SPhyR: Spatial-Physical Reasoning Benchmark on Material Distribution
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.
68302100d260f25aad14b1c7
Philipp D. Siedler
extracted_confirmed
2025-05-23T07:26:08.014Z
62a1e17591f85abff79c2cdf
https://cdn-avatars.huggingface.co/v1/production/uploads/1654776114657-noauth.jpeg
Philipp Siedler
user
philippds
SPhyR: Spatial-Physical Reasoning Benchmark on Material Distribution
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.
A dataset benchmarks spatial and physical reasoning of LLMs using topology optimization tasks without simulation tools.
Large Language Models (LLM) topology optimization material distribution structural stability spatial reasoning physical reasoning boundary conditions applied forces supports