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This study introduces MineCEraft, an open-source benchmark using Minecraft to rigorously evaluate Large Language Models on complex construction engineering tasks. By analyzing 723 hand-crafted instructions across 17 categories, the research identifies specific failure modes and reliability limits when applying LLMs to procedural building tasks. The work provides a controlled environment for understanding the practical constraints of current models in executing multi-step logical operations within a simulated physical world.
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MineCEraft: Evaluating Language Models as Construction Engineers in the World of Minecraft

arXiv1 min read

arXiv:2608.28884v1 Announce Type: new 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.

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.

Artificial Intelligence (cs.AI)

arXiv:2608.28884 [cs.AI]

(or arXiv:2608.28884v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.28884 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 188 words

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