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README.md

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 # WaveletLM
 
-A fully causal, attention-free language model that mixes tokens through learned lifting wavelet decomposition, FWHT, per-scale gated spectral mixing, inverse FWHT, and wavelet reconstruction. Combined with expanded MLPs and sparse product-key memory, this yields an architecture with no attention and O(n log n) scaling in sequence length.
+WaveletLM is a fully causal, attention-free language model that mixes tokens through learned lifting wavelet decomposition, a Fast Walsh-Hadamard Transform, per-scale gated spectral mixing with SwiGLU activation, an inverse FWHT, and wavelet reconstruction. Combined with expanded MLPs and sparse product-key memory, this yields an architecture with no attention and O(n log n) scaling in sequence length.
 
 **Full code, training details, ablations, and documentation:** 
 [github.com/ramongougis/WaveletLM](https://github.com/ramongougis/WaveletLM)