Self-attention mechanisms in Transformers typically incur costs that increase with context length, leading to higher demands for storage, compute, and energy. A new method using symmetry-aware Taylor approximation aims to maintain constant cost per token for self-attention, potentially alleviating these resource demands.
arxiv.org
2 min
2/4/2026
Self-attention mechanisms in Transformers typically incur costs that increase with context length, leading to higher demands for storage, compute, and energy. A new method using symmetry-aware Taylor approximation aims to maintain constant cost per token for self-attention, potentially alleviating these resource demands.
arxiv.org
2 min
2/4/2026
Self-attention mechanisms in Transformers typically incur costs that increase with context length, leading to higher demands for storage, compute, and energy. A new method using symmetry-aware Taylor approximation aims to maintain constant cost per token for self-attention, potentially alleviating these resource demands.
arxiv.org
2 min
2/4/2026
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