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