Contextually Self-Referential Large Language Models: A Computational Approach to Recursive Internal State Refinement

The recursive refinement of internal states has emerged as a potential mechanism for enhancing coherence and stability in generated text. A self-referential framework was introduced to allow LLMs to iteratively adjust their hidden representations through recursive feedback mechanisms, modifying token distributions based on prior activations. Comparative evaluations demonstrated that incorporating self-referential processing led to improved contextual retention, reduced divergence in attention weight distributions, and increased structural complexity in generated sequences. Computational efficiency analyses highlighted the trade-offs introduced through recursive inference, where the increased depth of refinement resulted in a measurable rise in processing overhead. Experimental results indicated that the activation dynamics within CSLMs exhibited structured adaptation over time, allowing for more stable output generation across long-form text synthesis tasks. A systematic assessment of entropy variability suggested that self-referential processing contributed to constrained probabilistic distributions, reinforcing stability in iterative refinement cycles. The findings highlighted the role of self-referential mechanisms in modulating LLM behavior, demonstrating how recursive adjustments influence token selection strategies, sequence coherence, and computational feasibility. Despite the improvements observed, the study emphasized the need for balancing recursive complexity with real-world applicability, particularly in domains requiring efficient yet contextually reliable text generation.