{
  "id": 3334,
  "url": "https://arxiv.org/abs/2606.01279v1",
  "title": "ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment",
  "summary": "AI agents are increasingly being tasked with automating AI research itself, particularly the critical post-training phase that transforms base LLMs into aligned assistants. However, recent evaluations reveal that even frontier agents struggle to perform this task. While the success of post-training fundamentally relies on acquiring high-quality data, relying on agents to autonomously curate targeted training datasets from the open web introduces severe challenges. Executing the long-horizon task",
  "authors": "Zhengyang Zhao, Shengjie Ye, Lu Ma, Hao Liang, Hengyi Feng, Wentao Zhang",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-31T15:03:50.000Z",
  "fetched_at": "2026-07-14T16:30:09.962Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3334",
  "original_url": "https://arxiv.org/abs/2606.01279v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}