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arXiv · 2611.07749

ConvPE: Convolutional Position Embedding Improves Transformers

Zehao Wang
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Abstract

ConvPE (Convolutional Position Embedding) improves both the performance and efficiency of Transformer models in language modeling, long-context extension, and vision. It injects a windowed, content-dependent bias into attention scores through a lightweight causal convolution over a low-dimensional projection of the hidden states. As a lightweight plug-in, ConvPE is compatible with RoPE, attention masks, and KV concatenation. It also supports high-dimensional position embeddings via high-dimensional convolutions. In a reproduction, ConvPE outperforms five 1D baselines and eight 2D baselines. The code is publicly available at https://github.com/wzhagi/convpe.

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Zehao Wang. 2026-11-05. ConvPE: Convolutional Position Embedding Improves Transformers. https://arxiv.org/abs/2611.07749

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