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Despite enhancements in classification accuracy, the fundamental ranking gap in Chain-of-Thought models remains a stubborn bottleneck in pointwise reranking.
Interleaving speech and text during ASR training boosts entity recognition accuracy and narrows the gap between modalities, challenging traditional training paradigms.
NOVA reduces silent failures in recommender systems by over 13x while boosting performance metrics by up to 2.02% in online A/B testing.
