BuildArena: A Physics-Aligned Interactive Benchmark of LLMs for Engineering Construction
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Abstract (arXiv)
Engineering construction automation aims to transform natural language specifications into physically viable structures, requiring complex integrated reasoning under strict physical constraints. While modern LLMs possess broad knowledge and strong reasoning capabilities that make them promising candidates for this domain, their construction competencies remain largely unevaluated. To address this gap, we introduce BuildArena, the first physics-aligned interactive benchmark designed for language-driven engineering construction. Technically, it contributes to the community in two aspects: (1) an extendable task design strategy spanning static and dynamic mechanics across multiple difficulty tiers; (2) a 3D Spatial Geometric Computation Library for supporting construction based on language instructions. On nine frontier LLMs and three additional open-weight models, BuildArena comprehensively evaluates their capabilities for language-driven and physics-grounded construction automation. We release the code at https://github.com/AI4Science-WestlakeU/BuildArena to benefit construction automation in engineering applications.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 30 of its 85 references that are in the map. First posted on arXiv 18 Oct 2025.
Builds on
30 of 85 references.
References · 30
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18 more
- 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
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- 2022PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Changecited by 382
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Built on by
Citing papers per year: 2025: 1; 2026: 8.
9 papers.
- 2026Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimizationcited by 16
- 2026Planning with Language and Generative Models: Toward General Reward-Guided Wireless Network Designcited by 2
- 2025Towards an AI Fluid Scientist: LLM-Powered Scientific Discovery in Experimental Fluid Mechanicscited by 1
- 2026VEHBench: A Stage-Local Diagnostic Benchmark for LLM-Assisted Vibration Energy Harvester Designcited by 0
- 2026WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modelingcited by 0
- 2026PolyBridgeBench: Benchmarking Multimodal LLMs for Physics-Grounded Bridge Designcited by 0
- 2026EngIntervene: Benchmarking Multimodal Engineering State Understanding and Design Intervention Reasoningcited by 0
- 2026PLCWorld: Benchmarking LLM-Generated PLC Programs in Closed-Loop Plant Simulationcited by 0
- 2026Words to World: Benchmarking LLMs in Interactive Physical Environmentscited by 0
- 2026Words to World: Benchmarking LLMs in Interactive Physical Environments10 Oct 2026
- 2026PLCWorld: Benchmarking LLM-Generated PLC Programs in Closed-Loop Plant Simulation2 Oct 2026
- 2026EngIntervene: Benchmarking Multimodal Engineering State Understanding and Design Intervention Reasoning27 Sep 2026
- 2026PolyBridgeBench: Benchmarking Multimodal LLMs for Physics-Grounded Bridge Design18 Sep 2026
- 2026VEHBench: A Stage-Local Diagnostic Benchmark for LLM-Assisted Vibration Energy Harvester Design20 Jul 2026
- 2026WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling2 Jun 2026
- 2026Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization14 Apr 2026
- 2026Planning with Language and Generative Models: Toward General Reward-Guided Wireless Network Design30 Jan 2026
- 2025Towards an AI Fluid Scientist: LLM-Powered Scientific Discovery in Experimental Fluid Mechanics4 Dec 2025
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- 2025EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving
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