Contextual Cascade Representations (CCR) have been introduced as a transformative framework designed to optimize the scalability and efficiency of large-scale language model architectures. The methodology employs hierarchical parameter activation, dynamically adjusting computational pathways to align with the contextual demands of specific tasks. Experimental findings demonstrated substantial reductions in energy consumption, inference latency, and parameter count, highlighting the framework's potential to achieve computational efficiency without sacrificing accuracy. Through a combination of sequentially weighted pruning mechanisms and dynamic thresholding, the approach maintained linguistic coherence across diverse tasks while ensuring adaptability to domain-specific challenges. Ablation studies demonstrated the synergistic contributions of individual components, including cross-layer coherence mechanisms, which preserved semantic alignment during the pruning process. Results from domain-specific evaluations revealed consistent improvements in accuracy and resource utilization, particularly within medical, legal, and financial datasets, where specialized linguistic structures posed significant challenges. A comprehensive analysis of error distributions further indicated that CCR excelled in mitigating inaccuracies in syntactically and semantically complex tasks. The proposed methodology leveraged context-sensitive parameter selection, enabling robust performance even in high-sparsity regimes. Quantitative metrics, including perplexity, BLEU scores, and classification accuracy, affirmed the advantages of CCR over conventional pruning and compression techniques. Comparisons with baseline architectures highlighted its ability to balance model size and performance more effectively. Additionally, insights from energy consumption analysis emphasized its contribution to environmentally sustainable computational practices. Contextual Cascade Representations exemplify a significant advancement in addressing the challenges of efficiency, adaptability, and performance retention in contemporary language model research.