Recursive Model Refinement introduces a novel mechanism for iteratively enhancing latent knowledge representation and task-specific adaptability within transformer-based architectures. The framework operates through a structured feedback-driven methodology, enabling the recalibration of internal embeddings to better align with evolving contextual demands. Experiments reveal significant reductions in error rates across complex linguistic tasks, including translation and summarization, through improved contextual coherence and token-level alignment. A mathematical formalization underpins the process, ensuring scalability and computational efficiency without compromising foundational pre-trained capacities. Comparative analyses highlight consistent performance gains relative to baseline methods, with observable benefits in semantic drift mitigation and vocabulary utilization rates across domainspecific benchmarks. The integration of recursive mechanisms into pre-trained models effectively balances generalization and specificity, a critical requirement for diverse natural language applications. Results demonstrate improved stability in latent space organization, reflected in clustering metrics and reduced representational discrepancies. Error analysis provides further evidence of the framework's capacity to systematically address inherent limitations in static representations. Through the use of scalable implementations and well-defined loss convergence patterns, the methodology establishes itself as a robust addition to the field of computational linguistics. The proposed approach holds promise for refining the adaptability of language models through iterative knowledge augmentation. Empirical evidence suggests that the refinements foster not only performance consistency but also enhanced domain alignment. The contributions of this work provide a foundation for advancing the operational and representational dimensions of large-scale language models.