Framework for Semantic Compression and Reconstruction in Large Language Models Using Layered Contextual Pruning

Compression techniques within deep transformer architectures have become an essential area of exploration to address growing computational demands while preserving high-quality generative outputs. By leveraging structured pruning methodologies and advanced reconstruction frameworks, the proposed approach reduces model complexity without compromising contextual and semantic fidelity. Iterative pruning cycles, combined with fine-tuning and attention redistribution mechanisms, ensure that linguistic coherence is maintained across varying compression levels. Results demonstrate significant gains in computational efficiency, with noticeable reductions in memory usage and inference latency, accompanied by relatively minimal degradation in task-specific accuracy. The integration of gradient alignment and multi-layer aggregation techniques provides a robust pathway for re-establishing disrupted contextual dependencies, ensuring adaptability in domain-specific tasks. Quantitative evaluations highlight the versatility of the proposed framework, as performance stability is observed across both general and specialized linguistic challenges. Further insights reveal that task complexity and noise levels significantly influence the trade-offs between compression ratios and generative quality. The innovative use of auxiliary loss functions and dynamic parameter adjustments underscores the importance of flexibility within compressed models. Energy efficiency evaluations highlight the broader implications of the methodology for sustainable AI deployments, particularly in resource-constrained environments. Together, the findings offer a comprehensive blueprint for enhancing the scalability and practicality of transformer-based architectures in modern computational linguistics.