Large language models (LLMs) demonstrate remarkable capabilities in various complex language tasks, yet they face significant reliability challenges, including factual inaccuracies and generated biases. Uncertainty quantification (UQ) plays a pivotal role in assessing model trustworthiness, particularly for high-stakes applications. However, current UQ methods for LLMs encounter computational efficiency bottlenecks due to their reliance on extensive sampling or external model invocations. In this work, we introduce a novel, sampling-free uncertainty quantification framework centered on hidden layer representation analysis. Our method facilitates real-time uncertainty quantification by modeling hierarchical internal semantic dynamics during the generation process. Through comprehensive experiments on multiple QA datasets and diverse model scales, we show that our approach consistently outperforms existing uncertainty quantification techniques in distinguishing correct from incorrect generations. Our results reveal that analyzing the dynamic evolution of hidden states provides a potent and computationally efficient signal for uncertainty quantification, directly from the model's internal workings, surpassing methods that depend solely on output probabilities or approximations via multiple samples.

Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the model's true epistemic state.While recent mechanistic approaches leverage hidden state dynamics, they typically aggregate residual stream updates, conflating the distinct roles of parametric memory (Feed-Forward Networks) and contextual processing (Attention).We argue that this aggregation obscures fine-grained mechanistic conflicts, such as memory-context misalignment, that are fundamental indicators of uncertainty.To address this, we introduce Decoupled Update Dynamics (DUD), a framework that explicitly decouples FFN and Attention contributions via noise-induced causal interventions.By quantifying the independent restoration capabilities of each module, we construct a dual-stream dynamic profile that captures the model's internal fragility.Extensive experiments demonstrate that DUD significantly outperforms state-of-the-art baselines in both uncertainty estimation and calibration, while exhibiting superior cross-dataset generalization, validating decoupled dynamics as a robust proxy for model faithfulness.