Hierarchical Contextual Knowledge Transfer in LLMs Using Layered Semantic Projections

Hierarchical semantic representations offer a new approach to addressing challenges in maintaining contextual coherence and logical consistency across extended sequences. The proposed Layered Semantic Projections framework introduces a multi-layered architecture designed to encode and propagate semantic hierarchies effectively, significantly enhancing the reasoning capabilities of language models. Experiments demonstrated improvements in coherence metrics, token diversity, and computational efficiency, surpassing baseline models across diverse linguistic tasks. The novel integration of semantic projection layers enabled robust handling of long-range dependencies, mitigating token repetition and contextual drift. Noise resilience evaluations further highlighted the framework’s adaptability, with consistent performance gains under perturbed input conditions. Cross-domain generalization experiments revealed high semantic transfer efficiency, showcasing the architecture's versatility in adapting to technical, literary, and conversational datasets. A detailed analysis of computational resource utilization demonstrated its scalability, achieving reduced memory requirements without compromising performance. Semantic alignment mechanisms embedded within the architecture demonstrated superior retention of hierarchical contexts, paving the way for more coherent and logically consistent text generation. The results of this study validate the potential of hierarchical design principles as a cornerstone for advancing the state of language model research.