# Automating Geometry-Intensive Compliance Checking in BIM: Graph-Based Semantic Reasoning Framework

arXiv: https://arxiv.org/abs/2606.12065 · HTML: https://mlsanity.com/paper/2606.12065/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 10 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning](https://mlsanity.com/paper/2102.10407/) (2021)
- [NLP-based Automated Compliance Checking of Data Processing Agreements against GDPR](https://mlsanity.com/paper/2209.09722/) (2022)
- [Few-shot Object Detection with Self-adaptive Attention Network for Remote Sensing Images](https://mlsanity.com/paper/2009.12596/) (2020)
- [Cypher4BIM: Releasing the Power of Graph for Building Knowledge Discovery](https://mlsanity.com/paper/2405.16345/) (2024)
- [LLM Agent Framework for Intelligent Change Analysis in Urban Environment using Remote Sensing Imagery](https://mlsanity.com/paper/2601.02757/) (2026)
- [Predictive Modeling: BIM Command Recommendation Based on Large-scale Usage Logs](https://mlsanity.com/paper/2504.05319/) (2025)
- [MMUEChange: A Generalized LLM Agent Framework for Intelligent Multi-Modal Urban Environment Change Analysis](https://mlsanity.com/paper/2601.05483/) (2026)

## Built on by (most cited first, 1 of 1)

- [ARCHER: Agentic Rule and Compliance Harness for Executable Regulations](https://mlsanity.com/paper/2607.25566/) (2026)

## Abstract (arXiv)

Automating compliance check for geometry-intensive regulations remains a significant technical bottleneck in Building Information Modeling (BIM), primarily due to the semantic disparity between high-level regulatory logic and structured IFC data. Existing methods, often reliant on static rule templates, struggle to traverse multi-hop reasoning chains or resolve latent spatial dependencies across multiple building entities. To address these challenges, a Spatial-Geometric Reasoning System for Building Information Modeling (SGR-BIM) is proposed as an integrative graph-driven reasoning framework. SGR-BIM dynamically constructs a cross-modal knowledge graph that aligns user intent, regulatory semantics, and BIM geometry, enabling interpretable reasoning without rigid hard-coding. Validated on 679 expert-verified queries from fire safety codes, the framework achieves 84.3% accuracy, representing an 8.6% improvement over enhanced-tool single-agent baselines. This research provides a graph-based semantic reasoning paradigm, enhancing the transparency and flexibility of automated geometric compliance check workflows in the Architecture, Engineering, and Construction (AEC) industry.