arXiv AI
FLOORA: A Human-Aligned Domain-Specific Language Model for Architectural Design
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arXiv Computer Vision
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Mitigating Domain Shift in Conditioned Floor Plan Generation: Synthetic Pre-training for Data-Efficient Adaptation
The paper investigates how conditioned floor plan generation models perform when applied to datasets from different regions, revealing significant performance drops due to domain shift. To address this, the authors create a large synthetic training set that enforces physical constraints while deliberately reducing architectural realism, and show that pre‑training on this data boosts zero‑shot cross‑domain performance and speeds up fine‑tuning in low‑data scenarios.
arXiv AI
Jun 19
BIM-Edit: Benchmarking Large Language Models for IFC-Based Building Information Modeling
arXiv:2606. 20146v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied to computer-aided design (CAD) to generate design artifacts from textual instructions.
arXiv AI
Aug 11
Learning an Interior Layout Policy in a Domain Specific Language Action Space
arXiv:2608. 07547v1 Announce Type: cross Abstract: Indoor scene layout generation is a challenging task in interior design.
arXiv Machine Learning
Jun 5
Aggregating LLM-Based Weak Verifiers for Spatial Layout Generation
arXiv:2606. 05268v1 Announce Type: cross Abstract: We present a pipeline for building and aggregating task-specific, LLM-generated weak (imperfect) verifiers into a strong verifier for spatial layout domains.
arXiv Computation and Language
Sep 1
PlanCraft: Sketch, Refine, and Furnish for Architect-Inspired Progressive 3D Residential Scene Generation
PlanCraft introduces a progressive approach to 3D residential scene generation that mirrors how architects design: starting with rough sketches and refining them over time. It leverages a large dataset of real floor plans to train a SketchPlan module that generates partial sketches at various completion levels, a PlanCraft‑Diff module that sharpens these sketches into precise vector floor plans, and a PlanCraft‑Agent that furnishes rooms within the established spatial contract. The method outperforms existing 2D and 3D baselines, achieving a 61.1% lower FID and a 15‑point lead in expert‑rated spatial rationality, even with only 25% sketch completion.
arXiv AI
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Embodied CAD: Solver-Grounded LLM Agents for Parametric B-Rep Assembly Modeling
arXiv:2606. 31252v1 Announce Type: new Abstract: Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature, placement, and assembly relation must be accepted by an exact geometric kernel while remaining editable as parametric boundary representation geometry.