Structured Context Recomposition (SCR) introduces a breakthrough approach that helps large language models maintain coherence over extended text generation.

• Uses probabilistic layer realignment to dynamically restructure how models process context
• Eliminates the trade-offs between inference latency and storage overhead seen in other approaches
• Maintains contextual consistency even with extremely long inputs
• Particularly valuable for engineering applications requiring extended text generation with coherent context

This innovation addresses a fundamental limitation in conventional self-attention mechanisms, offering a more efficient solution for long-range dependencies in language models.

Structured Context Recomposition for Large Language Models Using Probabilistic Layer Realignment