LLM-Based DSL Framework for Indoor Layout Generation

A recent paper published on arXiv (2608.07547) presents LayoutDSL, a framework leveraging large language models (LLMs) to develop interior layout policies within a domain-specific language (DSL) action framework. The authors criticize current techniques for oversimplifying the generation of indoor scenes by relying on broad 3D bounding boxes and overlooking important structural features such as doors and windows. They highlight that many previous methods approach spatial reasoning through direct coordinate predictions, treating layout design as a continuous regression problem, which limits the understanding of the underlying reasoning. LayoutDSL offers a clear symbolic representation of layout data, creating a structured action space with each action linked to an understandable design choice. Additionally, the framework generates a 3D-Fr dataset for training and assessment. This paper is marked as a cross-type announcement, suggesting it may have been submitted to various platforms. The research tackles a key issue in automating interior design, focusing on enhancing the interpretability and reasoning skills of AI in layout generation.