Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.07969 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918499919593472 |
|---|---|
| author | Fountzoulas, George |
| author_facet | Fountzoulas, George |
| contents | We present Kathleen, a text classification architecture that operates directly on raw UTF-8 bytes using frequency-domain processing -- requiring no tokenizer, no attention mechanism, and under 470K parameters. Kathleen introduces several novel components: (1) RecurrentOscillatorBanks -- damped sinusoid convolutions with temporal memory for O(L) sequence processing; (2) an FFT-Rotate Wavetable Encoder that maps all 256 byte values using a single learnable vector (256 floats); (3) PhaseHarmonics -- a sinusoidal non-linearity with just 6 learnable phase parameters (+2.6% accuracy, <0.001% of model parameters); (4) Content-Dependent Reverb with Positional Decay Modulation -- a temporal memory mechanism whose decay rate is jointly conditioned on input content and a learned position-indexed bias vector; (5) Token-Level Module Sequencer with consonance and dissonance interference channels. Through iterative architecture evolution from an initial 733K-parameter baseline (Kathleen-Clean) to the current Kathleen-V9 (469K parameters), we demonstrate that pretraining can be entirely eliminated while improving accuracy. Kathleen-V9 achieves 88.5% +/- 0.2% on IMDB, 92.4% +/- 0.2% on AG News, and 85.8% +/- 0.5% on SST-2 (3-seed averages) -- matching or exceeding the pretrained baseline on all benchmarks with 36% fewer parameters. On SST-2, the improvement is +2.5% absolute over the pretrained predecessor. Kathleen processes sequences in O(L) time and memory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07969 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Kathleen: Oscillator-Based Byte-Level Text Classification Without Tokenization or Attention Fountzoulas, George Computation and Language We present Kathleen, a text classification architecture that operates directly on raw UTF-8 bytes using frequency-domain processing -- requiring no tokenizer, no attention mechanism, and under 470K parameters. Kathleen introduces several novel components: (1) RecurrentOscillatorBanks -- damped sinusoid convolutions with temporal memory for O(L) sequence processing; (2) an FFT-Rotate Wavetable Encoder that maps all 256 byte values using a single learnable vector (256 floats); (3) PhaseHarmonics -- a sinusoidal non-linearity with just 6 learnable phase parameters (+2.6% accuracy, <0.001% of model parameters); (4) Content-Dependent Reverb with Positional Decay Modulation -- a temporal memory mechanism whose decay rate is jointly conditioned on input content and a learned position-indexed bias vector; (5) Token-Level Module Sequencer with consonance and dissonance interference channels. Through iterative architecture evolution from an initial 733K-parameter baseline (Kathleen-Clean) to the current Kathleen-V9 (469K parameters), we demonstrate that pretraining can be entirely eliminated while improving accuracy. Kathleen-V9 achieves 88.5% +/- 0.2% on IMDB, 92.4% +/- 0.2% on AG News, and 85.8% +/- 0.5% on SST-2 (3-seed averages) -- matching or exceeding the pretrained baseline on all benchmarks with 36% fewer parameters. On SST-2, the improvement is +2.5% absolute over the pretrained predecessor. Kathleen processes sequences in O(L) time and memory. |
| title | Kathleen: Oscillator-Based Byte-Level Text Classification Without Tokenization or Attention |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.07969 |