Abstract

The need for robust context retention mechanisms has become paramount, particularly as language models are increasingly applied to complex, multi-turn interactions and extended texts requiring coherent sequence management. Contextual Layer Fusion (CLF) introduces an innovative approach by integrating multi-layer contextual information directly into the model's architecture, enabling more effective weighting of relevant information across layers. CLF's design addresses limitations in traditional transformer-based models, where long-term dependencies are often weakened, by introducing a dynamic fusion mechanism that adjusts contextual weighting, thereby enhancing the model's comprehension and response coherence in extended sequences. Through rigorous evaluation across various metrics, CLF has shown quantifiable improvements in context retention, sequence coherence, and computational efficiency, demonstrating its potential as a viable solution for real-world applications that demand sustained engagement and contextual awareness. Additionally, comparative analyses indicate that CLF maintains computational efficiency advantages over more resource-intensive memory-augmented architectures, showing its practical applicability in diverse deployment scenarios. As a novel architectural enhancement, CLF has significant implications for the design and scalability of future language models, offering a foundation for more contextually adaptive and efficient natural language systems.