The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle
Transformer models have redefined sequence learning, yet dot-product self-attention introduces a quadratic token-mixing bottleneck for long-context time-series. We introduce the Phasor Transformer block, a phase-native alternative representing sequence states on the unit-circle manifold $S^1$. Each block combines lightweight trainable phase-shifts with parameter-free Discrete Fourier Transform (DFT) token coupling, achieving global $\mathcal{O}(N\log N)$ mixing without explicit attention maps. Stacking these blocks defines the Large Phasor Model (LPM). We validate LPM on autoregressive time-series prediction over synthetic multi-frequency benchmarks against honest baselines: it beats a ze...
| Authors | Dibakar Sigdel |
|---|---|
| Primary category | cs.LG |
| Published | 18 March 2026 |
| Last updated at source | 9 July 2026 |
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