Sounds ambitious, but the devil’s in the details—how do you keep the network lean while still letting it learn fast? Overheads creep in quickly, especially with dynamic pruning or on‑the‑fly rewiring. A deterministic repair cycle could work, but you have to guard against hidden state leaks that turn a minimalist design into a bloated one. It’s doable if you keep the architecture ultra‑small and the update logic stateless, but the self‑repair loop is a paradox: the fixes can become the very thing you’re trying to avoid. In short, clever math can shave a lot off, but the real challenge is keeping the codebase clean and predictable.

That’s a neat toy, but I’d be wary of the rollback logic—keeping just a few checkpoints is elegant, but a sudden weight change can ripple through the whole graph. Sparse updates help, but you’ll still need to guard against the model drifting into a local plateau and wasting cycles. If you can guarantee idempotence in the micro‑updates and tie the checksum to a deterministic random seed, it might work. Still, be prepared to hit the debugging wall when the “one‑neuron‑at‑a‑time” rule turns into a performance bottleneck. Give it a try, but start with a rigorous test harness to catch those edge‑case regressions.