{
  "id": 1230,
  "url": "https://arxiv.org/abs/2606.10334v1",
  "title": "Self-Distillation Policy Optimization via Visual Feedback: Bridging Code and Visual Artifacts",
  "summary": "Code-generating large language models (LLMs) increasingly produce visual artifacts such as charts, web pages, and slides by writing programs that are executed by non-differentiable renderers, committing to code before observing the render. As a result, otherwise executable code often yields artifacts with visually salient defects, including overlapping elements, clipped text, broken alignment, low contrast, and overflow. We study visual-feedback self-distillation for code-generated visual artifa",
  "authors": "Haoyu Dong",
  "category": "research",
  "topics": "regulation,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-09T02:28:39.000Z",
  "fetched_at": "2026-07-14T14:15:07.844Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1230",
  "original_url": "https://arxiv.org/abs/2606.10334v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}