CAWN: Continuous Acoustic Wave Networks for Autoregressive Language Modeling
Modern Large Language Models (LLMs) rely on Transformer self-attention, which scales quadratically with sequence length. Recent linear-time alternatives, like State Space Models (SSMs), often suffer from signal degradation over extended contexts. We introduce the Continuous Acoustic Wave Network (CAWN), a fully continuous sequence-mixing architecture. Instead of discrete matrix-based attention, CAWN projects hidden states into multi-headed complex-domain phasors, achieving sequence mixing through a causal, $O(L)$ Phase Accumulation mechanism. To prevent signal degradation over ultra-long contexts, we introduce a dual-gated Selective Phase Resonance mechanism incorporating Frequency-Depend...
| Authors | Dejan Čugalj, Aleksandar Jevremovic |
|---|---|
| Primary category | cs.CL |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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