Neuro-Genesis Engine

Neuro-Genesis Engine is a self-expanding Mixture-of-Experts network that autonomously repairs its own architecture, live, during training. Think of a hospital that hires its own specialists: when a patient shows up that nobody on staff can treat, the hospital doesn't wait for HR — it writes the job spec, interviews the candidate, verifies credentials, and puts them to work the same shift. Neuro-Genesis Engine does exactly that for a neural network. When training loss spikes — a sign the network hit something it can't handle — a local LLM, Gemma-2-2b-it, writes the actual PyTorch source code for a new expert module. That code is statically screened, sandboxed, smoke-tested, and hot-swapped into the live network without stopping training. If a candidate is rejected, the exact rejection reason is fed straight back to the model, which corrects its own mistake and retries. No human is involved at any point. This isn't a simulation or a toy demo. The system passes 44 tests, has been through three independent rounds of adversarial review, and survives a genuine OS-level Ctrl+C interrupt mid-registration with full checkpoint/resume. It's verified end-to-end on real AMD hardware — a Radeon Pro W7900D running ROCm 7.2 — with Gemma-2-2b-it running entirely on-device, no external API calls. Every long-lived model deployment eventually meets data it wasn't built for — perception systems drift, fraud patterns shift, edge devices see conditions nobody trained for. Today, fixing that means a human notices, a retrain gets scheduled, and a new version ships days or weeks later. Neuro-Genesis Engine is a step toward infrastructure that repairs a model's blind spots the moment they appear, instead of waiting on a human in the loop. A live, interactive visualizer replaying a real training run — showing the network growing from 4 to 11 experts across 7 spike-detect-generate-register cycles — is hosted at neuro-genesis-engine.vercel.app.