Caracal: Causal Architecture via Spectral Mixing

• URL: http://arxiv.org/abs/2605.00292v2
• Date: Thu, 07 May 2026 01:55:25 GMT
• Title: Caracal: Causal Architecture via Spectral Mixing
• Abstract summary: Caracal is a novel architecture that replaces attention with a parameter-efficient, O(L log(L)) Multi-Head Fourier (MHF) module.<n>We leverage the Fast Fourier Transform (FFT) for sequence mixing, inherently addressing both bottlenecks.<n>Unlike efficient models relying on hardware-specific implementations, we uses standard library operators.
• Score: 11.904784955484857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The scalability of Large Language Models to long sequences is hindered by the quadratic cost of attention and the limitations of positional encodings. To address these, we introduce Caracal, a novel architecture that replaces attention with a parameter-efficient, O(L log(L)) Multi-Head Fourier (MHF) module. Our contributions are threefold: (1) We leverage the Fast Fourier Transform (FFT) for sequence mixing, inherently addressing both bottlenecks mentioned above. (2) We apply a frequency-domain causal masking technique that enforces autoregressive capabilities via asymmetric padding and truncation, overcoming a critical barrier for Fourier-based generative models. (3) Unlike efficient models relying on hardware-specific implementations (e.g., Mamba), we uses standard library operators. This ensures robust portability, eliminating common deployment barriers. Evaluations demonstrate that Caracal performs competitively with Transformer and SSM baselines, offering a scalable and simple pathway for efficient long-sequence modeling. Code is available in Appendix.

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