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Scaled dot-product attention (SDPA) represents one of the most influential advancements in the field of artificial intelligence and machine learning, serving as the bedrock of transformer architectures that have fundamentally reshaped how we approach sequence modeling, representation learning, and multimodal integration. First unveiled in the landmark 2017 paper Attention is All You Need by Ashish Vaswani and his collaborators at Google, SDPA has propelled the development of models capable of achieving superhuman performance in tasks ranging from natural language understanding to protein folding prediction.

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Scaled Dot-Product Attention Core—Sliding Window and Grouped Query Attention—The Core Behind All Transformer Models

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  • 发表日期：

    2026年

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  • Dilyan Grigorov

摘要

Scaled dot-product attention (SDPA) represents one of the most influential advancements in the field of artificial intelligence and machine learning, serving as the bedrock of transformer architectures that have fundamentally reshaped how we approach sequence modeling, representation learning, and multimodal integration. First unveiled in the landmark 2017 paper Attention is All You Need by Ashish Vaswani and his collaborators at Google, SDPA has propelled the development of models capable of achieving superhuman performance in tasks ranging from natural language understanding to protein folding prediction.