HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation

• URL: http://arxiv.org/abs/2605.18932v2
• Date: Fri, 22 May 2026 18:28:41 GMT
• Title: HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation
• Abstract summary: We propose a novel approach to generation of floor plans based on learning hypergraph representations with a large language model (LLM)<n>We show that the proposed methodology offers a high degree of editability, making it well suited to design-oriented supported by LLMs.
• Score: 0.8699280339422538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 demonstrate its generalizability on a separate out-of-distribution dataset, which we release in this paper. Our method outperforms state-of-the-art techniques based on rasterized or vectorized representations across a diverse set of metrics. We also show improved data efficiency, particularly under distribution shift. The hypergraph formulation enables the generation of floor plans for arbitrary, irregular, user-specified boundaries by decoupling apartment footprints from their functional and geometric subdivisions. Furthermore, we show that the proposed methodology offers a high degree of editability, making it particularly well suited to design-oriented workflows supported by LLMs.

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