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The real capabilities of an agentic AI system stem from its ability to tackle scientific research. Scientific research includes insufficient evidence on trusting results, constant failures, and uncertainty. Scientists must perform difficult experiments, troubleshoot answers, assess translational risks, and ponder the next steps. Prior AI benchmarks failed to capture the actuality and focused on structured questions. To tackle this, researchers developed LifeSciBench, a benchmark created to assess whether artificial intelligence systems can support science research workflows. What Does LifeSciBench Measure? LifeSciBench marks whether artificial intelligence systems can complement science research tasks and not just answer structured questions. It strives for […]