Author: Hong Liu Efficient scaling of embedding models has been a core focus of Voyage AI research: Rather than simply scaling up, we aim to improve the quality-cost trade-off—extending the Pareto frontier beyond what is possible with standard architectures. In the Voyage 3.5 series, we pushed the scaling trends of traditional dense embedding models to their […]
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