SPhyR：材料分布空间物理推理基准测试

SPhyR: Spatial-Physical Reasoning Benchmark on Material Distribution

May 21, 2025

作者: Philipp D. Siedler

cs.AI

摘要

我们引入了一个新颖的数据集，旨在基于拓扑优化方法评估大型语言模型（LLM）的物理与空间推理能力。拓扑优化是一种在设计空间内，根据给定载荷和支撑条件计算最优材料分布的方法。在该数据集中，LLM被提供诸如二维边界、施加的力及支撑等条件，并需推理出相应的最优材料分布。数据集包含多种任务，从填补部分结构中的掩码区域到预测完整的材料分布不等。解决这些任务需要理解在给定约束下力的流动及所需材料分布，而无需借助仿真工具或显式的物理模型，从而挑战模型对结构稳定性和空间组织的推理能力。我们的数据集专注于二维环境下的空间与物理推理能力评估，为传统的语言与逻辑基准提供了补充视角。

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.