MineExplorer：评估MLLM智能体在Minecraft中的开放世界探索能力

Computer Science > Computation and Language arXiv:2605.30931 (cs) [Submitted on 29 May 2026 (v1), last revised 4 Sep 2026 (this version, v4)] Title:MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft Authors:Tianjie Ju, Yueqing Sun, Zheng Wu, Wei Zhang, Yaqi Huo, Xi Su, Qi Gu, Xunliang Cai, Gongshen Liu, Zhuosheng Zhang View a PDF of the paper titled MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft, by Tianjie Ju and 9 other authors View PDF HTML (experimental) Abstract:Multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and action generation. However, their ability to sustain exploration in dynamic open worlds remains unclear. Existing embodied and game-based benchmarks often compress interaction into short-horizon tasks or entangle success with domain-specific game mechanics. In this paper, we introduce MineExplorer benchmark for evaluating open-world exploration capabilities of MLLM agents in Minecraft. We first filter atomic tasks whose solutions rely heavily on Minecraft-specific knowledge to better reflect general open-world reasoning. Then we organize the benchmark around a ReAct-style capability formulation and compose atomic tasks into implicit multi-hop tasks. To further construct reliable instances, MineExplorer uses a multi-agent synthesis workflow that jointly designs task graphs, sandbox scenes, and rule-based milestone evaluators. Human evaluation shows that the multi-agent synthesis workflow produces significantly more reliable instances than a single-agent baseline. Experiments with advanced MLLM agents show that open-world exploration remains challenging, as strong models can handle many single-hop tasks but degrade sharply when hidden prerequisites must be coordinated over longer trajectories. Further analysis finds that task difficulty tracks agent completion, and larger models or thinking modes do not consistently translate into better performance. Code and dataset are available at this https URL. Comments: Subjects: Computation and Language (cs.CL) Cite as: arXiv:2605.30931 [cs.CL] (or arXiv:2605.30931v4 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2605.30931 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Tianjie Ju [view email] [v1] Fri, 29 May 2026 07:21:47 UTC (30,413 KB) [v2] Fri, 12 Jun 2026 03:58:07 UTC (30,424 KB) [v3] Thu, 27 Aug 2026 14:41:02 UTC (30,424 KB) [v4] Fri, 4 Sep 2026 14:55:50 UTC (30,424 KB) Full-text links: Access Paper: View a PDF of the paper titled MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft, by Tianjie Ju and 9 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)