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arXiv · 2604.24878

Transformer Approximations from ReLUs

Jerry Yao-Chieh Hu·Mingcheng Lu·Yi-Chen Lee·Han Liu
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Abstract

We provide a systematic recipe for translating ReLU approximation results to softmax attention mechanism. This recipe covers many common approximation targets. Importantly, it yields target-specific, economic resource bounds beyond universal approximation statements. We showcase the recipe on multiplication, reciprocal computation, and min/max primitives. These results provide new analytical tools for analyzing softmax transformer models.

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Jerry Yao-Chieh Hu, Mingcheng Lu, Yi-Chen Lee, Han Liu. 2026-04-27. Transformer Approximations from ReLUs. https://arxiv.org/abs/2604.24878

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