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As frontier models saturate existing benchmarks, the work of designing harder, more meaningful evaluations becomes the primary mechanism by which the field can track — and anticipate — the pace of AI capability growth.
Researchers and developers building on OpenAI's platform should watch for the life sciences model series and its plugin ecosystem, which could significantly accelerate biology and drug discovery workflows through agentic, reproducible automation.
Life sciences teams can use GPT-Rosalind in Codex to automate multi-lane evidence synthesis across genetics, biology, and regulatory data — replacing manual literature triage with a structured, repeatable agentic workflow for target prioritization.