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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.18932 |
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| _version_ | 1866918519082319872 |
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| author | Klimenko, Nikita Salehipour, Hesam Eftekhar, Parham Khasahmadi, Amir Weber, Ramon Elias |
| author_facet | Klimenko, Nikita Salehipour, Hesam Eftekhar, Parham Khasahmadi, Amir Weber, Ramon Elias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18932 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation Klimenko, Nikita Salehipour, Hesam Eftekhar, Parham Khasahmadi, Amir Weber, Ramon Elias Machine Learning Artificial Intelligence 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. |
| title | HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.18932 |