{
  "id": 13049,
  "url": "https://arxiv.org/abs/2607.21482v1",
  "title": "Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks",
  "summary": "Large language models (LLMs) and agents are now widely used tools in code development, with data typically sent to third-party cloud-based models. Their adoption in research using personal data is constrained by governance requirements that typically prohibit data transmission to external services. Locally deployable open-weight models offer an alternative since sensitive data never leave the local environment. We introduce an open-source framework for evaluating the efficacy of AI agents powere",
  "authors": "Mack Nixon, Liam Wright, Yevgeniya Kovalchuk, Alison Fang-Wei Wu, Martin Danka, Andy Boyd et al.",
  "category": "research",
  "topics": "regulation,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-23T16:23:42.000Z",
  "fetched_at": "2026-07-25T05:10:48.796Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/13049",
  "original_url": "https://arxiv.org/abs/2607.21482v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}