@inproceedings{miyaji-valdenegro-toro-2026-rethinking,
title = "Rethinking Aleatoric Uncertainty: Partitioning Irreducible States for Large Language Model Agents",
author = "Miyaji, Renato Okabayashi and
Valdenegro-Toro, Matias",
editor = {Aziz, Wilker and
Berant, Jonathan and
Eikema, Bryan and
Ilia, Evgenia and
de Marneffe, Marie-Catherine and
Plank, Barbara and
Shelmanov, Artem and
Swayamdipta, Swabha and
Tiedemann, J{\"o}rg and
Vazhentsev, Artem and
V{\'a}zquez, Ra{\'u}l and
Zerva, Chrysoula},
booktitle = "The Proceedings of the Third Workshop on Uncertainty-Aware {NLP}",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.uncertainlp-main.10/",
pages = "138--161",
ISBN = "979-8-89176-486-6",
abstract = "While Uncertainty Quantification (UQ) remains an active research field for Large Language Models (LLMs), the current literature lacks a rigorous understanding of what constitutes aleatoric uncertainty and its exact sources. Existing methodologies largely rely on the traditional dichotomy of aleatoric and epistemic uncertainty, which inadequately treats uncertainty as a static snapshot. To bridge this gap, this position paper challenges the static treatment of uncertainty. Supported by empirical evidence, we posit that aleatoric uncertainty in LLM agents is fundamentally dynamic and must be partitioned into strictly and temporarily irreducible states{---}differentiating intrinsic task variance, such as multiple valid answers, from resolvable noise, including input ambiguity, typographical errors, and contextual conflict. Through episodic empirical evaluations across multi-turn scenarios, we demonstrate how targeted agentic interventions can actively resolve temporarily irreducible noise, transitioning the interaction into an epistemic regime. When data uncertainty is strictly irreducible, model interventions cannot isolate a single correct distribution. While we acknowledge that the traditional static dichotomy remains a valuable framework for non-agentic settings, we urge the community to develop new decomposition paradigms that track uncertainty along the timeline of interaction. Embracing this dynamic perspective is essential to achieve robust uncertainty quantification for modern LLM agents."
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<abstract>While Uncertainty Quantification (UQ) remains an active research field for Large Language Models (LLMs), the current literature lacks a rigorous understanding of what constitutes aleatoric uncertainty and its exact sources. Existing methodologies largely rely on the traditional dichotomy of aleatoric and epistemic uncertainty, which inadequately treats uncertainty as a static snapshot. To bridge this gap, this position paper challenges the static treatment of uncertainty. Supported by empirical evidence, we posit that aleatoric uncertainty in LLM agents is fundamentally dynamic and must be partitioned into strictly and temporarily irreducible states—differentiating intrinsic task variance, such as multiple valid answers, from resolvable noise, including input ambiguity, typographical errors, and contextual conflict. Through episodic empirical evaluations across multi-turn scenarios, we demonstrate how targeted agentic interventions can actively resolve temporarily irreducible noise, transitioning the interaction into an epistemic regime. When data uncertainty is strictly irreducible, model interventions cannot isolate a single correct distribution. While we acknowledge that the traditional static dichotomy remains a valuable framework for non-agentic settings, we urge the community to develop new decomposition paradigms that track uncertainty along the timeline of interaction. Embracing this dynamic perspective is essential to achieve robust uncertainty quantification for modern LLM agents.</abstract>
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%0 Conference Proceedings
%T Rethinking Aleatoric Uncertainty: Partitioning Irreducible States for Large Language Model Agents
%A Miyaji, Renato Okabayashi
%A Valdenegro-Toro, Matias
%Y Aziz, Wilker
%Y Berant, Jonathan
%Y Eikema, Bryan
%Y Ilia, Evgenia
%Y de Marneffe, Marie-Catherine
%Y Plank, Barbara
%Y Shelmanov, Artem
%Y Swayamdipta, Swabha
%Y Tiedemann, Jörg
%Y Vazhentsev, Artem
%Y Vázquez, Raúl
%Y Zerva, Chrysoula
%S The Proceedings of the Third Workshop on Uncertainty-Aware NLP
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-486-6
%F miyaji-valdenegro-toro-2026-rethinking
%X While Uncertainty Quantification (UQ) remains an active research field for Large Language Models (LLMs), the current literature lacks a rigorous understanding of what constitutes aleatoric uncertainty and its exact sources. Existing methodologies largely rely on the traditional dichotomy of aleatoric and epistemic uncertainty, which inadequately treats uncertainty as a static snapshot. To bridge this gap, this position paper challenges the static treatment of uncertainty. Supported by empirical evidence, we posit that aleatoric uncertainty in LLM agents is fundamentally dynamic and must be partitioned into strictly and temporarily irreducible states—differentiating intrinsic task variance, such as multiple valid answers, from resolvable noise, including input ambiguity, typographical errors, and contextual conflict. Through episodic empirical evaluations across multi-turn scenarios, we demonstrate how targeted agentic interventions can actively resolve temporarily irreducible noise, transitioning the interaction into an epistemic regime. When data uncertainty is strictly irreducible, model interventions cannot isolate a single correct distribution. While we acknowledge that the traditional static dichotomy remains a valuable framework for non-agentic settings, we urge the community to develop new decomposition paradigms that track uncertainty along the timeline of interaction. Embracing this dynamic perspective is essential to achieve robust uncertainty quantification for modern LLM agents.
%U https://aclanthology.org/2026.uncertainlp-main.10/
%P 138-161
Markdown (Informal)
[Rethinking Aleatoric Uncertainty: Partitioning Irreducible States for Large Language Model Agents](https://aclanthology.org/2026.uncertainlp-main.10/) (Miyaji & Valdenegro-Toro, UncertaiNLP 2026)
ACL