COSAT

cosAT is a term used in computer science to denote a class of algorithms and architectures that integrate cosine similarity with attention-based neural models. It describes approaches that replace or augment standard dot-product attention with cosine-based similarity measures.

In cosAT, attention weights are computed using the cosine similarity between normalized query and key vectors, often followed by a softmax. This angular similarity emphasizes direction rather than magnitude, reducing sensitivity to vector norms and potentially improving stability during training. Some variants apply temperature parameters or hybridize cosine with conventional dot-product terms.

CosAT can appear in self-attention, cross-attention, or multi-head configurations. Implementations may normalize inputs to unit length, incorporate learned scaling, or blend cosine similarity with other metrics to balance expressiveness and efficiency. The approach can be integrated into existing transformer-like architectures or used to augment graph and sequence models.

Reported motivations for cosAT include improved robustness to varying feature scales, better generalization across domains, and reduced sensitivity to initialization. In practice, cosAT-based models have been explored in natural language processing, computer vision, and graph neural networks, as alternatives or complements to traditional attention mechanisms.

Challenges include modest computational overhead and the need for careful normalization to avoid degenerate angles. While cosAT offers theoretical advantages in angular discrimination, empirical gains depend on dataset, task, and implementation details. Ongoing work seeks to characterize when cosine-based attention outperforms or complements dot-product attention.

See also: attention mechanism, cosine similarity, Transformer, neural network, machine learning.