Probabilistic State Propagation in Large Language Models through Recursive Token Reconfiguration

Structured inference processes in text generation have traditionally relied on autoregressive token prediction mechanisms, where each token is selected based on prior context without subsequent refinement. Recursive Token Reconfiguration (RTR) introduces an iterative inference strategy that enables probabilistic token realignment through recursive state propagation, ensuring that generated sequences remain dynamically responsive to evolving contextual dependencies. The experimental evaluation compared RTR-enhanced inference with conventional autoregressive decoding, assessing its impact on fluency, semantic similarity, and structural coherence across diverse textual contexts. Recursive probability adjustments contributed to reducing exposure bias effects, ensuring that earlier token predictions remained adaptable rather than statically determined. Computational analyses examined the trade-offs between enhanced coherence and increased inference latency, revealing that structured recursion improved text consistency while maintaining feasible processing constraints. Empirical findings demonstrated that recursive inference stabilized token probability distributions, ensuring that probability evolution followed systematic adjustment patterns rather than introducing stochastic variability. The probabilistic state propagation mechanism enabled token selection refinements to be guided through structured reconfiguration constraints, ensuring that modifications remained linguistically coherent rather than arbitrarily adjusted. The introduction of RTR provided a framework for structured token evolution without requiring modifications to pre-trained model parameters, ensuring that iterative inference remained computationally feasible within existing architectures.