QUEST: A robust attention formulation using query-modulated spherical attention
The Transformer model architecture has become one of the most widely used in deep learning and the attention mechanism is at its core. The standard attention formulation uses a softmax operation applied to a scaled dot product between query and key vectors. We explore the role played by norms of the queries and keys, which can cause training instabilities when they arbitrarily increase. We demonstrate how this can happen even in simple Transformer models, in the presence of easy-to-learn spurious patterns in the data. We propose a new attention formulation, QUEry-modulated Spherical aTtention (QUEST), that constrains the keys to a hyperspherical latent space, while still allowing individu...
| Authors | Hariprasath Govindarajan, Per Sidén, Jacob Roll et al. |
|---|---|
| Primary category | cs.LG |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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