# Training-Free Agentic Computer Vision for Structural Component Detection in 2D Structural Framing Plans

arXiv: https://arxiv.org/abs/2608.17237 · HTML: https://mlsanity.com/paper/2608.17237/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 18 Aug 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 19 of 19)

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- [Integrating Large Language Models for Automated Structural Analysis](https://mlsanity.com/paper/2504.09754/) (2025)
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## Abstract (arXiv)

Converting structural framing plans into editable finite-element model drafts is labor-intensive and susceptible to transcription errors. Existing building-component recognition systems generally depend on task-specific neural detectors, whereas language-model agents in structural engineering typically operate on text or model data rather than on drawings. To the authors' knowledge, this work is the first to apply an agentic vision-language layer to structural-component detection and model drafting from framing-plan PDFs without task-specific detector training or fine-tuning. A deterministic stage extracts geometric primitives, estimates scale by dimension-ratio consensus, recognizes five entity classes using an explicit drafting grammar, and assembles an editable layout. The agentic stage constrains typed corrections through deterministic candidates, operation-specific admission tests, change-level review, and fail-closed transactions. Evaluation used an author-generated benchmark of 100 plans, divided equally between a development half used for all rule revisions and a seed-disjoint held-out half generated after the rules were frozen and evaluated once. All scores are end-to-end results for the complete framework on the held-out half. Scale estimates were within 0.1% of the generator reference for every drawing. Recall and precision were 0.922/0.997 for columns, 0.886/0.990 for beams, 1.000/1.000 for walls, 1.000/1.000 for braces, and 1.000/0.964 for openings. A controlled study repeated two corruptions three times on three development drawings. Calibration passed all nine trials, whereas member repair satisfied every strict end-state criterion in five of nine trials. Because both benchmark halves share a generator, the evaluation does not address independently drafted plans, raster input, analytical connectivity, or solver validation.