bim

This study proposes an integrated framework using domain-specific LLMs for intelligent identification and repair of design defects in BIM, achieving 85% accuracy and 94% reasonable repair suggestions with effective hallucination control.

This paper introduces IFCMemoryBench, a human-validated benchmark for evaluating long-term memory in LLM-based agents for BIM information retrieval. It shows that current memory systems achieve only 32.4% answer accuracy, revealing a domain-transfer gap in agent memory.

This paper introduces SGR-BIM, a graph-driven semantic reasoning framework that dynamically aligns regulatory intent with BIM geometry to automate geometry-intensive compliance checks, achieving 84.3% accuracy on fire safety code queries.

This paper introduces Ishigaki-IDS-Bench, a benchmark for evaluating LLMs' ability to generate Information Delivery Specification (IDS) XML from BIM information requirements. Evaluation of 10 LLMs shows best models achieve 65.6% macro F1 for content agreement but only 27.7% pass the Content audit, indicating struggles with standard and vocabulary constraints.