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Radial Attention introduces a novel sparse attention mechanism that cuts training costs by 4.4× and inference time by 3.7× in video diffusion models, enabling generation of longer videos without quality loss.
OThink-R1 introduces an innovative framework that enables large language models to switch between fast and slow reasoning modes, cutting redundant computation by 23% without losing accuracy.
DeepSeek-V3 introduces innovative architecture and hardware co-design strategies that drastically improve efficiency and scalability in large language models, making high-performance AI more accessible.
New research shows that specialized reasoning models combined with efficient inference-time scaling methods like majority voting outperform non-reasoning models in complex tasks, offering insights into optimizing computational resources.