SPhyR：材料分佈的空間物理推理基準測試

SPhyR: Spatial-Physical Reasoning Benchmark on Material Distribution

May 21, 2025

作者: Philipp D. Siedler

cs.AI

摘要

我們引入了一個新穎的數據集，旨在基於拓撲優化方法來評估大型語言模型（LLM）的物理與空間推理能力。該方法用於在給定載荷和支撐條件下，計算設計空間內的最優材料分佈。在此數據集中，LLM會獲得如二維邊界、施加的力與支撐等條件，並需推理出相應的最優材料分佈。數據集包含多樣化的任務，從填充部分結構中的遮罩區域到預測完整的材料分佈不等。解決這些任務需要理解力的傳遞及在特定約束下所需的材料分佈，而無需借助仿真工具或顯式的物理模型，從而挑戰模型對結構穩定性與空間組織的推理能力。我們的數據集專注於二維環境下的空間與物理推理能力評估，為傳統語言與邏輯基準提供了一個互補的視角。

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.