RubricsTree: Scalable and Evolving Open-Ended Evaluation of Personal Health Agents across Health Memory and Medical Skills
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
Record details
Published: 16 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare · Agents & autonomy
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (16 June 2026), “RubricsTree: Scalable and Evolving Open-Ended Evaluation of Personal Health Agents across Health Memory and Medical Skills,” evidence record 867, https://ethics.ai/record/867 (originally published by arXiv).
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