Stochastic Token Permutation in Large Language Models for Controlled Contextual Perturbation

Token sequences play a fundamental role in structuring linguistic representations, influencing how predictive models process contextual dependencies to generate coherent outputs. Stochastic Token Permutation (STP) introduces a controlled reordering mechanism that perturbs input sequences in a probabilistic yet structured manner, enabling an analysis of how alterations in token positioning affect language model performance. Variations in token order modify attention weight distributions, prompting models to adjust predictive probabilities dynamically, thereby revealing the extent to which sequential structures contribute to contextual coherence. Empirical evaluations demonstrate that different permutation strategies induce distinct patterns of response variation, where entropy-aware perturbations retain linguistic fluency more effectively than purely stochastic reordering, while hybrid approaches balance flexibility with contextual retention. Comparative analysis against baseline models establishes that structured permutations reduce perplexity and enhance robustness under certain perturbation thresholds, contributing to a refined understanding of how token-level modifications impact response stability. Statistical assessments confirm that shifts in predictive behavior arise from the structured perturbation process rather than stochastic variance, underscoring the role of controlled modifications in evaluating contextual robustness. Computational overhead remains a consideration, with entropy-based reordering incurring greater latency compared to frequency-informed permutations, suggesting that trade-offs between efficiency and flexibility warrant further examination. The findings highlight the necessity of refining token-level perturbation techniques to advance the study of structured linguistic variability in predictive modeling.