@Samuelm

I got frustrated that LLMs can explain a mistake without actually learning from it, while retraining is expensive and static. SL-LLM-R tests a controlled loop: snapshot failures, replay counterfactual repairs, verify what actually worked, and only later allow bounded reversible updates. The public repo now contains the pinned model runtime, experiment history, and a live Learning Lab.