---
format: "aidr-story-markdown/v1"
id: "fd4092e3115c0e96f5d86356ea0d063712c8012f7387e441b3490ef2d5d7ba7c"
canonical_url: "https://aidr.today/fd4092e3?lang=en"
title: "BIBIMBAP: A Benchmark for Instructional BIM-Based Automated Programming"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-11-10T05:00:00.000Z"
category: "Data"
topics: ["llm","data-engineering"]
source_urls: ["https://arxiv.org/abs/2611.09933"]
summary: "BIBIMBAP, a new Text-to-BIM benchmark, evaluates large language models' ability to edit Industry Foundation Classes (IFC) through 100 curated tasks covering Create-Read-Update-Delete operations across spatial, geometric, topological, numeric, and conceptual categories. Each task includes a natural-language prompt, an IFC model, expected structured outputs, and test scripts for automated evaluation. Baseline results show limited performance with a best score of 50.2% and frequent failures in constraint preservation and spatial reasoning."
---

# BIBIMBAP: A Benchmark for Instructional BIM\-Based Automated Programming

> [Open the canonical story](<https://aidr.today/fd4092e3?lang=en>)

**Published:** 2026-11-10T05:00:00.000Z
**Category:** Data
**Topics:** llm, data\-engineering

## Summary

BIBIMBAP, a new Text\-to\-BIM benchmark, evaluates large language models' ability to edit Industry Foundation Classes \(IFC\) through 100 curated tasks covering Create\-Read\-Update\-Delete operations across spatial, geometric, topological, numeric, and conceptual categories\. Each task includes a natural\-language prompt, an IFC model, expected structured outputs, and test scripts for automated evaluation\. Baseline results show limited performance with a best score of 50\.2% and frequent failures in constraint preservation and spatial reasoning\.

## Sources

- [Story source](<https://arxiv.org/abs/2611.09933>)