The field of autonomous driving is moving towards more sophisticated and nuanced approaches to navigation and decision-making. Researchers are exploring new methods to improve the performance of autonomous vehicles in complex and dynamic environments, such as urban driving and long-tail scenarios. One notable trend is the development of multimodal learning approaches, which can capture the diversity of possible actions and behaviors in different scenarios. Another area of focus is the use of counterfactual reasoning and negative data to improve the calibration and safety of autonomous driving systems. Noteworthy papers in this area include: CoReVLA, which proposes a dual-stage end-to-end autonomous driving framework that improves performance in long-tail scenarios through a process of data collection and behavior refinement. ReflectDrive, which introduces a novel learning-based framework that integrates a reflection mechanism for safe trajectory generation via discrete diffusion. AnchDrive, which proposes a framework for end-to-end driving that effectively bootstraps a diffusion policy to mitigate the high computational cost of traditional generative models.