HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation

ArXi:2605.18932v1 Announce Type: cross In this work, we propose HypergraphFormer, a novel and efficient approach to floor plan generation based on learning hypergraph representations with a large language model (LLM). The model is trained via supervised fine-tuning to generate a hypergraph-based textual representation that encodes spatial relationships and connectivity information within floor plans. We train and evaluate our approach on the RPLAN dataset, and further nstrate its generalizability on a separate out-of-distribution dataset, which we release in this paper.