From Ambiguity to Execution: An Agentic Neuro-Symbolic Framework for Transforming Building Regulations into Deterministic Constraints

Abstract

The transition toward fully autonomous Digital Building Permitting (DBP) requires Automated Compliance Checking (ACC) systems to verify Building Information Models (BIM) against natural-language regulations. While Large Language Models (LLMs) offer strong semantic comprehension, integrating them into ACC introduces “Spatial Hallucinations” and “Serialization Bottlenecks” when processing high-dimensional BIM graphs. This research proposes an Agentic Neuro-Symbolic Framework that decouples semantic interpretation from geometric verification. Instead of relying on generative text for spatial reasoning, an Agentic LLM acts as a dynamic logic synthesizer orchestrating a deterministic geometry ker..