用拓扑优化构建材料分布推理数据集，评测大模型空间物理推理能力。
SPhyR: Spatial-Physical Reasoning Benchmark on Material Distribution
- 基于拓扑优化生成2D结构材料分布任务，模拟受力与支撑条件。
- 模型需在无仿真工具下推断力流路径与最优材料布局。
- 适合评估大模型对结构稳定性与空间组织的理解能力。
我们提出一个新型数据集，用于评估大语言模型（LLM）在拓扑优化框架下的物理与空间推理能力。该数据集基于在给定载荷和支撑条件下计算最优材料分布的方法，要求模型根据2D边界、施加力和支撑条件，推断出合理的材料分布。任务涵盖从部分结构中补全遮蔽区域到完整预测材料分布等多种形式。解决这些任务需要理解力的传递路径与约束下的材料需求，且不依赖仿真工具或显式物理模型，从而挑战模型对结构稳定性和空间组织的推理能力。该数据集聚焦于二维场景中的空间与物理推理评估，为传统语言和逻辑基准提供了补充视角。
原文摘要 · Abstract (English)
We introduce a novel dataset designed to benchmark the physical and spatial reasoning capabilities of Large Language Models (LLM) based on topology optimization, a method for computing optimal material distributions within a design space under prescribed loads and supports. In this dataset, LLMs are provided with conditions such as 2D boundary, applied forces and supports, and must reason about the resulting optimal material distribution. The dataset includes a variety of tasks, ranging from filling in masked regions within partial structures to predicting complete material distributions. Solving these tasks requires understanding the flow of forces and the required material distribution under given constraints, without access to simulation tools or explicit physical models, challenging models to reason about structural stability and spatial organization. Our dataset targets the evaluation of spatial and physical reasoning abilities in 2D settings, offering a complementary perspective to traditional language and logic benchmarks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。