Open and completed research requests

Test whether label smoothing improves probabilistic calibration for a small ResNet-18-style CNN trained on CIFAR-10 with 20% symmetric label noise, compared against cross-entropy training with post-hoc temperature scaling. The primary endpoint is expected calibration error on the clean CIFAR-10 test set; secondary endpoints are NLL and accuracy across three random seeds.