Spectral-Window Hybrid (SWH)
This addresses the problem of scaling sequence modeling for long-horizon tasks in machine learning, representing an incremental improvement over existing methods.
The paper tackles the computational inefficiency of Transformers for long sequences by proposing the Spectral-Window Hybrid (SWH) architecture, which matches Transformer perplexity on short contexts and scales linearly to extended sequences.
Scaling sequence modeling to extreme contexts requires balancing computational efficiency with representational expressivity. While Transformers provide precise retrieval via the attention mechanism, their quadratic $\mathcal{O}(T^2)$ complexity limits their application to long-horizon tasks. In this work, we propose the \textbf{Spectral-Window Hybrid (SWH)}, an architecture that decouples sequence modeling into two \textit{parallel} streams: a global branch utilizing the Convolution Theorem to model long-range decay dynamics in $\mathcal{O}(T \log T)$ time, and a local branch employing sliding-window attention for token interactions within a bounded context. By aggregating these representations, SWH avoids the computational bottleneck of global attention while retaining local precision. We demonstrate that SWH matches the perplexity of standard Transformers on short contexts while enabling efficient linear scaling to extended sequences. The code is available at https://github.com/VladimerKhasia/SWH