The field of continual learning is moving towards more efficient and effective methods for adapting to new data and tasks. Researchers are exploring new approaches to mitigate catastrophic forgetting and improve the retention of previously learned knowledge. One notable direction is the use of prompt-based methods, which have shown promise in incremental learning settings. Another area of focus is the development of more realistic benchmarks for evaluating incremental learning methods, such as those that capture domain shifts and class expansions. Noteworthy papers include: Towards Efficient Prompt-based Continual Learning in Distributed Medical AI, which proposes a prompt-based continual learning approach for medical AI applications, and RICO: Two Realistic Benchmarks and an In-Depth Analysis for Incremental Learning in Object Detection, which introduces two new benchmarks for evaluating incremental learning methods in object detection. These papers demonstrate significant advancements in the field and highlight the potential for continual learning to enable more efficient and effective machine learning systems.