Semantic Layer Reconstruction in Large Language Models Using Multi-Level Transformer Feedback Mechanisms

Semantic Layer Reconstruction introduces a transformative mechanism for refining the interactions between transformer layers, leveraging recursive feedback to enhance semantic coherence across linguistic tasks. The framework incorporates a multi-level feedback mechanism that recalibrates token embeddings through bidirectional propagation of semantic cues, addressing challenges such as semantic drift and alignment inefficiencies. By dynamically integrating higher-layer abstractions into earlier computational processes, the approach fosters a more coherent synthesis of token-level and contextual representations. Experimental evaluations reveal significant improvements in perplexity, BLEU scores, and semantic similarity indices, highlighting the framework's ability to address complex linguistic phenomena. Layer-wise analyses underscore the critical contributions of middle layers in achieving optimal semantic alignment, while attention-weight distributions demonstrate a more balanced contextual focus. The recursive feedback mechanism also proved instrumental in capturing long-range dependencies, showcasing its utility in tasks requiring deep contextual understanding. Comparative configurations further validated the adaptability of the mechanism, with dynamic recursive intervals outperforming static approaches across all metrics. Noise-resilience experiments highlighted the model's robustness, while visualizations of semantic drift reduction provided compelling evidence of improved coherence. The integration of the mechanism into existing transformer architectures maintained computational efficiency through optimizations such as sparse attention and modularity. The study bridges theoretical innovations with empirical validation, offering a scalable and adaptable solution for advancing the semantic capabilities of transformer-based architectures. The findings provide a foundation for redefining the paradigms of contextual representation and layer-wise interaction in contemporary language modeling.