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Query quality improves by over 5% while reducing hard-failure rates by more than half, showcasing a novel approach to balancing efficiency and intent accuracy in on-device systems.
Lever-Edit shows that you can effectively optimize image editing policies using T2I rewards, bypassing the need for costly editing-specific rewards altogether.
System Intelligence emerges as a game-changer, enabling LLM agents to collaborate effectively across complex tasks by organizing their interactions through dynamic graph structures.
On-device LLMs can now drive real-time recommendation improvements, unlocking faster adaptation to evolving user intent without cloud reliance.
