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Research PaperResearchia:202610.20005

Can It Actually Work? Synthesizing Functional CAD Mechanisms

Wenqian Zhang

Abstract

While LLM agents can generate visually plausible CAD assemblies, these designs often fail to fulfill their intended physical functions. We study functional CAD design, a setting where agents interpret underspecified requests to construct and iteratively revise mechanisms that satisfy functional requirements in physical simulation. Verifying these requirements is nontrivial: different candidate mechanisms may realize the same function through entirely different components, controls, and operating...

Submitted: October 20, 2026Subjects: Computer Vision; Computer Vision

Description / Details

While LLM agents can generate visually plausible CAD assemblies, these designs often fail to fulfill their intended physical functions. We study functional CAD design, a setting where agents interpret underspecified requests to construct and iteratively revise mechanisms that satisfy functional requirements in physical simulation. Verifying these requirements is nontrivial: different candidate mechanisms may realize the same function through entirely different components, controls, and operating sequences. To bridge this gap, we propose FunCAD-Harness, a framework that translates abstract requirements into candidate-specific physical tests and uses simulation feedback to guide design revisions. To systematically evaluate this capability, we introduce FunCADBench, a benchmark spanning 46 mechanism families with interdependent and multi-step operations. Experiments across frontier LLM agents show that FunCAD-Harness exposes subtle mechanical failures invisible to visual inspection, substantially improving functional success rates over standard simulation-augmented baselines. Ultimately, our work marks a step toward CAD agents capable of translating human intent into working mechanisms.


Source: arXiv:2610.26861v1 - http://arxiv.org/abs/2610.26861v1 PDF: https://arxiv.org/pdf/2610.26861v1 Original Link: http://arxiv.org/abs/2610.26861v1

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Submission Info
Date:
Oct 20, 2026
Topic:
Computer Vision
Area:
Computer Vision
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