The article addresses the importance of tracking solutions for time-varying variational inequalities, a topic relevant to areas such as game theory, optimization, and machine learning (F1, F2). It highlights that existing research has primarily focused on time-varying games and optimization problems (F3). Within this context, the article notes that strong convexity and monotonicity conditions can facilitate effective tracking guarantees, provided that the variations in the underlying problems are appropriately controlled (F4). This emphasis on controlled variations aligns with ongoing studies in related fields, underscoring the practical significance of these mathematical properties for ensuring solution stability over time. The discussion contributes to a broader understanding of how dynamic problem settings can be managed in computational and theoretical frameworks. Overall, the article situates its analysis within a well-established research trajectory, reinforcing the critical role of structural conditions in tracking solutions for evolving variational inequalities.