@inproceedings{bu-etal-2026-dud,
title = "{DUD}: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models",
author = "Bu, Yixin and
Xia, Runze and
Zou, Guanyun and
Ji, Yupeng and
Dai, Hongliang and
Liu, Haodong and
Li, Piji",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1665/",
pages = "35972--35993",
ISBN = "979-8-89176-390-6",
abstract = "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 **D**ecoupled **U**pdate **D**ynamics (**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."
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<abstract>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 **D**ecoupled **U**pdate **D**ynamics (**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.</abstract>
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%0 Conference Proceedings
%T DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models
%A Bu, Yixin
%A Xia, Runze
%A Zou, Guanyun
%A Ji, Yupeng
%A Dai, Hongliang
%A Liu, Haodong
%A Li, Piji
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F bu-etal-2026-dud
%X 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 **D**ecoupled **U**pdate **D**ynamics (**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.
%U https://aclanthology.org/2026.acl-long.1665/
%P 35972-35993
Markdown (Informal)
[DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models](https://aclanthology.org/2026.acl-long.1665/) (Bu et al., ACL 2026)
ACL
- Yixin Bu, Runze Xia, Guanyun Zou, Yupeng Ji, Hongliang Dai, Haodong Liu, and Piji Li. 2026. DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 35972–35993, San Diego, California, United States. Association for Computational Linguistics.