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arXiv · 2605.18932

HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation

Nikita Klimenko·Hesam Salehipour·Parham Eftekhar·Amir Khasahmadi·Ramon Elias Weber
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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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Nikita Klimenko, Hesam Salehipour, Parham Eftekhar, Amir Khasahmadi, Ramon Elias Weber. 2026-05-18. HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation. https://arxiv.org/abs/2605.18932

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