MineCEraft: Evaluating Language Models as Construction Engineers in the World of Minecraft

Sewoong Lee; Risham Sidhu; Julia Hockenmaier; Yoonhwa Jung

MineCEraft: Evaluating Language Models as Construction Engineers in the World of Minecraft

Sewoong Lee, Risham Sidhu, Julia Hockenmaier, Yoonhwa Jung

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Abstract

We introduce MineCEraft (Minecraft Construction Engineering Benchmark, pronounced mine-see-ee-raft), an easy-to-use, open-source benchmark designed to systematically evaluate the reliability and limitations of LLMs for construction tasks in Minecraft. The MineCEraft benchmark comprises 723 domain-expert hand-crafted natural-language instructions with programmatically verifiable evaluation, spanning 17 distinct task categories, providing a safe and controllable experimental environment for assessing LLMs’ ability to perform realistic construction engineering tasks. With this benchmark, we conduct an in-depth evaluation of state-of-the-art LLMs and perform a detailed error analysis, revealing key failure modes and practical challenges in applying LLMs to construction engineering tasks.

Anthology ID:: 2026.findings-emnlp.130
Volume:: Findings of the Association for Computational Linguistics: EMNLP 2026
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:: Findings
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 2643–2667
Language:
URL:: https://aclanthology.org/2026.findings-emnlp.130/
DOI:
Bibkey:
Cite (ACL):: Sewoong Lee, Risham Sidhu, Julia Hockenmaier, and Yoonhwa Jung. 2026. MineCEraft: Evaluating Language Models as Construction Engineers in the World of Minecraft. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 2643–2667, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: MineCEraft: Evaluating Language Models as Construction Engineers in the World of Minecraft (Lee et al., Findings 2026)
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PDF:: https://aclanthology.org/2026.findings-emnlp.130.pdf

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Endnote
Preformatted

@inproceedings{lee-etal-2026-mineceraft,
    title = "{M}ine{CE}raft: Evaluating Language Models as Construction Engineers in the World of {M}inecraft",
    author = "Lee, Sewoong  and
      Sidhu, Risham  and
      Hockenmaier, Julia  and
      Jung, Yoonhwa",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-emnlp.130/",
    pages = "2643--2667",
    ISBN = "979-8-89176-462-0",
    abstract = "We introduce MineCEraft (Minecraft Construction Engineering Benchmark, pronounced mine-see-ee-raft), an easy-to-use, open-source benchmark designed to systematically evaluate the reliability and limitations of LLMs for construction tasks in Minecraft. The MineCEraft benchmark comprises 723 domain-expert hand-crafted natural-language instructions with programmatically verifiable evaluation, spanning 17 distinct task categories, providing a safe and controllable experimental environment for assessing LLMs' ability to perform realistic construction engineering tasks. With this benchmark, we conduct an in-depth evaluation of state-of-the-art LLMs and perform a detailed error analysis, revealing key failure modes and practical challenges in applying LLMs to construction engineering tasks."
}

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%0 Conference Proceedings
%T MineCEraft: Evaluating Language Models as Construction Engineers in the World of Minecraft
%A Lee, Sewoong
%A Sidhu, Risham
%A Hockenmaier, Julia
%A Jung, Yoonhwa
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lee-etal-2026-mineceraft
%X We introduce MineCEraft (Minecraft Construction Engineering Benchmark, pronounced mine-see-ee-raft), an easy-to-use, open-source benchmark designed to systematically evaluate the reliability and limitations of LLMs for construction tasks in Minecraft. The MineCEraft benchmark comprises 723 domain-expert hand-crafted natural-language instructions with programmatically verifiable evaluation, spanning 17 distinct task categories, providing a safe and controllable experimental environment for assessing LLMs’ ability to perform realistic construction engineering tasks. With this benchmark, we conduct an in-depth evaluation of state-of-the-art LLMs and perform a detailed error analysis, revealing key failure modes and practical challenges in applying LLMs to construction engineering tasks.
%U https://aclanthology.org/2026.findings-emnlp.130/
%P 2643-2667

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Markdown (Informal)

[MineCEraft: Evaluating Language Models as Construction Engineers in the World of Minecraft](https://aclanthology.org/2026.findings-emnlp.130/) (Lee et al., Findings 2026)

MineCEraft: Evaluating Language Models as Construction Engineers in the World of Minecraft (Lee et al., Findings 2026)

ACL

Sewoong Lee, Risham Sidhu, Julia Hockenmaier, and Yoonhwa Jung. 2026. MineCEraft: Evaluating Language Models as Construction Engineers in the World of Minecraft. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 2643–2667, Budapest, Hungary. Association for Computational Linguistics.