Latent Convergence Modulation in Large Language Models: A Novel Approach to Iterative Contextual Realignment

TL;DR

This paper introduces a structured latent modulation mechanism for transformer-based language models that improves stability, coherence, and contextual alignment in long-form text generation without significant computational overhead.

Contribution

A novel structured modulation framework for latent states in transformers that enhances stability and coherence in autoregressive language models.

Findings

Reduced perplexity fluctuations and lexical instability.

Smoother gradient propagation and optimization pathways.

Improved long-form text coherence and contextual alignment.

Abstract

Token prediction stability remains a challenge in autoregressive generative models, where minor variations in early inference steps often lead to significant semantic drift over extended sequences. A structured modulation mechanism was introduced to regulate hidden state transitions, ensuring that latent representation trajectories remain aligned with prior contextual dependencies while preserving generative flexibility. The modulation framework was designed to function within transformer-based architectures, dynamically constraining representation evolution without imposing external memory dependencies or extensive architectural modifications. Empirical evaluations demonstrated that structured latent adjustments contributed to reductions in perplexity fluctuations, entropy variance, and lexical instability, improving coherence in long-form text generation. Gradient propagation stability was further analyzed, revealing that the modulation process led to smoother optimization pathways, mitigating erratic fluctuations in weight updates across successive inference steps. The computational efficiency of the modulation process was assessed, showing that its integration within transformer-based architectures introduced only marginal overhead while maintaining compatibility with existing optimization frameworks. The structured modulation constraints also influenced syntactic variation, preventing excessive repetition while maintaining balanced sentence length distributions. Comparative evaluations against baseline models reinforced the role of controlled latent state evolution in improving pronoun resolution, logical consistency, and contextual alignment across autoregressive text generation tasks.