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RubricsTree transforms the evaluation landscape for personal health agents, achieving expert alignment and significant performance gains while addressing the scalability challenge in clinical deployment.
Training a foundation model on a trillion minutes of wearable sensor data unlocks surprisingly accurate predictions across a wide range of health conditions, even with limited labeled data.
Generalization in LLMs hinges on training reward saturation dynamics, with reasoning faithfulness emerging as a critical predictor of success under weak supervision.
