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

arXiv: https://arxiv.org/abs/2502.06302 · HTML: https://mlsanity.com/paper/2502.06302/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 27 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 1 of 1)

- [Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence](https://mlsanity.com/paper/2402.09880/) (2024)

## Built on by (most cited first, 2 of 2)

- [Mind the Generation Process: Fine-Grained Confidence Estimation During LLM Generation](https://mlsanity.com/paper/2508.12040/) (2025)
- [Gated Differentiable Working Memory for Long-Context Language Modeling](https://mlsanity.com/paper/2601.12906/) (2026)

## Abstract (arXiv)

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.