The field of uncertainty quantification and robustness is rapidly advancing, with a focus on developing innovative methods for capturing and quantifying uncertainty in various computational and physical systems. Recent developments have centered around creating robust and efficient frameworks for uncertainty quantification, including the use of quantale-valued metric spaces and conformal prediction. These approaches have shown promise in providing a foundation for quantitative reasoning about imprecision and robustness in a wide range of applications. Notably, researchers have made significant progress in designing truthful calibration measures, which is essential for reliable interpretation of predictions. Furthermore, the development of novel visualization methods, such as uncertainty tubes, has improved the ability to effectively communicate uncertainty in predictions. Noteworthy papers include: A Perfectly Truthful Calibration Measure, which introduces a perfectly truthful calibration measure in the batch setting. TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML, which presents a single-pass, label-free uncertainty monitor for streaming TinyML. SNAP-UQ: Self-supervised Next-Activation Prediction for Single-Pass Uncertainty in TinyML, which proposes a single-pass, label-free uncertainty method for TinyML that estimates risk from depth-wise next-activation prediction.