Self-Reconfiguring Semantic Lattices for Context Preservation in Large Language Models

A novel architectural framework, Self-Reconfiguring Semantic Lattices, introduces a graph-based approach to addressing the persistent limitations of semantic retention and long-sequence comprehension in transformer models. By employing dynamic restructuring mechanisms, the framework adapts to evolving contexts through real-time adjustments of node importance and relationship weights, ensuring that semantic coherence is preserved even as sequence lengths increase. Empirical evaluations reveal significant improvements in semantic similarity scores and computational scalability, highlighting the framework's ability to manage extensive inputs without overwhelming memory or processing resources. Comparative analyses demonstrate its superiority over baseline models, particularly in handling hierarchical text structures and maintaining contextual alignment across extended narratives. The framework's robustness is further validated through its performance on noisy and domain-diverse datasets, where it effectively mitigates information loss while maintaining efficiency. Its integration into open-source large-scale architectures demonstrates its practical utility and compatibility with existing infrastructures. Experiments indicate that the proposed approach significantly reduces redundant token representations, enhances generalization to unseen domains, and achieves near-linear scalability across multiple processing units. With its potential to redefine the boundaries of long-sequence processing, the methodology contributes to both theoretical advancements and practical innovations in semantic modeling and contextual reasoning. The results demonstrate the critical role of adaptive semantic frameworks in driving the next generation of transformer-based systems.