# CNet preprint reports fewer training steps for complex-valued language model

_Published Friday, October 16, 2026 at 8:12 AM EDT · Science, AI · Latest · Tier 2 — Notable_

Researchers report that a complex-valued language model built with their CNet framework matched a real-valued comparison model on character-level tiny-shakespeare in under half the training steps. The preprint introduces a C++/CUDA framework for neural networks that retain phase information in complex-valued data.

Its language model uses a causal Fourier mixer, which combines information from preceding tokens. A separate attention mechanism assigns weights using squared magnitudes rather than softmax and reaches 1.52 nats/char, matching softmax self-attention. The training-step comparison is against a parameter-matched real causal FNet on a character-level task; it does not establish equivalent savings for large-scale language-model training.

## Sources

- [cs.LG updates on arXiv.org](https://arxiv.org/abs/2610.08592)

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Canonical: https://techandbusiness.org/newswire/gDg5BNpUhxvV_TPEo6pdeN
Published: 2026-10-16T12:12:49.041Z
Story chronology: 2026-10-16T04:00:00.000Z
Retrieved: 2026-10-16T15:27:34.930Z
Publisher: Tech & Business (techandbusiness.org)