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Achieving similar performance to larger models with significantly less data and faster inference speeds could redefine efficiency benchmarks in foundation models.
ProtoPilot achieves a staggering 90.2% expert-preference rate in autonomous wet-lab experimentation, setting a new benchmark for protocol generation and execution.
Static benchmarks fail to predict LLM performance in dynamic clinical settings, with top models only achieving 60.4% of expert criteria in real-world simulations.