Google (United States)

The work proposes and deploys a self-evolving recommendation system in which Gemini-family LLM agents act as machine learning engineers: an Offline Agent (Fast Loop, waking every 5 minutes and running daily) generates and offline-scores candidate configurations, while an Online Agent (Slow Loop, running daily) ranks candidates and drives A/B experiments; across several YouTube recommendation surfaces the agents autonomously discovered optimizer switches (Adagrad to RMSprop, FTRL), a Gated Path (GLU) architecture, activation refinement, multi-objective reward synthesis, and reward hyperparameter tuning, with multiple online metrics improving at the 95% confidence level and experiment throughput rising from Θ(1)-Θ(10) per week to Θ(100) per week.

The work proposes and deploys a self-evolving recommendation system in which Gemini-family LLM agents act as machine learning engineers: an Offline Agent (Fast Loop, waking every 5 minutes and running daily) generates and offline-scores candidate configurations, while an Online Agent (Slow Loop, running daily) ranks candidates and drives A/B experiments; across several YouTube recommendation surfaces the agents autonomously discovered optimizer switches (Adagrad to RMSprop, FTRL), a Gated Path (GLU) architecture, activation refinement, multi-objective reward synthesis, and reward hyperparameter tuning, with multiple online metrics improving at the 95% confidence level and experiment throughput rising from Θ(1)-Θ(10) per week to Θ(100) per week.

This Nature Medicine comment argues that trust in clinical AI cannot be benchmarked into existence but must be earned through rigorous prospective studies in real-world clinical settings, noting that the hardest lessons often concern the humans and systems around the AI rather than the technology itself.

This Nature Medicine comment argues that trust in clinical AI cannot be benchmarked into existence but must be earned through rigorous prospective studies in real-world clinical settings, noting that the hardest lessons often concern the humans and systems around the AI rather than the technology itself.