Wave Field LLM — O(n log n) attention via wave equation dynamics, within 5% of standard transformer

[ Update ] Just built fused Triton kernels for Wave Field LLM v5.

When you build an architecture from scratch, you end up building

everything from scratch.

• Custom attention mechanism (O(n log n) via FFT wave convolution)

• Custom optimizer (Wave optimization)

• Custom KV cache compression (WaveKV filtering)

• Custom Triton kernels (fused scatter-FFT-gather for H100)

• Custom positional encoding (Wave Field pipeline)

None of the existing tools work when your math is fundamentally

different.

Standard transformers use Q·K^T dot products. We use damped wave

propagation through a continuous field. Flash Attention can’t help us

: it optimizes matrix multiplies we don’t do.

So we write our own.

The result: 20x faster than standard attention at 32K context. Runs at 128K where others OOM. 5x less memory.

Building the full stack isn’t a choice — it’s a requirement when

you’re doing something new.

#WaveFieldLLM #AI #DeepLearning #Triton #CUDA #Optimization