HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation

Amir Khasahmadi; Hesam Salehipour; Nikita Klimenko; Parham Eftekhar; Ramon Elias Weber

arxiv: 2605.18932 · v2 · pith:24FCVORGnew · submitted 2026-05-18 · 💻 cs.LG · cs.AI

HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation

Nikita Klimenko , Hesam Salehipour , Parham Eftekhar , Amir Khasahmadi , Ramon Elias Weber This is my paper

Pith reviewed 2026-05-20 12:07 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords floor plan generationhypergraph representationlarge language modelsupervised fine-tuningspatial relationshipseditable designRPLAN datasetdistribution shift

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The pith

An LLM fine-tuned to output hypergraph text representations generates floor plans that outperform raster and vector methods while supporting arbitrary user-specified boundaries.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that supervised fine-tuning of a large language model on hypergraph-based textual encodings of floor plans captures spatial relationships and connectivity better than image or line-based alternatives. This formulation separates the outer apartment footprint from internal functional subdivisions, which allows the model to accept irregular or user-defined boundaries at generation time. The approach is trained on the RPLAN dataset and evaluated on both in-distribution and out-of-distribution data released by the authors. It reports gains in standard metrics, data efficiency under distribution shift, and editability for design workflows. If correct, the method shifts floor-plan generation from fixed representations toward structured, LLM-native outputs that remain editable after creation.

Core claim

By training an LLM via supervised fine-tuning to generate a hypergraph-based textual representation, the model encodes spatial relationships and connectivity within floor plans; this hypergraph formulation decouples apartment footprints from their functional and geometric subdivisions, enabling generation for arbitrary irregular user-specified boundaries and offering a high degree of editability.

What carries the argument

Hypergraph-based textual representation generated by the fine-tuned LLM, which encodes spatial relationships and connectivity by treating rooms as hyperedges connecting multiple nodes.

If this is right

Outperforms state-of-the-art rasterized and vectorized methods across diverse metrics.
Demonstrates improved data efficiency especially under distribution shift.
Enables floor-plan generation for arbitrary irregular user-specified boundaries.
Provides high editability suited to LLM-supported design workflows.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same hypergraph output format could be applied to other layout problems such as circuit boards or organizational charts where connectivity and grouping matter.
Conversational editing sessions with the LLM could allow iterative refinement of the generated hypergraph without retraining.
The decoupling of boundary and subdivision might reduce the amount of paired training data needed for related structured generation tasks.

Load-bearing premise

Supervised fine-tuning on the RPLAN dataset produces hypergraph representations that accurately encode spatial relationships and connectivity for reliable generalization to out-of-distribution cases and arbitrary boundaries.

What would settle it

Generate floor plans from the model on a set of previously unseen irregular boundaries and measure whether the resulting hypergraphs produce topologically valid layouts that satisfy the given boundary constraints without connectivity errors.

Figures

Figures reproduced from arXiv: 2605.18932 by Amir Khasahmadi, Hesam Salehipour, Nikita Klimenko, Parham Eftekhar, Ramon Elias Weber.

**Figure 2.** Figure 2: Qualitative comparison of generated floor plans. From left to right: (a) access graph, (b) [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Figure 3: Illustrative hypergraph edits. The title of each panel names the corresponding edit command. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

**Figure 4.** Figure 4: Examples of HypergraphFormer outputs fitted to non-Manhattan apartment boundaries. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: Illustration of the hypergraph representation from both a visual (top) and a data-structure [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗

**Figure 6.** Figure 6: RPLAN-to-hypergraph conversion on two test samples ( [PITH_FULL_IMAGE:figures/full_fig_p025_6.png] view at source ↗

**Figure 7.** Figure 7: Distribution of (a) apartment area, (b) number of bedrooms, and (c) five boundary-shape [PITH_FULL_IMAGE:figures/full_fig_p027_7.png] view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The core move—fine-tuning an LLM to emit textual hypergraphs that separate outer boundaries from internal divisions—looks workable for editable floor plans, but the outperformance and OOD claims rest on thin visible evidence.

read the letter

The paper's main contribution is training an LLM via supervised fine-tuning to output a hypergraph in text form rather than raster or vector data. This lets the model treat the apartment footprint as fixed input while generating the room layout and connections separately, which in principle supports user-specified irregular boundaries and post-generation edits. They train on RPLAN and evaluate on a new out-of-distribution set they release, claiming better metrics and data efficiency under shift compared with prior raster or vector baselines. Releasing the OOD data is a concrete plus for the community, and the editability angle aligns with real design-tool needs. The hypergraph framing itself is a straightforward way to encode connectivity without pixel-level artifacts. That said, the abstract and available details give no numbers, no ablation tables, and no description of how hypergraph validity is checked or enforced. Without explicit geometric constraints or post-processing, it is not obvious that the model will avoid overlaps, disconnected rooms, or boundary violations on truly irregular inputs that differ from RPLAN's mostly regular layouts. The stress-test point about missing validity mechanisms therefore lands. This work is aimed at people building generative tools for architecture and urban planning who already use LLMs. A reader looking for a practical, editable representation will get some ideas here even if the current results feel preliminary. The paper deserves a serious referee because the framing is coherent and the released data adds value, but any review should focus on quantitative validation and output correctness checks.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces HypergraphFormer, which uses supervised fine-tuning of an LLM to generate hypergraph-based textual representations that encode spatial relationships and connectivity in floor plans. Trained on the RPLAN dataset and evaluated on a newly released out-of-distribution dataset, the approach claims to outperform rasterized and vectorized state-of-the-art methods across diverse metrics, exhibit improved data efficiency under distribution shift, enable generation for arbitrary irregular user-specified boundaries by decoupling footprints from subdivisions, and offer high editability for design workflows.

Significance. If the empirical claims of outperformance, data efficiency, and reliable generalization to arbitrary boundaries hold with proper validation, this could represent a meaningful advance in LLM-assisted architectural design by providing more flexible and editable representations than raster or vector baselines. The release of the out-of-distribution dataset strengthens reproducibility. The core idea of hypergraph decoupling has potential if the textual outputs consistently preserve valid geometry and connectivity.

major comments (2)

[Abstract] Abstract: The assertion that the method 'outperforms state-of-the-art techniques based on rasterized or vectorized representations across a diverse set of metrics' and shows 'improved data efficiency, particularly under distribution shift' is presented without any quantitative metric values, tables, ablation studies, or error analysis, which is load-bearing for the central empirical claims.
[§3 and §4] §3 (Method) and §4 (Experiments): The supervised fine-tuning process to produce hypergraph textual outputs includes no described mechanisms for enforcing geometric validity (e.g., non-overlapping rooms, boundary adherence, or connectivity preservation) when inputs are irregular or out-of-distribution, directly undermining the claim that the formulation enables reliable generation for arbitrary user-specified boundaries.

minor comments (1)

[§3.1] The hypergraph textual representation format would benefit from an explicit example in the main text to clarify how nodes, hyperedges, and attributes are serialized.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address each major comment point by point below, indicating where revisions will be made to improve clarity and support for our claims.

read point-by-point responses

Referee: [Abstract] Abstract: The assertion that the method 'outperforms state-of-the-art techniques based on rasterized or vectorized representations across a diverse set of metrics' and shows 'improved data efficiency, particularly under distribution shift' is presented without any quantitative metric values, tables, ablation studies, or error analysis, which is load-bearing for the central empirical claims.

Authors: We agree that the abstract, as a high-level summary, does not include specific quantitative values or references to tables and ablations. These details are provided in full in Section 4, including metric comparisons, data-efficiency curves under distribution shift, and error analyses on both RPLAN and the released out-of-distribution dataset. To directly address the concern, we will revise the abstract to incorporate a small number of key quantitative highlights (e.g., relative improvements on primary metrics) while remaining within length constraints. revision: yes
Referee: [§3 and §4] §3 (Method) and §4 (Experiments): The supervised fine-tuning process to produce hypergraph textual outputs includes no described mechanisms for enforcing geometric validity (e.g., non-overlapping rooms, boundary adherence, or connectivity preservation) when inputs are irregular or out-of-distribution, directly undermining the claim that the formulation enables reliable generation for arbitrary user-specified boundaries.

Authors: The referee correctly notes that Section 3 does not explicitly describe additional enforcement mechanisms such as geometric constraints, post-processing, or validity filters beyond the supervised fine-tuning itself. The hypergraph textual representation is intended to encode connectivity and boundary relationships directly, with the model learning valid structures from the training distribution; empirical results on the out-of-distribution dataset are used to demonstrate generalization to irregular boundaries. To strengthen the manuscript, we will expand the Method section to clarify how the representation format and training data promote validity, and we will add a brief discussion of any validation steps performed during evaluation. revision: yes

Circularity Check

0 steps flagged

No circularity detected; claims rest on held-out empirical evaluation

full rationale

The paper describes a standard supervised fine-tuning pipeline in which an LLM is trained to output a textual hypergraph encoding of floor plans from the RPLAN dataset and is then evaluated on held-out RPLAN data plus a separately released out-of-distribution set. No derivation chain, equation, or first-principles result is presented that reduces to its own inputs by construction. Performance and generalizability claims are supported by direct metric comparisons rather than by renaming fitted quantities or by load-bearing self-citations. The hypergraph decoupling argument is an architectural description whose validity is asserted via experiment, not by tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the premise that LLMs can be supervised-fine-tuned to produce hypergraph representations that faithfully capture floor-plan connectivity; no explicit free parameters are named, but the approach implicitly depends on standard LLM training choices and the validity of the hypergraph encoding.

axioms (1)

domain assumption Supervised fine-tuning on floor plan data enables an LLM to generate hypergraph representations that encode spatial relationships and connectivity.
This premise is invoked when the abstract states the model is trained via supervised fine-tuning to generate the hypergraph-based textual representation.

invented entities (1)

Hypergraph-based textual representation no independent evidence
purpose: To encode spatial relationships and connectivity within floor plans for generation and editing.
The representation is introduced as the core output format that decouples footprints from subdivisions; no independent falsifiable evidence outside the training loop is described.

pith-pipeline@v0.9.0 · 5708 in / 1388 out tokens · 40694 ms · 2026-05-20T12:07:14.145996+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean reality_from_one_distinction unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

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... 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.
IndisputableMonolith/Foundation/AlexanderDuality.lean alexander_duality_circle_linking unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We adopt the graph-based textual representation of a floor plan introduced by Weber et al. [15], referred to as a hypergraph, which decouples an apartment’s outer boundary from its interior layout by combining a binary space partition (BSP) tree... with an access graph capturing their functional connectivity.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.