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NVIDIA used Tavily to generate high-quality research trajectories for post-training Nemotron 3 Super, leading to top rankings on DeepResearch Benchmarks. The process highlights how specialized fine-tuning enhances research agent efficiency and performance.
Using NVIDIA's Nemotron 3 Super and Moonshot AI's Kimi K3 as examples, this article analyzes how the LatentMoE architecture overcomes the efficiency bottleneck of traditional MoE by compressing the Expert computation dimension, and points out that this is a turning point for the next generation of MoE architectures.