A LLM Benchmark based on the Minecraft Builder Dialog Agent Task
On the map: LLM agents and tool use → Reinforcement learning for LLM agents
Abstract (arXiv)
In this work we proposing adapting the Minecraft builder task into an LLM benchmark suitable for evaluating LLM ability in spatially orientated tasks, and informing builder agent design. Previous works have proposed corpora with varying complex structures, and human written instructions. We instead attempt to provide a comprehensive synthetic benchmark for testing builder agents over a series of distinct tasks that comprise of common building operations. We believe this approach allows us to probe specific strengths and weaknesses of different agents, and test the ability of LLMs in the challenging area of spatial reasoning and vector based math.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 21 references that are in the map. First posted on arXiv 17 Jul 2024.
Builds on
14 of 21 references.
Closest ancestors · 2
Same direction, newest first
- 2024Large Language Models as Minecraft Agents13 Feb
- 2023SmartPlay: A Benchmark for LLMs as Intelligent Agents2 Oct
Other references · 12
- 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
- 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
- 2015Towards AI-Complete Question Answering: A Set of Prerequisite Toy Taskscited by 309
- 2019Learning to Speak and Act in a Fantasy Text Adventure Gamecited by 122
- 2022StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Textscited by 65
- 2022Dungeons and Dragons as a Dialog Challenge for Artificial Intelligencecited by 35
- 2022Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoningcited by 32
- 2019CraftAssist: A Framework for Dialogue-enabled Interactive Agentscited by 27
- 2022Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021cited by 23
- 2019Why Build an Assistant in Minecraft?cited by 14
- 2022Collecting Interactive Multi-modal Datasets for Grounded Language Understandingcited by 10
- 2024Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraftcited by 4
Built on by
Citing papers per year: 2024: 2; 2025: 2.
4 papers.
- 2024GameArena: Evaluating LLM Reasoning through Live Computer Gamescited by 32
- 2025MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluationcited by 15
- 2024Reflection-Bench: Evaluating Epistemic Agency in Large Language Modelscited by 7
- 2025Referential ambiguity and clarification requests: comparing human and LLM behaviourcited by 5
- 2025Referential ambiguity and clarification requests: comparing human and LLM behaviour14 Jul 2025
- 2025MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation21 May 2025
- 2024GameArena: Evaluating LLM Reasoning through Live Computer Games9 Dec 2024
- 2024Reflection-Bench: Evaluating Epistemic Agency in Large Language Models21 Oct 2024
Similar, not cited
Close in wording, no citation either way
- 2024APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World Agents
- 2025MineAnyBuild: Benchmarking Spatial Planning for Open-world AI Agents
- 2024Plancraft: an evaluation dataset for planning with LLM agents
- 2025BAP v2: An Enhanced Task Framework for Instruction Following in Minecraft Dialogues
- 2025GVGAI-LLM: Evaluating Large Language Model Agents with Infinite Games
- 2023AgentBench: Evaluating LLMs as Agents
- 2024AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents
- 2026Spatial Reasoning in LLM Game Agents: Impact of Causal Context and Multi-Step Planning