{
  "id": 867,
  "url": "https://arxiv.org/abs/2606.18203v1",
  "title": "RubricsTree: Scalable and Evolving Open-Ended Evaluation of Personal Health Agents across Health Memory and Medical Skills",
  "summary": "The LLM-empowered personal health agents with user health (sensor) metrics have offered a promising pathway to alleviate global disparities in healthcare access. However, large-scale clinical deployment remains constrained by an open-ended evaluation bottleneck: physician annotation is reliable but costly and unscalable, while LLM-as-a-judge evaluators are scalable but subjective, inconsistent, and sometimes clinically misaligned. We introduce RubricsTree, a scalable evaluation framework with an",
  "authors": "Weizhi Zhang, Zechen Li, Hamid Palangi, Ben Graef, A. Ali Heydari, Simon A. Lee et al.",
  "category": "research",
  "topics": "safety-alignment,healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-16T17:34:53.000Z",
  "fetched_at": "2026-07-14T14:14:50.325Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/867",
  "original_url": "https://arxiv.org/abs/2606.18203v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}