[P] neuropt: LLM-guided hyperparameter optimization that reads your training curves

The problem: You're tuning hyperparameters. Each run takes multiple hours. You have a budget of maybe 15-20 trials before you run out of time or compute. Bayesian optimization picks your next config based entirely on the final validation score, it has no idea your model overfit at epoch 3, or that val loss was flat for 20 epochs before diverging. What neuropt does differently: After each trial, it sends the full per-epoch train/val curves (and any other information you want) to an LLM and asks it to reason about what's happening, then suggest the next config.