We introduce Dynamic Manifold Evolution Theory (DMET),a unified framework that models large language model generation as a controlled dynamical system evolving on a low_dimensional semantic manifold. By casting latent_state updates as discrete time Euler approximations of continuous dynamics, we map intrinsic energy_driven flows and context_dependent forces onto Transformer components (residual connections, attention, feed-forward networks). Leveraging Lyapunov stability theory We define three empirical metrics (state continuity, clustering quality, topological persistence) that quantitatively link latent_trajectory properties to text fluency, grammaticality, and semantic coherence. Extensive experiments across decoding parameters validate DMET's predictions and yield principled guidelines for balancing creativity and consistency in text generation.
View on arXiv@article{zhang2025_2505.20340,
title={ Dynamic Manifold Evolution Theory: Modeling and Stability Analysis of Latent Representations in Large Language Models },
author={ Yukun Zhang and Qi Dong },
journal={arXiv preprint arXiv:2505.20340},
year={ 2025 }
}