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ROCS can triple the throughput of recommendation systems without sacrificing prediction accuracy, revolutionizing how we handle user requests in large-scale settings.
Re-ranking can make or break user engagement, and GR2 boosts performance by over 18% by harnessing the power of LLMs in ways previously unexplored.
Doubling the knowledge transfer ratio from trillion-parameter foundation models to recommendation systems is now possible without real-time FM inference, unlocking significant conversion improvements in large-scale industrial settings.
