The evolution of language models has achieved remarkable breakthroughs in understanding and generating human-like text, yet a gap persists in managing recursive semantic dependencies that require dynamically layered contextual interpretation. Addressing this limitation, the proposed Dynamic Recursive Framework (DRF) introduces an innovative recursive processing mechanism that adapts context dynamically across layered dependencies, enhancing language models' abilities to handle complex, recursive structures in text. Unlike conventional architectures that rely on static attention mechanisms, the DRF utilizes recursive feedback loops to achieve complex semantic alignment, particularly valuable for tasks requiring the interpretation of long-range dependencies, polysemous disambiguation, and layered meanings. Experimental evaluations reveal that the DRF improves semantic alignment accuracy and contextual consistency, while only minimally increasing computational demands, showing its potential as a significant advancement for tasks demanding recursive contextual comprehension. Implemented on an open-source language model, the DRF's contributions to both performance and interpretive depth illustrate its transformative impact on the structural capabilities of language models, paving the way for models that can process the complexity of human language with enhanced precision and adaptability.