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. In engineering practice, this requires more than creating new geometry, models must also understand existing scenes, edit them correctly, and preserve semantics and relations. However, many CAD benchmarks focus on creating new models rather than editing existing ones, and mostly evaluate geometric correctness. We introduce BIM-Edit, a benchmark for evaluating LLMs on natural-language editing of Building Info
The proliferation of increasingly capable large language models is driving efforts to extend their utility into complex, domain-specific applications like computer-aided design.
Evaluating LLMs in realistic design scenarios, particularly editing existing models and preserving semantic integrity, is crucial for their practical adoption in industries reliant on precise digital representations.
The introduction of BIM-Edit shifts the focus of LLM evaluation in CAD from mere geometry generation to more complex tasks involving understanding, editing, and maintaining semantic relationships within established models.
- · AI model developers
- · Architecture, Engineering, and Construction (AEC) sector
- · Digital design software companies
- · Companies with legacy CAD systems
- · LLMs lacking robust contextual understanding
Improved benchmarks will accelerate the development of LLMs more capable of handling complex design modifications and maintaining data integrity in CAD environments.
The integration of advanced LLMs could significantly accelerate design iteration cycles and reduce manual effort in architectural and engineering projects.
Widespread adoption of AI-driven design editing could lead to new regulatory frameworks for AI-generated building information models, ensuring safety and compliance.
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Read at arXiv cs.AI