Lévy process-driven stochastic differential equations (SDEs) are crucial for modeling extreme events and heavy-tailed phenomena in fields like finance and climate science. However, Bayesian inference for these SDEs has been hindered by existing methods, which struggle to efficiently capture jumps and heavy tails. Researchers have proposed a novel approach using variational inference and neural tilting to address this challenge1. This method leverages neural networks to approximate the underlying Lévy process, enabling more accurate and efficient inference. By applying this technique, practitioners can better model and predict rare events, ultimately leading to more reliable predictive systems. The development of this approach has significant implications for domains where extreme events have substantial consequences, such as finance and safety-critical AI, so what matters most is that this breakthrough can enhance the robustness of predictive models in these high-stakes fields.