The field of causal discovery and graph learning is moving towards addressing complex challenges such as handling latent confounders, cyclic causal relationships, and imbalanced regression tasks. Researchers are developing innovative methods, including variants of existing algorithms and novel frameworks, to improve the accuracy and robustness of causal discovery and graph learning models. These advancements have the potential to enable more reliable and generalizable predictions, and to support applications in various domains, such as education, sociology, and policy modeling. Noteworthy papers in this area include Less Greedy Equivalence Search, which modifies the greedy step to reduce computational cost and improve finite-sample accuracy, and Bivariate Denoising Diffusion, which introduces a novel independence test statistic to handle latent noise introduced by unmeasured mediators. Other notable contributions include the development of Spectral Manifold Harmonization for addressing imbalanced regression challenges, and Relational Causal Discovery with Latent Confounders, which proposes a sound and complete causal discovery algorithm for relational data with latent confounders.