The Adaptive Semantic Framework introduces a modular architecture for large language models, enabling dynamic integration of semantic components to enhance scalability and adaptability across diverse natural language processing tasks. By employing modular semantic units, the framework facilitates efficient knowledge incorporation, resulting in improved performance metrics such as accuracy and inference speed. Empirical evaluations demonstrate the framework's resilience to noisy inputs, maintaining high accuracy levels and showing its robustness for real-world applications. Additionally, the framework's capacity for domain adaptation without extensive retraining highlights its versatility and practical utility. The mathematical foundations of the framework provide a formal basis for modeling semantic modularity and integration, contributing to the theoretical understanding of large language model architectures. Comparative analyses with existing models establish the framework's superiority, setting a new benchmark in the field. The scalability assessment reveals that the framework effectively manages computational demands, making it suitable for deployment in resource-constrained environments. Furthermore, the energy efficiency evaluation indicates a favorable balance between performance and power consumption, aligning with sustainable computing practices. Collectively, these contributions mark a substantial progression in the development of large language models, offering a robust foundation for future innovations in the field.