AI RESEARCH
RubiConv -- Efficient Boundary-Respecting Convolutions
arXiv CS.LG
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ArXi:2605.08451v1 Announce Type: new Convolutional architectures have emerged as powerful alternatives to Transformers for sequence modeling. The primary advantage is that they offer improved theoretical sequence length complexity by leveraging the Fast Fourier Transform (FFT). However, this theoretical improvement does not always meaningfully land in practice. One critical obstacle is that applying standard FFTs is not amenable to the large-scale