Wave-Coherence Scoring: Phase-Aware Alternative to Cosine Similarity

Kerr Engine — Pure Rust training engine for Kerr-ODE transformers (3x faster than PyTorch at 128-dim, full GPU via WGSL)

Released the training engine for the Wave Coherence project as a standalone repo under Apache 2.0.

What it is: a specialised Rust engine for training and running Kerr-ODE transformers — the architecture that replaces dense MLP layers with physics-inspired wave propagation (98.1% of MLP at 44% of parameters).

Key numbers:

• 3x faster than PyTorch+CUDA at 128-dim, on CPU alone, GPU off

• Full GPU backward pass at 768-dim via 13 WGSL compute shaders (NVIDIA, AMD, Intel, Apple — no CUDA dependency)

• Hand-derived analytical gradients verified against PyTorch autograd (max diff 7.63e-6)

• 6,500 lines of Rust, 4 dependencies, cargo build --release and you’re done

The WGSL backward pass shaders (attention backward, batched outer product, batched linear backward) appear to be the first open-source implementations of ML training backward passes in WGSL.

Repo: GitHub - atech-hub/kerr-engine · GitHub Parent project: GitHub - atech-hub/Wave-Coherence-as-a-Computational-Primitive: Harmonic coherence as a universal relationship detection operator · GitHub

Note: The Kerr-ODE is a novel architecture — it doesn’t work with LM Studio, Ollama, or llama.cpp today. The engine trains and runs inference natively. Ecosystem connector patterns are documented and published as prior art (Pattern 68) for anyone who wants to build bridges.

Built with AI collaboration (Claude Desktop + Claude Code). Stated openly.