Semantic Distillation Through Recursive Neural Contextualisation in Large Language Models

Achieving coherent and semantically consistent outputs remains a significant challenge in extended text generation tasks, where models often struggle with contextual retention over long sequences. Recursive neural contextualisation offers a groundbreaking approach that refines the internal representations of large language models through iterative feedback and dynamic memory enhancements. The framework integrates recursive feedback pathways, adaptive gating mechanisms, and hierarchical positional embeddings, enabling a more effective alignment of semantic relationships across varied linguistic structures. Quantitative results highlight substantial improvements in semantic alignment scores and contextual coherence, with qualitative analyses further illustrating the framework’s capacity to preserve logical consistency in complex, multi-turn interactions. Layer-specific evaluations reveal that deeper layers play a pivotal role in consolidating long-term dependencies, while multilingual tests confirm the adaptability of the approach across languages and syntactic variances. Memory efficiency assessments demonstrate that the recursive architecture scales effectively, maintaining practicality for real-world applications with constrained resources. The modular nature of the proposed modifications ensures seamless compatibility with existing transformer architectures, fostering opportunities for widespread adoption. By addressing the core limitations of traditional attention mechanisms, recursive neural contextualisation establishes a pathway for advancing the coherence and adaptability of natural language systems. The findings demonstrate the transformative potential of recursive mechanisms in bridging gaps in semantic retention, providing significant implications for future developments in natural language processing.