{
  "id": 3889,
  "url": "https://arxiv.org/abs/2605.23204v1",
  "title": "AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery",
  "summary": "Scientific research is being reshaped by AI systems that move beyond isolated assistance toward longer-horizon workflows spanning literature grounding, hypothesis generation, experimentation, validation, reporting, and revision. This shift marks a transition from task-level AI for science to workflow-level research automation. Yet current systems remain fragmented, differing in autonomy, domain scope, execution environment, validation mechanism, and human oversight, while still struggling with e",
  "authors": "Guiyao Tie, Jiawen Shi, Dingjie Song, Yixiao Huang, Ziji Sheng, Xueyang Zhou et al.",
  "category": "research",
  "topics": "jobs-economy,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-22T03:40:30.000Z",
  "fetched_at": "2026-07-14T16:30:36.740Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3889",
  "original_url": "https://arxiv.org/abs/2605.23204v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}