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Evidence-focused reasoning in RecGPT-Mobile-V2 boosts query quality by over 5% while slashing hard-failure rates to a mere 1.6%.
Re-ranking control alone boosts key performance metrics by over 2%, but extending it to fine ranking unlocks even greater gains without sacrificing system stability.
By cutting end-to-end serving resource consumption by over 50% while boosting user engagement metrics, RecGPT-V3 redefines efficiency in large-scale recommender systems.
ShopX transforms agentic shopping by seamlessly translating complex intents into item-space actions, outperforming traditional retrieval-based systems.
Uniboost decouples complex weighting schemes in recommendation systems, enabling precise attribution of each traffic allocation plan's contribution and boosting overall efficiency.
On-device LLMs can now drive real-time recommendation improvements, unlocking faster adaptation to evolving user intent without cloud reliance.
Train smarter, not bigger: LoopCTR unlocks state-of-the-art CTR prediction by decoupling computation from parameter growth through recursive layer reuse.
