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

Kerr-ODE: Full Stack Now Live — Train, Serve, Chat

Three updates since the last post:

1. Inference Server Released kerr-server — 640 lines of Rust, OpenAI-compatible API. Load a checkpoint, serve on localhost, connect any chat UI. SSE streaming, bearer token auth, temperature/top-k/top-p sampling.

Tested with LM Studio 0.4.6 via the openai-compat-endpoint plugin. Selected “gpt-4.1” from the model dropdown (the server doesn’t care what name the client sends — it serves whatever checkpoint is loaded). Typed “hello.” A 354K parameter Shakespeare-trained Kerr-ODE model responded with character-level Shakespeare fragments through the GPT-4.1 label. Not exactly GPT-4.1’s output, but the pipeline works end to end.

The full pipeline: train a model with the engine → serve it with kerr-server → chat through LM Studio or any OpenAI-compatible client. No custom clients needed.

2. Engine Performance: 13s → 1.72s at 768-dim Three new WGSL compute shaders (matvec_batch, layer_norm_batch, kerr_step_batch) batch all forward pass operations across positions in single dispatches. Forward pass went from 8 seconds to 500ms — 16x speedup. Total iteration from 13s to 1.72s. 17 shaders total, ~7,200 lines of Rust. GPU at 38%, 49°C, 1.2GB VRAM training a 12M parameter model on an RTX 4070 Ti.

3. BPE Tokenizer Support The engine and server now accept any HuggingFace tokenizer.json via --bpe flag. Qwen, Llama, GPT-2 — borrow their vocabulary, train your own Kerr-ODE model from scratch. No more character-level only.

The stack:

• Wave Coherence — research framework, 68 defensive patents (MIT)

• Kerr Engine — training, 17 WGSL shaders, 3x faster than PyTorch at 128-dim (Apache 2.0)

• Kerr Server — inference, OpenAI-compatible API (Apache 2.0)

All open source. All documented. Contributor targets listed in both READMEs.