existing methods and the proposed modules in a virtual environment that simulates complex tasks based on real-world scenarios. |
Analysis of Dialogue in Human-Human Collaboration in Minecraft (2022.lrec-1)
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| Challenge: | Recent studies have focused on developing dialogue systems that enable collaborative work, but few studies have centered on creative collaborative work. |
| Approach: | They collected 500 dialogues of human-human collaboration in Minecraft as a basis for developing a dialogue system that enables creative collaborative work. |
| Outcome: | The proposed system can be used to create a collaborative garden in Minecraft and collect text chats, action logs, and subjective evaluations. |
Spatial AMR: Expanded Spatial Annotation in the Context of a Grounded Minecraft Corpus (2020.lrec-1)
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| Challenge: | Existing annotation tools for spatial relations capture fine-grained semantics and pragmatics derived from spatial information. |
| Approach: | They propose an extension to the Abstract Meaning Representation annotation schema that captures fine-grained spatial information in grounded corpora. |
| Outcome: | The proposed tool can handle fine-grained spatial relationships grounded in quantized space. |
CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks (2025.findings-emnlp)
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| Challenge: | Existing work on multi-agent collaborative tasks in Minecraft is limited due to inefficiency and limited fault tolerance. |
| Approach: | They propose a framework that incorporates causality to manage dependencies among subtasks. |
| Outcome: | The proposed framework achieves state-of-the-art performance in multi-agent cooperative tasks of Minecraft. |
JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse (2025.findings-acl)
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| Challenge: | Visual Language Action models have shown promise in decision-making tasks, but have been neglected in previous work . |
| Approach: | They propose a new paradigm for visual language action models that enhances the foundation model prior to action-specific tuning by first post-training it on a curated set of visual and linguistic tasks using self-supervised learning. |
| Outcome: | The proposed model outperforms the best agent baseline on a diverse set of atomic tasks and surpasses imitation learning-based policies in Minecraft. |
MoEC: A Memory-Routed Mixture-of-Experts Controller for Adaptive Minecraft Control (2026.acl-long)
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| Challenge: | Existing systems rely on a monolithic policy to execute subgoals across varying contexts, causing inconsistent outcomes and scaling only partially mitigates. |
| Approach: | They propose a memory-routed mixtureof-experts controller for Adaptive Minecraft Control that routes via a subgoal-indexed expert memory and regulates capacity through failure-triggered expert growth and redundancy-aware consolidation. |
| Outcome: | The proposed controller shows significant gains in adaptability, robustness, and execution consistency over strong baselines. |
Metagent-P: A Neuro-Symbolic Planning Agent with Metacognition for Open Worlds (2025.findings-acl)
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YanfangZhou YanfangZhou, Yuntao Liu, Xiaodong Li, Yongqiang Zhao, Xintong Wang, Jinlong Tian, Zhenyu Li, Xinhai Xu
| Challenge: | Recent advances in large language models (LLMs) show promising potential through their world knowledge and language processing capabilities in open-world planning. |
| Approach: | They propose a framework that integrates the world knowledge of large language models, symbolic reasoning capabilities of cognitive architectures, and metacognition to improve experience utilization. |
| Outcome: | The proposed framework outperforms current state-of-the-art methods in Minecraft and reduces the average replanning counts by 34% and exceeds the human success rate by 18.96%. |