The field of conformal prediction is moving towards improving the efficiency and reliability of uncertainty quantification methods. Recent developments focus on enhancing the accuracy and robustness of conformal predictors, particularly in scenarios with limited data or non-stationary distributions. Notable advancements include the integration of conformal prediction with other machine learning techniques, such as deep learning and state-space models, to produce more accurate and calibrated uncertainty estimates. Additionally, researchers are exploring new statistical guarantees and methods to improve the reliability of conformal predictors, especially for small datasets. Noteworthy papers include: CLAPS, which presents a posterior-aware conformal regression method that yields narrower prediction intervals, and Conformal Correction for Efficiency May be at Odds with Entropy, which proposes an entropy-constrained conformal correction method to improve efficiency while maintaining entropy.