{
  "id": 13584,
  "url": "https://arxiv.org/abs/2607.21799",
  "title": "Agentic Evaluation of Copyright Law Compliance",
  "summary": "arXiv:2607.21799v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly perform commercial tasks that involve retrieving external content such as images and, where appropriate, reproducing that content. LLM agents should comply with the law, including copyright law. Presently, however, we lack adequate frameworks to assess whether they do so in practice. To that end, we introduce \\textbf{Copyright-Bench}, a benchmark designed to evaluate \\textit{LLM agents' compliance wi",
  "authors": "Zheng Hui, Doni Bloomfield, Noam Kolt",
  "category": "research",
  "topics": "regulation,copyright-ip,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-27T04:00:00.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13584",
  "original_url": "https://arxiv.org/abs/2607.21799",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}