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IfcLLM framework combines relational and graph data for accurate IFC model querying

By PulseAugur Editorial · [1 sources] ·

Researchers have developed IfcLLM, a new framework designed to improve natural language querying of Industry Foundation Classes (IFC) models. This system uniquely combines relational and graph representations, routing queries to the most suitable backend for attribute retrieval or spatial reasoning. An integrated LLM agent employs iterative retry-and-refine logic to recover from failures autonomously. Tested on three IFC models across 30 query scenarios, IfcLLM achieved up to 96% accuracy, with open-weight LLMs showing potential for local deployment in sensitive environments. AI

IMPACT This framework could enable more accessible interaction with complex building information models, potentially streamlining workflows in architecture, engineering, and construction.

RANK_REASON The cluster contains a research paper detailing a new framework for querying IFC models using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

IfcLLM framework combines relational and graph data for accurate IFC model querying

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The cluster contains a research paper detailing a new framework for querying IFC models using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Rabindra Lamsal, Sisi Zlatanova, Haowen Xu, Yafei Sun, Xuesong Shen ·

    IfcLLM: Natural Language Querying of IFC Models through Complementary Relational and Graph Representations

    arXiv:2605.13236v3 Announce Type: replace Abstract: The Industry Foundation Classes (IFC) standard is central to building data exchange across the lifecycle, from design and construction to facility management and Digital Twin integration. In operational settings, stakeholders in…

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