{
  "id": 6629,
  "url": "https://arxiv.org/abs/2603.27448v1",
  "title": "GIFT: Bootstrapping Image-to-CAD Program Synthesis via Geometric Feedback",
  "summary": "Generating executable CAD programs from images requires alignment between visual geometry and symbolic program representations, a capability that current methods fail to learn reliably as design complexity increases. Existing fine-tuning approaches rely on either limited supervised datasets or expensive post-training pipelines, resulting in brittle systems that restrict progress in generative CAD design. We argue that the primary bottleneck lies not in model or algorithmic capacity, but in the s",
  "authors": "Giorgio Giannone, Anna Clare Doris, Amin Heyrani Nobari, Kai Xu, Akash Srivastava, Faez Ahmed",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-28T23:49:20.000Z",
  "fetched_at": "2026-07-14T16:32:37.310Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6629",
  "original_url": "https://arxiv.org/abs/2603.27448v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}