The concept of Semantic Vector Collapse proposes a novel approach to recalibrating vector representations within large-scale transformer models, aiming to mitigate the effects of semantic drift and improve memory retention over extended input sequences. Through the dynamic computation of relevance-weighted projection matrices, the method refines highdimensional embeddings, enhancing their ability to prioritize critical contextual information while suppressing redundancy. Experiments demonstrate significant improvements in sequence coherence and contextual alignment, with marked reductions in entropy observed across varying input lengths. The framework adapts seamlessly to diverse linguistic tasks, achieving notable robustness to noise and variability in input data, as evidenced through qualitative and quantitative analyses. Results from embedding space evaluations indicate optimized dimensionality utilization, enabling efficient memory representation and facilitating scalable applications in real-world scenarios. The method's integration into pre-trained architectures requires minimal modifications, ensuring practical applicability without introducing prohibitive computational overhead. Observations of improved dependency parsing and thematic continuity suggest a profound impact on the model's ability to handle complex semantic relationships across long sequences. Memory retention rates and token-level similarity metrics reflect the transformative potential of the recalibration mechanism in addressing challenges inherent to extended text processing. Additional insights into embedding compression ratios and robustness metrics emphasize the scalability and efficiency of the approach. The mathematical rigor underpinning Semantic Vector Collapse highlights its innovative contribution to advancing semantic representation dynamics within transformer-based language models. Its ability to achieve significant contextual improvements with resourceefficient techniques marks a step forward in the design and functionality of large-scale natural language processing systems.