Self-Improving CAD Generation Agents with Finite Element Analysis as Feedback

• URL: http://arxiv.org/abs/2605.17448v1
• Date: Sun, 17 May 2026 13:47:38 GMT
• Title: Self-Improving CAD Generation Agents with Finite Element Analysis as Feedback
• Abstract summary: We introduce a more industry-native task formulation that requires a model to produce a fully assembled multi-part STEP file from a free-form engineering brief.<n>FEA validation reveals that Codex (GPT-5.5) and Claude Code (Opus-4.7) agents do not produce a single strict-passing artifact in the main first-attempt sweep.<n>On S2O and Fusion360, the same feedback tools improve geometric reconstruction, with GPT-5.5/xhigh rising from 0.444 to 0.592 Box-IoU on S2O and from 0.397 to 0.505 on Fusion360.
• Score: 21.804668679670655
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Computer-aided design (CAD) is the backbone of modern industrial design, yet learned CAD generators still fall short of real engineering pipelines: they neither iterate like engineers nor evaluate what engineering requires. Prior work has treated CAD generation as two disjoint steps, part synthesis and assembly, where the former is graded by proximity to a gold reference and the latter, when handled at all, is reduced to a separate constraint solving step. In this work, we introduce a more industry-native task formulation that requires a model to produce a fully assembled multi-part STEP file from a free-form engineering brief, which is then validated via finite element analysis (FEA). FEA validation reveals that Codex (GPT-5.5) and Claude Code (Opus-4.7) agents do not produce a single strict-passing artifact in the main first-attempt sweep, with the best configuration meeting only about 20% of typed requirements on average. Moreover, we introduce two additional supervision signals, a novel text-only blueprint schema and a 21-view image renderer that aids the agent's visual inspection, that better align the generation loop with how engineers iterate in practice. On S2O and Fusion360, the same feedback tools improve geometric reconstruction, with GPT-5.5/xhigh rising from 0.444 to 0.592 Box-IoU on S2O and from 0.397 to 0.505 on Fusion360. Together these signals move CAD programs toward artifacts that are not only visually plausible but also checked against physical and structural requirements.

Related papers

• CADEngBench: It Looks Like CAD, but Does It Work? Evaluating Parametric Design, Assembly Reasoning, and Physics Simulation [1.7032702581423902]
  A CAD model is not engineering-grade merely because it looks correct.<n>We present CADEngBench, a two-track benchmark for these capabilities.
  arXiv  Detail & Related papers  (2026-08-10T08:47:18Z)
• OmniMech: All-in-one Multimodal Mechanical Benchmark for 3D Reconstruction [15.427717696810456]
  We introduce OmniMech, the first million-scale benchmark for evaluating vision-language models on CAD generation from industrial data.<n> OmniMech contains more than 251,000 fully dimensioned and toleranced 2D orthographic drawings, paired with native CAD models.<n>We will release the benchmark data, evaluation code, and tool interfaces to support future research.
  arXiv  Detail & Related papers  (2026-08-06T02:36:22Z)
• Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data [8.896486786253766]
  We introduce Zero-to-CAD, a scalable framework for executable CAD construction sequences.<n>We frame synthesis as an agentic search problem: by embedding a large language model within a feedback-driven CAD environment, our system iteratively generates, executes, and validates code.<n>This agentic approach enables the synthesis of approximately one million executable, readable, editable CAD sequences.
  arXiv  Detail & Related papers  (2026-04-27T13:46:41Z)
• MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale [92.09717763663873]
  We present MinerU2.5-Pro, which advances the state of the art purely through data engineering and training strategy design.<n>At its core is a Data Engine co-designed around coverage, informativeness, and annotation accuracy.<n>We show that MinerU2.5-Pro achieves 95.69 on OmniDocBench v1.6, improving over the same-architecture baseline by 2.71 points and surpassing all existing methods.
  arXiv  Detail & Related papers  (2026-04-06T15:44:18Z)
• CADReasoner: Iterative Program Editing for CAD Reverse Engineering [8.468103398438917]
  Many AI systems tackle CAD reverse engineering, but most are single-pass and miss fine geometric details.<n>We introduce CADReasoner, a model trained to iteratively refine its prediction using geometric discrepancy.<n>The model outputs a runnable CadQuery Python program whose rendered mesh is fed back at the next step.
  arXiv  Detail & Related papers  (2026-02-18T10:27:29Z)
• Using GUI Agent for Electronic Design Automation [123.86509061313912]
  Graphical User Interface (GUI) agents adopt an end-to-end paradigm that maps a screenshot to an action sequence.<n>Existing GUI agents are evaluated almost exclusively on commodity software such as Microsoft Word and Excel.<n>This work extends GUI agents from generic office automation to specialized, high-value engineering domains.
  arXiv  Detail & Related papers  (2025-12-12T14:49:32Z)
• Exploring Diffusion Transformer Designs via Grafting [82.91123758506876]
  We present grafting, a simple approach for editing pretrained diffusion transformers (DiTs) to materialize new architectures under small compute budgets.<n>We show that new diffusion model designs can be explored by grafting pretrained DiTs, with edits ranging from operator replacement to architecture restructuring.
  arXiv  Detail & Related papers  (2025-06-05T17:59:40Z)
• CADCrafter: Generating Computer-Aided Design Models from Unconstrained Images [69.7768227804928]
  CADCrafter is an image-to-parametric CAD model generation framework that trains solely on synthetic textureless CAD data.<n>We introduce a geometry encoder to accurately capture diverse geometric features.<n>Our approach can robustly handle real unconstrained CAD images, and even generalize to unseen general objects.
  arXiv  Detail & Related papers  (2025-04-07T06:01:35Z)
• PHT-CAD: Efficient CAD Parametric Primitive Analysis with Progressive Hierarchical Tuning [52.681829043446044]
  ParaCAD comprises over 10 million annotated drawings for training and 3,000 real-world industrial drawings with complex topological structures and physical constraints for test.<n> PHT-CAD is a novel 2D PPA framework that harnesses the modality alignment and reasoning capabilities of Vision-Language Models.
  arXiv  Detail & Related papers  (2025-03-23T17:24:32Z)
• PS-CAD: Local Geometry Guidance via Prompting and Selection for CAD Reconstruction [86.726941702182]
  We introduce geometric guidance into the reconstruction network PS-CAD. We provide the geometry of surfaces where the current reconstruction differs from the complete model as a point cloud. Second, we use geometric analysis to extract a set of planar prompts, that correspond to candidate surfaces.
  arXiv  Detail & Related papers  (2024-05-24T03:43:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.