In-context learning gained significant attention for its ability to allow language models to adapt to new tasks without the need for explicit retraining. Despite this capability, the precise mechanisms through which in-context examples influence model behavior remain inadequately explored, particularly in terms of how they interact with retrieval-based processes. The study presents a novel approach through the enhancement of GPT-Neo, integrating progressive recursive token regression to better understand the dynamic interplay between learning from context and retrieving pre-existing knowledge. Experimental results demonstrated that the model exhibits a marked improvement in token regression accuracy when leveraging complex in-context examples, suggesting an inherent capacity to balance learning and retrieval for optimal performance. The architectural modifications, including specialized attention mechanisms and recursive feedback loops, were shown to contribute to more complex and contextually relevant text generation, albeit with certain computational trade-offs. This research contributes to a deeper understanding of how in-context learning and retrieval can be optimized, paving the way for advancements in contextually adaptive language model applications.