Fundamentally, these two approaches actually follow the same core logic. They both address one issue: for a model to achieve long-term memory coherence and stable understanding, relying solely on fixed context windows and weight storage is not enough. This limitation determines the ceiling of current architectures. In other words, true "understanding" needs to go beyond the constraints of the model's parameters — this is the fundamental challenge that AI architecture design must solve.