The field of continual learning is moving towards developing innovative solutions to mitigate catastrophic forgetting in neural networks. Recent research has focused on designing novel architectures and frameworks that can efficiently learn from sequential tasks while maintaining knowledge across tasks. Notable advancements include the integration of Fisher-weighted asymmetric regularization, hippocampal-inspired dual-memory architectures, and synergetic memory rehearsal mechanisms. These approaches have shown significant improvements in reducing task interference and enhancing model adaptability. Noteworthy papers include: Adaptive Variance-Penalized Continual Learning with Fisher Regularization, which presents a novel framework that dynamically modulates regularization intensity according to parameter uncertainty. HiCL: Hippocampal-Inspired Continual Learning, which proposes a biologically grounded yet mathematically principled gating strategy that enables differentiable and scalable task-routing. Escaping Stability-Plasticity Dilemma in Online Continual Learning for Motion Forecasting via Synergetic Memory Rehearsal, which introduces a novel CL method that maintains a compact memory buffer and employs an inequality constraint to limit increments in the average loss over the memory buffer. Complementary Learning System Empowers Online Continual Learning of Vehicle Motion Forecasting in Smart Cities, which introduces Dual-LS, a task-free, online continual learning paradigm that pairs two synergistic memory rehearsal replay mechanisms to accelerate experience retrieval.