## Introduction Continual learning is a technique that enables models to adapt to new information and improve their performance. However, spurious forgetting can represent an obstacle to its effectiveness. A recent study arXiv:2512.20634v1 discovered that spurious forgetting is a fundamental cause of poor model performance. ## Framework Proposal The new framework proposal provides the first quantitative characterization of alignment depth as the cause of spurious forgetting. The proposal introduces the concept of 'shallow versus deep alignment', which provides the first quantitative characterization of alignment. ## Solution Proposals The framework proposal offers a series of solution proposals to avoid spurious forgetting: * Real-time detection methods for identifying shallow alignment during training * Specialized analysis tools for visualization and recovery prediction * Adaptive mitigation strategies that distinguish between types of forgetting and promote deep alignment. ## Experiments Experiments conducted on multiple datasets and model architectures (Qwen2.5-3B to Qwen2.5-32B) demonstrated an accuracy rate of 86.2-90.6%. ## Conclusion The framework proposal offers an innovative solution to avoid spurious forgetting in continual learning. Promoting deep alignment can improve the robustness of models against forgetting.