Google's multi-agent framework turns noisy wearables into vetted biomarker candidates
Wearables already capture floods of physiological data, but turning it into trustworthy biomarkers is the real bottleneck. Google's Biomarker Discovery Framework tackles that with a human-supervised multi-agent loop that generates hypotheses, runs adversarial validation, and grounds results in the literature before anything becomes a candidate. Across three cohorts (9,279 participant-observations), it recovered known clinical signals and surfaced convergent markers across mental-health and metabolic datasets. The design keeps statistical rigor front and center, addressing exactly where naive LLM pipelines leak spurious correlations.